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Evidence for a Dirac Spin Liquid in the Kagome Heisenberg Antiferromagnet from High-Precision Neural-Network Quantum States

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Abstract

The spin-1/2 kagome Heisenberg antiferromagnet is a central model in the study of quantum spin liquids, yet its ground-state properties remain unresolved. We perform large-scale variational Monte-Carlo calculations using neural-network quantum states (NQSs) with kagome-geometry-aware message-passing convolution on two-dimensional tori containing up to 300 spins, and achieve exceptionally low variational energies for the kagome model. The spin and dimer structure factors show no evidence of magnetic or valence-bond long-range order. Moreover, the dominant symmetry-resolved spin and dimer correlations exhibit a common scaling behavior consistent with the emergent SO(6)SO(6) monopole multiplet of the Dirac spin liquid. Finally, the fidelity of the optimized NQSs with Gutzwiller-projected mean-field states increasingly favors the Dirac state over the competing Z2Z_{2} candidates as the system size grows. Together, these complementary results provide evidence for a U(1)U(1) Dirac spin-liquid ground state in the kagome Heisenberg antiferromagnet.

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Introduction

Introduction.—Quantum spin liquids (QSLs) represent a fundamental class of quantum phases beyond the Landau symmetry-breaking paradigm, exhibiting fractionalized excitations, long-ranged quantum entanglement, and emergent gauge fields [1, 2, 3, 4]. Understanding the nature of such phases of matter has become one of the central questions in condensed-matter physics. The long-standing experimental search for QSLs has placed kagome antiferromagnets at the forefront of two-dimensional quantum magnetism, with prominent candidate materials including herbertsmithite [5, 6, 7, 8], Zn-barlowite [9, 10, 11], and Y-based kagome antiferromagnets [12, 13, 14]. The spin-1/2 kagome Heisenberg model with nearest-neighbour interactions has become the minimal canonical model for kagome antiferromagnets, yet the nature of its ground state remains elusive. Early density-matrix renormalization-group (DMRG) calculations provided evidence for a Z2Z_2 gapped QSL [15, 16, 17]. However, subsequent calculations based on infinite projected entangled simplex states found scaling behaviour consistent with a gapless state [18], and flux-threaded infinite-cylinder DMRG revealed Dirac-cone signatures [19]. Alongside tensor-network simulations, variational Monte Carlo (VMC) studies based on Gutzwiller-projected fermionic mean-field ansätze initially identified the π\pi-flux U(1)U(1) Dirac QSL as a leading variational candidate [20, 21, 22, 23, 24], although further optimizations have also revealed closely competing Z2Z_2 states [25].

Despite the aforementioned numerical progress, both DMRG and VMC face intrinsic limitations. DMRG is largely restricted to quasi-one-dimensional cylinders, with a cost that grows rapidly with circumference, whereas VMC is constrained by the bias and limited expressivity of the underlying mean-field ansatz.

Neural-network quantum states (NQSs) provide a variational framework that can be directly applied to large two-dimensional lattices with periodic boundary conditions, thereby allowing studies on torus rather than quasi-one-dimensional cylinders. Moreover, NQSs can represent complex many-body correlations and sign structures without being constrained to a specific parton mean-field manifold [26, 27, 28]. Recent NQS-based studies have achieved ground-state energy estimates competitive with the best available results for J1J_1–J2J_2 square-lattice and triangular Heisenberg models [29, 30, 31, 32, 33], as well as two-dimensional Fermi-Hubbard models [34, 35, 36, 37].

The group convolutional neural-network (G-CNN) architecture of NQS has been applied to the kagome Heisenberg antiferromagnet, suggesting an evidence of spinon pair-density-wave (SPDW) ground state [38]. However, a subsequent analysis reveals that the unusually low variational energies reported in Ref. [38] are artifacts induced by nonergodic Monte Carlo sampling [39], and the signatures of SPDW require a further demonstration. These deficiencies leave the NQS study of the kagome Heisenberg ground state unresolved and demand a rigorous reassessment based on reliable sampling.

In this work, we study ground-state properties of spin-1/2 kagome Heisenberg antiferromagnet using deep neural quantum states whose architecture is tailored to the kagome geometry. Our variational ansatz extends the residual convolutional network architecture developed in Ref. [30, 31, 32], i.e., the ResNet, to the kagome model by replacing conventional grid-based kernels with message-passing convolutions that propagate features along the nearest-neighbour bonds while retaining an explicit representation of the three sublattices in each unit cell (see Fig. 1a-c). We benchmark that the ResNet with geometry-aware message-passing convolutions (KagomeResNet) outperforms its standard-convolution counterparts. To the best of our knowledge, the resulting variational energies for large system sizes on a torus achieve the lowest raw energies, apart from the disputed G-CNN results of Ref. [38]. More importantly, based on the analysis of static structure factors, correlation functions, and projections of the optimized NQS onto manifolds of Gutzwiller-projected mean-field states, we provide consistent evidence for a Dirac QSL ground state in the spin-1/2 kagome Heisenberg antiferromagnet.

Kagome-geometry-aware neural quantum state and variational energy optimization

Figure 1. The kagome-geometry-aware neural quantum state and variational energy optimization for L=6L = 6. a, Input encoding. A spin configuration s∈{±1}N\mathbf{s} \in\{ \pm1 \}^{N} on the kagome lattice, with N=3L2N = 3L^{2} sites (shown for L=4L = 4 as an example), is reshaped into a tensor of shape (1,3,L,L)(1, 3, L, L). The three sublattice sites AA, BB and CC within each unit cell form three L×LL \times L feature maps indexed along the primitive vectors a1\mathbf{a}_{1} and a2\mathbf{a}_{2} of the underlying triangular Bravais lattice. Periodic boundary conditions are implemented through cyclic shifts of the Bravais-lattice indices. b, Kagome message-passing convolution (KagomeConv). Each site receives messages from its four nearest neighbors and from itself, illustrated for an AA-sublattice site as an example. Each directed nearest-neighbor bond class carries an independent kernel We∈Rc×cW_{e} \in\mathbb{R}^{c \times c}, with cc being the number of feature channels. Here we plot W1,2,3,4W_{1,2,3,4} for an AA-site as an example, and there are 12 bond classes in total for a unit cell. Each sublattice α\alpha has its own on-site kernel SαS_{\alpha} and bias. c, Network architecture of KagomeResNet. The input is processed by nn residual blocks, each consisting of normalization (Norm), GELU activations, and two layers of KagomeConv connected by a residual shortcut. After a final normalization, the pair-complex activation combines real channels into complex outputs, which are summed to yield the wave-function amplitude ψ(s)\psi(\mathbf{s}). d, Variational-energy optimization for L=6L = 6 in the Γ\Gamma-point sector. After optimization with translational symmetry, the spin-inversion symmetry Is=+1\mathcal{I}_{s} = +1 and the D6D_{6} point-group symmetry are imposed at the arrow-marked iteration. The inset shows the Monte Carlo acceptance rate RaccR_{\mathrm{acc}} in the final 500 steps of the symmetry-projected stage. For comparison, we highlight the raw energies obtained by projected VMC based on U(1)U(1) Dirac QSL E≃−46.307E \simeq-46.307 [22, 23], DMRG E≃−46.613E \simeq-46.613 [16, 22], and conventional ResNet E≃−47.105E \simeq-47.105 (see the Supplementary Material) by dashed lines. e, Variational-energy optimization for L=6L = 6 in the odd spin-inversion sector Is=−1\mathcal{I}_{s} = -1 at the M3M_{3} and Γ\Gamma points. The corresponding little-group symmetries, C2vC_{2v} and D6D_{6}, are imposed at the iteration marked by the arrows. The dashed red line marks the optimized variational energy E≃−47.160E \simeq-47.160 in the Γ\Gamma-point sector with Is=+1\mathcal{I}_{s} = +1.

Model and variational wave function. The Hamiltonian of kagome Heisenberg antiferromagnet studied in this work is

H=∑⟨i,j⟩Si⋅Sj(1)\mathcal{H} = \sum_{\langle i,j \rangle} \mathbf{S}_{i} \cdot\mathbf{S}_{j} \tag*{(1)}

with Si=(Six,Siy,Siz)\mathbf{S}_{i} = (S_{i}^{x}, S_{i}^{y}, S_{i}^{z}) being the spin-1/2 operators, and ⟨i,j⟩\langle i,j \rangle indicating pairs of nearest-neighbor sites on a kagome lattice shown in Fig. 1a. Here, we consider the periodic boundary condition of the model, i.e., the torus, and the system sizes L=6L = 6, 8 and 10, with number of sites N=108N = 108, 192 and 300, respectively (N=L×L×3N = L \times L \times3).

In the NQS approach of training the ground state of H\mathcal{H}, a neural network provides a compact parametrization of the many-body wave function. For a spin-1/2 system with NN sites, the Hilbert space is spanned by the zz-basis spin configurations ∣s⟩=∣s1,…,sN⟩\lvert\mathbf{s} \rangle= \lvert s_{1}, \ldots, s_{N} \rangle with si=±1s_{i} = \pm1. The NQS parameterized by θ\theta can map each spin configuration s\mathbf{s} to a complex wave-function amplitude ψθ(s)\psi_{\theta}(\mathbf{s}). To provide a phase prior for the ground state of Hamiltonian (1), we incorporate a fixed reference phase corresponding to a coplanar q=0q = 0 three-sublattice pattern ϕq=0(s)\phi_{q=0}(\mathbf{s}) into the wave-function parametrization, thereby defining the variational state ∣Ψθ⟩=∑sψθ(s)ϕq=0(s)∣s⟩\lvert\Psi_{\theta} \rangle= \sum_{\mathbf{s}} \psi_{\theta}(\mathbf{s})\phi_{q=0}(\mathbf{s})\lvert\mathbf{s} \rangle. Within variational Monte Carlo, the parameters θ\theta are optimized using MinSR [31], to minimize the variational energy Eθ=⟨Ψθ∣H∣Ψθ⟩/⟨Ψθ∣Ψθ⟩E_{\theta} = \langle\Psi_{\theta} \lvert\mathcal{H} \rvert\Psi_{\theta} \rangle/ \langle\Psi_{\theta} \vert\Psi_{\theta} \rangle. We emphasize that the complex-valued network ψθ(s)\psi_{\theta}(\mathbf{s}) can learn additional phase corrections beyond the reference factor ϕq=0(s)\phi_{q=0}(\mathbf{s}). More details of the optimization are provided in the Supplemental Material.

The architecture of our NQS, the KagomeResNet, is plotted in Fig. 1a-c. First, the spin configuration sis_{i} is reshaped into three L×LL \times L matrices associated with the AA, BB, and CC sublattices of the kagome lattice. Then, as a key operation for our NQS, we perform message passing directly along the nearest-neighbor kagome connectivity. Specifically, in Fig. 1b, we show the operation for an AA-sublattice site as an example, i.e.,

yA,i=SAxA,i+W1xB,j1+W2xB,j2+W3xC,j3+W4xC,j4(2)y_{A,i}=S_Ax_{A,i}+W_1x_{B,j_1}+W_2x_{B,j_2}+W_3x_{C,j_3}+W_4x_{C,j_4} \tag*{(2)}

where xα,ix_{\alpha,i} and yα,iy_{\alpha,i} are the input and output feature vectors at site ii of sublattice α\alpha, respectively. The matrices SAS_A and W1,…,4W_{1,\ldots,4} represent trainable transformations for the central site and its four directed nearest-neighbor bonds. Similar operations are performed for the BB and CC sublattices, and thus there are 12 nearest-neighbor message-passing channels in a complete kagome convolutional (KagomeConv) layer. The full structure of KagomeResNet is shown in Fig. 1c. The ll-th residual block acts on xl∈R3×L×L×cx_l \in\mathbb{R}^{3 \times L \times L \times c}, where cc is the channel dimension, and maps it to xl+1x_{l+1} via two KagomeConv layers with GELU activations and a residual connection. We provide more details for the KagomeResNet and conventional ResNet in the Supplementary Material. The network contains nn residual blocks, and the output is finally converted by normalization and a pair-complex activation into the wave-function amplitude ψθ(s)\psi_\theta(s).

Benchmarks.—We now benchmark the performance of KagomeResNet for training the ground state of the Hamiltonian (1) with L=6L=6 in the Γ\Gamma-point sector. We first perform 10410^4 optimization steps imposing only translational symmetry, and subsequently for 1800 steps including the even spin-inversion sector Is=+1\mathcal{I}_s=+1 and D6D_6 point-group symmetry. In Fig. 1d, for the KagomeResNet with c=n=32c=n=32, we show the final 1000 iterations of the translationally symmetric optimization, followed by the full 1800-step optimization after the additional symmetry projections are imposed, and demonstrate that the converged value of the ground-state energy is E=−47.1597(6)E=-47.1597(6), which is highly competitive with the previously reported raw variational energies [16, 22, 23], and also lower than the conventional ResNet comparable number of variational parameters (see Supplemental Material).

Recent work using a G-CNN ansatz for the kagome model (1) suggests that non-ergodic Monte Carlo sampling can induce low energy during the optimization, and reports E≃−45.6E \simeq-45.6 for G-CNN with ergodic sampling [39]. In the inset of Fig. 1d, we show the acceptance rate Racc≃0.555R_{\mathrm{acc}} \simeq0.555 over the final 500 iterations with full symmetries, demonstrating that our Monte Carlo sampling is ergodic. The acceptance rate is monitored throughout all subsequent optimizations in this work to ensure that the Monte Carlo sampling remains ergodic.

We then benchmark the low-lying energies in the odd spin-inversion sector Is=−1\mathcal{I}_s=-1. For L=4L=4, the exact diagonalization study shows that the lowest-energy with Is=−1\mathcal{I}_s=-1 lies in the MM-point sector, while the lowest Γ\Gamma-point energy is nearly degenerate, only ∼6.8×10−3\sim6.8 \times10^{-3} higher in energy [40]. In Fig. 1e, we plot the final 1000 iterations of the translationally symmetric optimization with Is=−1\mathcal{I}_s=-1 for both MM and Γ\Gamma-point sectors, followed by 1800 additional iterations imposing their respective little-group symmetries D6D_6 and C2vC_{2v}. Our results show that, different from the L=4L=4 case, the lowest-energy state within the odd spin-inversion sector Is=−1\mathcal{I}_s=-1 occurs at the Γ\Gamma point for L=6L=6. More importantly, upon comparing both spin-inversion sectors, we find the overall ground state at the Γ\Gamma point with Is=+1\mathcal{I}_s=+1. This restores the same ground-state quantum numbers as found by exact diagonalization for L=4L=4,

in stark contrast to Ref. [38], which reported a change of the ground-state sector to the MM point with Is=−1\mathcal{I}_s=-1 for L=6L=6. We therefore find no evidence for the size-driven change of ground-state quantum numbers that underlies the SPDW interpretation of Ref. [38].

LL\multicolumn{2}{c}{c=n=32c=n=32}\multicolumn{2}{c}{c=n=40c=n=40}
eeσ2\sigma^2eeσ2\sigma^2
6−0.436664(6)-0.436664(6)0.00350(4)0.00350(4)−0.436760(5)-0.436760(5)0.00312(3)0.00312(3)
8−0.435835(4)-0.435835(4)0.00519(5)0.00519(5)−0.435949(5)-0.435949(5)0.00444(4)0.00444(4)
10−0.435239(4)-0.435239(4)0.00506(6)0.00506(6)−0.435472(5)-0.435472(5)0.00434(4)0.00434(4)

Table 1. Variational energy density ee and energy-variance density σ2\sigma^2 for KagomeResNet with different system and network sizes.

In Table I (PDF p. 19), we systematically represent the energy density e=E/Ne=E/N and the energy density variance σ2\sigma^2 obtained from KagomeResNet with n=c=32n=c=32 and n=c=40n=c=40 for the kagome model (1) with L=6L=6, 8 and 10 at the Γ\Gamma point with Is=+1\mathcal{I}_s=+1. For the L=8L=8 torus, previous references of the raw energy density include e=−0.4286749(4)e=-0.4286749(4) and −0.428783(1)-0.428783(1) for the projected mean-field VMC based on U(1)U(1) Dirac [22, 23] and Z2Z_2 [25] QSLs, respectively, and e∼−0.4349e \sim-0.4349 for the symmetric projected entangled-pair states calculations [41]. Our variational energies are substantially lower than the aforementioned benchmarks, improving the best known estimate for the energy density ee to the third decimal place.

LLμZ2∗\mu Z_2^*ρ\rhoη\etaϕ1\phi_1ϕ2\phi_2ϕ3\phi_3
60.48840.31260.34282.04122.82042.8421
80.55340.35770.39202.52102.94752.9692
100.57390.37880.41372.68513.00093.0246

Table I (PDF p. 19). Parameters of the Z2∗Z_2^* reference state, corresponding to the finite-size optimized Z2[0,π]βZ_2[0,\pi]\beta Ansatz of Ref. [6].

Ground-state correlation functions.—To study the ground-state properties of the Hamiltonian (1), for the trained NQS with n=c=40n=c=40, we first calculate the spin structure factors

Sfzz(q)=1N∑m,neiq⋅(rm−rn)⟨SmzSnz⟩(3)S_f^{zz}(\mathbf{q})=\frac{1}{N}\sum_{m,n}e^{i\mathbf{q}\cdot(\mathbf{r}_m-\mathbf{r}_n)}\langle S_m^z S_n^z\rangle \tag*{(3)}

with q=(qx,qy)\mathbf{q}=(q_x,q_y) the wave vector in the Cartesian reciprocal-space coordinates, and the dimer structure factors

SfD(q)=1N∑m,n,k,leiq⋅(rmk−rnl)Dmk,nl(4)S_f^D(\mathbf{q})=\frac{1}{N}\sum_{m,n,k,l}e^{i\mathbf{q}\cdot(\mathbf{r}_{m k}-\mathbf{r}_{n l})}D_{m k,n l} \tag*{(4)}

with the sum running over pairs of nearest-neighbor bonds, rmk=(rm+rk)/2\mathbf{r}_{m k}=(\mathbf{r}_m+\mathbf{r}_k)/2 being the midpoint of bond (m,k)(m,k), and Dmk,nl=⟨BmkBnl⟩−⟨Bmk⟩⟨Bnl⟩D_{m k,n l}=\langle B_{m k}B_{n l}\rangle-\langle B_{m k}\rangle\langle B_{n l}\rangle, where Bmk=Sm⋅SkB_{m k}=S_m\cdot S_k is the nearest-neighbor bond-energy operator. In Fig. 2a, we plot the spin structure factor Sfzz(q)S_f^{zz}(\mathbf{q}) in an extended Brillouin zone. We show that no sharp peak develops inside the first Brillouin zone, where the maxima occur at the reciprocal-lattice images GG of Γ\Gamma. Since GG differs from Γ\Gamma only by a reciprocal-lattice vector, the corresponding real-space correlations characterize a zero-Bravais-momentum three-sublattice fluctuation pattern repeated across unit cells, rather than a distinct finite-momentum modulation. The nontrivial structure resides within the unit cell, where the AA, BB, and CC sublattice components form predominantly the nonuniform Γ−E2\Gamma-E_2 sector through the kagome sublattice form factor (see Supplemental Material). As shown in Fig. 2b, the normalized peak values of spin structure factors Smax⁡zz/NS_{\max}^{zz}/N tend to 0 with N→∞N\to\infty, indicating the absence of long-range magnetic order. We also provide the data of SfD(q)S^{D}_{f}(\mathbf{q}) and its normalized maxima Smax⁡DS^{D}_{\max} in the Supplementary Material, showing that Smax⁡D/N→0S^{D}_{\max}/N\to0 with increasing NN, which rules out the valence-bond-solid (VBS) order. Additionally, the dramatically large value of Smax⁡D∼87.37S^{D}_{\max}\sim87.37 for L=6L=6 reported by Ref. [38] is not reproduced in our calculations. Instead, Smax⁡DS^{D}_{\max} systematically decreases with increasing LL. Thus, we argue that the signatures of an SPDW ground state reported for Hamiltonian (1) in Ref. [38] require further validation, in particular through explicit checks of the ergodicity of the Monte Carlo sampling.

Structure factors and symmetry-resolved correlations

Figure 2. Structure factors and symmetry-resolved correlations. a, Spin structure factor Sfzz(q)S_f^{\mathrm{zz}}(\mathbf{q}) for L=10L=10 over an extended Brillouin zone. The dashed hexagon marks the first Brillouin zone. The maxima lie at the reciprocal-lattice images G\mathbf{G} of Γ\Gamma probe the nonuniform Γ\Gamma-E2E_2 spin channel. b, Finite-size scaling of the normalized maxima Smax⁡zz/NS_{\max}^{\mathrm{zz}}/N and λ1[Kconn(M)]/N\lambda_1[K^{\mathrm{conn}}(M)]/N. Dashed lines are linear fits through the origin. c, Largest eigenvalue λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] of the connected bond-correlation matrix for L=10L=10. The strongest dimer fluctuations occur at the three symmetry-related MM points. d, Correlation between the symmetry-resolved MM-F2F_2 dimer correlation CF2D(r)C_{F_2}^{D}(\mathbf{r}) and the Γ\Gamma-E2E_2 spin correlation CΓ−E2zz(r)C_{\Gamma-E_2}^{\mathrm{zz}}(\mathbf{r}) for L=6,8,10L=6,8,10 and different separations rr. The dashed line denotes CF2D=ACΓ−E2zzC_{F_2}^{D}=A C_{\Gamma-E_2}^{\mathrm{zz}} with A≃2.52A\simeq2.52. The inset shows the symmetry-resolved MM-point dimer modes, with the F2F_2 sector dominant for all system sizes.

The dimer structure factors (4) consider a particular bond form factor fixed by the bond-center positions. To resolve the full bond-correlation structure, we then focus on the connected bond-correlation matrix

Kα,βconn(q)=1N∑R,R′eiq⋅(R−R′)⟨δBα(R)δBβ(R′)⟩,(5)K^{\mathrm{conn}}_{\alpha,\beta}(\mathbf{q})=\frac{1}{N}\sum_{\mathbf{R},\mathbf{R}'}e^{i\mathbf{q}\cdot(\mathbf{R}-\mathbf{R}')}\langle\delta B_{\alpha}(\mathbf{R})\delta B_{\beta}(\mathbf{R}')\rangle, \tag*{(5)}

where Bα(R)B_{\alpha}(\mathbf{R}) is the nearest-neighbor bond-energy operator, α=1,…,6\alpha=1,\ldots,6 labels the complete set of six nearest-neighbor bonds associated with each Bravais unit cell, and δBα(R)=Bα(R)−⟨Bα(R)⟩\delta B_{\alpha}(\mathbf{R})=B_{\alpha}(\mathbf{R})-\langle B_{\alpha}(\mathbf{R})\rangle. In Fig. 2c, we plot the largest eigenvalue of the matrix Kα,βconn(q)K^{\mathrm{conn}}_{\alpha,\beta}(\mathbf{q}), i.e., λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})], which quantifies the strongest connected dimer fluctuation at the momentum q\mathbf{q}. It is seen that λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] is maximal at the MM points. In real space, the dominant MM-point dimer fluctuations correspond to period-doubled bond modulations, with the bond pattern alternating under translations along one or both primitive Bravais-lattice directions. The three MM points represent the three symmetry-related orientations of this modulation. Condensation in the MM-point dimer channel implies broken lattice-translation symmetry, while λ1[Kconn(M)]/N→0\lambda_1[K^{\mathrm{conn}}(M)]/N\to0 with increasing NN precludes long-range VBS order (see Fig. 2b).

Next, we resolve the MM-point dimer fluctuations into symmetry-adapted sectors of the kagome space group, including F2F_2, B2B_2, and F1F_1 (see Supplemental Material for their explicit construction and symmetry projectors). As shown in the inset of Fig. 2d, the F2F_2 sector is dominant for all LL. Together with Fig. 2a, this identifies the MM-F2F_2 dimer and Γ\Gamma-E2E_2 spin channels as the dominant symmetry-resolved fluctuations. To further examine the relation between these two channels, we compare the real-space correlations in the aforementioned two dominant symmetry channels. We define the sublattice-resolved spin and bond correlations as

Cαβzz(r)=1N∑R⟨Sαz(R)Sβz(R+r)⟩,C_{\alpha\beta}^{\mathrm{zz}}(\mathbf{r})=\frac{1}{N}\sum_{\mathbf{R}}\langle S_{\alpha}^{z}(\mathbf{R})S_{\beta}^{z}(\mathbf{R}+\mathbf{r})\rangle,
CαβD(r)=1N∑R⟨δBα(R)δBβ(R+r)⟩,(6)C_{\alpha\beta}^{D}(\mathbf{r})=\frac{1}{N}\sum_{\mathbf{R}}\langle\delta B_{\alpha}(\mathbf{R})\delta B_{\beta}(\mathbf{R}+\mathbf{r})\rangle, \tag*{(6)}

where α,β=A,B,C\alpha,\beta=A,B,C for the spin correlations, while α,β=1,…,6\alpha,\beta=1,\ldots,6 label the nearest-neighbor bonds for the dimer correlations defined above. The Γ\Gamma-E2E_2 spin correlation is obtained by projecting the three-sublattice correlation matrix onto the E2E_2 sector, i.e., CΓ−E2zz(r)=12∑α,β=A,B,C(PE2)αβCαβzz(r)C_{\Gamma-E_2}^{\mathrm{zz}}(\mathbf{r})=\frac{1}{2}\sum_{\alpha,\beta=A,B,C}(P_{E_2})_{\alpha\beta}C_{\alpha\beta}^{\mathrm{zz}}(\mathbf{r}) with PE2=I−uu†P_{E_2}=I-\mathbf{u}\mathbf{u}^{\dagger} and u=13(1,1,1)T\mathbf{u}=\frac{1}{\sqrt{3}}(1,1,1)^{T}. Similarly, we define CF2D(r)=13∑i=13e−iMi⋅r[vF2(i)]†CD(r)vF2(i)C_{F_2}^{D}(\mathbf{r})=\frac{1}{3}\sum_{i=1}^{3}e^{-i\mathbf{M}_{i}\cdot\mathbf{r}}[\mathbf{v}_{F_2}^{(i)}]^{\dagger}C^{D}(\mathbf{r})\mathbf{v}_{F_2}^{(i)}, where the explicit F2F_2 bond vectors and their symmetry construction are given in the Supplemental Material.

In the Nf=4N_f=4 QED3_3 description of the U(1)U(1) Dirac QSL of the kagome Heisenberg antiferromagnet, the MM-F2F_2 dimer and Γ\Gamma-E2E_2 spin channels carry the quantum numbers of spin-singlet and spin-triplet minimal monopoles, respectively [42]. These monopoles are components of the same emergent SO(6)SO(6) multiplet and thus share a common scaling dimension [42, 43]. In Fig. 2d, we directly compare CF2D(r)C_{F_2}^{D}(r) and CΓ−E2zz(r)C_{\Gamma-E_2}^{\mathrm{zz}}(r) for different distances rr and system sizes LL. Remarkably, all data collapse onto a proportional function CF2D(r)=ACΓ−E2zz(r)C_{F_2}^{D}(r)=A C_{\Gamma-E_2}^{\mathrm{zz}}(r) with A≃2.52A\simeq2.52. This is precisely the behavior expected for different components of the same emergent SO(6)SO(6) monopole multiplet, showing that the symmetry-resolved correlations obtained from our NQS calculations are consistent with the emergent monopole structure of the kagome U(1)U(1) Dirac QSL.

Overlap of ground state with candidate QSLs.

Previous studies of projected mean-field VMC for the kagome model (1) cannot unambiguously distinguish between the between Z2Z_2 and the U(1) Dirac spin-liquid states because their variational energies are nearly degenerate [21, 25]. Our NQS is not biased toward either of the competing parton mean-field ansätze, providing an independent and more accurate representation of the ground-state wave function of the Hamiltonian (1). This motivates us to assess whether the optimized NQS is closer to the U(1) Dirac or Z2Z_2 spin-liquid manifold.

For this assessment, we use the following procedure. We determine the fidelity between our optimized NQS ∣Ψθopt⟩\lvert\Psi_\theta^{\mathrm{opt}}\rangle with representative Gutzwiller-projected fermionic states from these competing variational families ∣Φμ,ν⟩\lvert\Phi_{\mu,\nu}\rangle with μ\mu and ν\nu labeling the variational family and spinon boundary sector, respectively. The fidelity is given by

Fμ,ν=∣⟨Ψθopt∣Φμ,ν⟩∣2⟨Ψθopt∣Ψθopt⟩⟨Φμ,ν∣Φμ,ν⟩,(7)F_{\mu,\nu}=\frac{\left|\langle\Psi_\theta^{\mathrm{opt}}\mid\Phi_{\mu,\nu}\rangle\right|^2}{\langle\Psi_\theta^{\mathrm{opt}}\mid\Psi_\theta^{\mathrm{opt}}\rangle\langle\Phi_{\mu,\nu}\mid\Phi_{\mu,\nu}\rangle}, \tag*{(7)}

and can be evaluated by Monte Carlo sampling configurations from the NQS probability distribution. We adopt approximately 3×1053 \times10^5 Monte Carlo samples to obtain fidelity estimates with small statistical uncertainties. As the Dirac spin-liquid reference (μ=Dirac\mu= \mathrm{Dirac}), we employ the optimized extended U(1) Dirac state in Ref. [21], as one of the lowest projected-mean-field variational energies reported for the Dirac state. For Z2Z_2 candidates, we consider two complementary references. The first (μ=Z2\mu= Z_2) is obtained by introducing a finite perturbation away from the Dirac state considered in Ref. [21], without producing any detectable change in the variational energy. The second (μ=Z2∗\mu= Z_2^*) is the fully optimized Z2Z_2 resonating-valence-bond state in Ref. [25], whose variational energy lies slightly below that of their optimized U(1) state. Following the boundary conditions used in the original VMC studies, we restrict ν\nu to the two mixed periodic-antiperiodic spinon sectors. For the Dirac state, only nondegenerate closed-shell sectors are considered, however for the Z2Z_2 states, both mixed sectors are retained. Finally, we compare the maximum fidelity Fμmax⁡F_\mu^{\max} over all admissible boundary sectors for different μ\mu. More details of the parametrizations and boundary conditions for the chosen mean-field states are given in the Supplemental Material.

The results of Fμmax⁡F_\mu^{\max} for the aforementioned three Gutzwiller-projected mean-field states and the optimized KagomeResNet NQS with n=c=40n = c = 40 are displayed in Fig. 3. Although the variational energies for the Dirac and Z2Z_2 (μ=Dirac\mu= \mathrm{Dirac} and Z2Z_2) reference states in Ref. [21] are indistinguishable, FZ2max⁡F_{Z_2}^{\max} is significantly smaller than FDiracmax⁡F_{\mathrm{Dirac}}^{\max}. Moreover, for a more competitive Z2Z_2 reference (μ=Z2∗\mu= Z_2^*) in Ref. [25] with lower variational energy, FZ2∗max⁡F_{Z_2^*}^{\max} is close to FDiracmax⁡F_{\mathrm{Dirac}}^{\max} at L=6L = 6, while it becomes systematically smaller than FDiracmax⁡F_{\mathrm{Dirac}}^{\max} with increasing LL. The relative separation is highlighted by the ratio FDiracmax⁡/FZ2∗max⁡F_{\mathrm{Dirac}}^{\max}/F_{Z_2^*}^{\max}, which increases linearly with LL (see the inset of Fig. 3a).

We emphasize that although both the Z2Z_2 and Z2∗Z_2^* reference states originate from a gapped Z2Z_2 mean-field ansätze, the latter, after Gutzwiller projection, exhibits properties resembling those of a Dirac QSL, such as a gapless spin excitation spectrum [25]. The much larger fidelity FZ2∗max⁡≫FZ2max⁡F_{Z_2^*}^{\max} \gg F_{Z_2}^{\max} shows

Fidelity with Gutzwiller-projected spin-liquid reference states

Figure 3. Fidelity with Gutzwiller-projected spin-liquid reference states. a, The maximum fidelity Fμmax⁡F_\mu^{\max} between the optimized KagomeResNet NQS with μ=Dirac\mu= \mathrm{Dirac} and μ=Z2∗\mu= Z_2^* as a function of system size. The inset shows the ratio between the two fidelities FDiracmax⁡/FZ2∗max⁡F_{\mathrm{Dirac}}^{\max}/F_{Z_2^*}^{\max} as a function of LL. b, The maximum fidelity FZ2∗max⁡F_{Z_2^*}^{\max} with μ=Z2\mu= Z_2. Error bars indicate Monte Carlo statistical uncertainties. Dashed lines connecting data points are guides to the eye.

that the optimized NQS is substantially closer to this reference with Dirac-like spin properties. Combined with the direct fidelity preference for the Dirac reference at L=8L = 8 and 1010 (Fig. 3a), these results support a Dirac spin-liquid interpretation of the NQS ground state.

Discussion. — We have studied the ground-state properties of spin-1/2 kagome Heisenberg antiferromagnet with nearest-neighbor interactions using the KagomeResNet, an NQS architecture developed in this work, featuring message-passing convolutions tailored to the geometry of kagome lattice. In stark contrast to the results in Ref. [38], we show that the ground state lies in the Γ\Gamma instead of MM point and no evidence of SPDWs states can be identified. The analysis of symmetry-resolved spin and dimer correlations and the wave-function fidelity comparisons support the U(1) Dirac QSL nature for the ground state of the kagome model (1).

Future work can extend the application of KagomeResNet to explore the ground-state properties of other kagome systems, such as the long-range XYXY kagome model, which can be realized by dipolar Rydberg atoms [44, 45, 46]. In addition, we have demonstrated that the KagomeResNet outperforms the conventional ResNet, suggesting that the geometry-aware message-passing convolutions can play a key role in the NQS architecture. This can motivate the design of NQS for solving the ground states of models with more complex geometry, including the spin-1/2 Heisenberg antiferromagnet on the shuriken lattice [47, 48] and maple-leaf lattice [49].

Acknowledgments

The NQS simulations are performed using Quantax [50] based on JAX [51] and Equinox [52]. We gratefully acknowledge the scientific support and high-performance computing resources provided by the Erlangen National High Performance Computing Center (NHR) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (NHR Project No. nqsQuMat), and by the Jülich Booster (JUPITER Booster) at the Jülich Supercomputing Centre, Forschungszentrum Jülich (project neuquass). This work was supported by the German Research Foundation (DFG) via project 492547816 (TRR 360). C.R. acknowledges support from the Stewart Blusson Quantum Matter Institute at the University of British Columbia.

References

Supplementary Materials for “Evidence for a Dirac Spin Liquid in the Kagome Heisenberg Antiferromagnet from High-Precision Neural-Network Quantum States”

Zheng-Hang Sun; Christopher Roth; Ao Chen; Markus Heyl

supp.pdf

Supplementary Materials for “Evidence for a Dirac Spin Liquid in the Kagome Heisenberg Antiferromagnet from High-Precision Neural-Network Quantum States”

Details of network architecture and results for conventional residual convolutional network

In this section, we present the details of the conventional residual convolutional network ResNet, which is employed as a baseline for comparison with KagomeResNet considered in the main text.

As shown in Fig. 1a, the kagome lattice is regarded as a Bravais lattice with three sublattice sites AA, BB, and CC in each unit cell. For an L×LL \times L system, the total number of spins is N=3L2N = 3L^2, and a spin configuration can be written as s={sα(i,j)}\mathbf{s} = \{s_{\alpha}(i,j)\} with α=A,B,C\alpha= A, B, C, sα(i,j)=±1s_{\alpha}(i,j) = \pm1, and (i,j)(i,j) labelling the unit cell along the two primitive vectors a1\mathbf{a}_1 and a2\mathbf{a}_2. The conventional ResNet treats the three spins within each unit cell as a single local degree of freedom. Specifically, we first introduce the binary variables bα(i,j)=sα(i,j)+12∈{0,1}b_{\alpha}(i,j) = \frac{s_{\alpha}(i,j)+1}{2} \in\{0,1\} and encode the local spin configuration by the integer

k(i,j)=bA(i,j)+2bB(i,j)+4bC(i,j),(8)k(i,j) = b_A(i,j) + 2b_B(i,j) + 4b_C(i,j), \tag*{(8)}

with k(i,j)∈{0,…,7}k(i,j) \in\{0,\ldots,7\}. Therefore, the 23=82^3 = 8 possible spin configurations of a kagome unit cell are represented as eight local states.

A trainable embedding matrix E∈Rc×8E \in\mathbb{R}^{c \times8} then maps each local state to a cc-dimensional feature vector,

Xμ,i,j(0)=Eμ,k(i,j),μ=1,…,c.(9)X_{\mu,i,j}^{(0)} = E_{\mu,k(i,j)}, \qquad\mu= 1,\ldots,c. \tag*{(9)}

The resulting input to the convolutional network therefore has shape X(0)∈Rc×L×LX^{(0)} \in\mathbb{R}^{c \times L \times L}. After the embedding layer, all spatial transformations are performed by standard two-dimensional convolutions on the Bravais-cell grid, as illustrated in Fig. 1b. For a hidden feature tensor X∈Rc×L×LX \in\mathbb{R}^{c \times L \times L}, a convolutional layer can be written as

[C(X)]μ,i,j=bμ+∑ν=1c∑δ1,δ2=−11Wμν(δ1,δ2)Xν,i+δ1,j+δ2,(10)[C(X)]_{\mu,i,j} = b_{\mu} + \sum_{\nu=1}^{c}\sum_{\delta_1,\delta_2=-1}^{1} W_{\mu\nu}^{(\delta_1,\delta_2)}X_{\nu,i+\delta_1,j+\delta_2}, \tag*{(10)}

where

W(δ1,δ2)∈Rc×c(11)W^{(\delta_1,\delta_2)} \in\mathbb{R}^{c \times c} \tag*{(11)}

is the weight matrix associated with the cell displacement δ=(δ1,δ2)\boldsymbol{\delta} = (\delta_1,\delta_2). For periodic boundary conditions, the cell indices in Eq. (3) are understood modulo LL, corresponding to the circular padding used in the implementation. The 3×33 \times3 stencil contains nine Bravais-cell positions, including the central cell δ=(0,0)\boldsymbol{\delta} = (0,0). On the Bravais lattice, six of the eight surrounding cells, (±1,0)(\pm1,0), (0,±1)(0,\pm1), (1,−1)(1,-1), and (−1,1)(-1,1) are nearest-neighbour Bravais cells, whereas (1,1)(1,1) and (−1,−1)(-1,-1) are next-nearest-neighbour Bravais cells. Hence, the standard 3×33 \times3 convolution treats these cell-index displacements as independent learnable kernel components.

Figure Fig. 1c shows the entire ResNet architecture. Starting from the embedded tensor X0=X(0)X_0 = X^{(0)}, the network contains nn residual blocks. Each block consists of two standard convolutions with GELU nonlinearities and an identity shortcut. For the llth residual block (l=0,…,n−1l = 0,\ldots,n-1), the transformation implemented in our calculations is

X~l=Xll+1,(12)\widetilde{X}_l = \frac{X_l}{\sqrt{l+1}}, \tag*{(12)}
Yl=Conv⁡l(1)[GELU⁡(X~l)],(13)Y_l = \operatorname{Conv}^{(1)}_l\left[\operatorname{GELU}(\widetilde{X}_l)\right], \tag*{(13)}
Zl=Conv⁡l(2)[GELU⁡(Yl)],(14)Z_l = \operatorname{Conv}^{(2)}_l\left[\operatorname{GELU}(Y_l)\right], \tag*{(14)}
Xl+1=Xl+Zl.(15)X_{l+1} = X_l + Z_l. \tag*{(15)}
Conventional residual convolutional network applied directly to the kagome model

Figure 1 (PDF p. 9). Conventional residual convolutional network (ResNet) applied directly to the kagome model. a, Input embedding. The kagome lattice is represented as a triangular Bravais lattice with three sites AA, BB, and CC in each unit cell. For a spin configuration s∈{±1}N\mathbf{s} \in\{\pm1\}^{N}, the three spins (sA,sB,sC)∈{±1}3(s_A,s_B,s_C) \in\{\pm1\}^{3} within each unit cell (shaded) are encoded as one of 8 local states. A trainable embedding table maps each local state to a cc-dimensional feature vector, producing a feature tensor of shape (c,L,L)(c,L,L), where cc is the number of feature channels. The three-sublattice structure of the kagome lattice is therefore absorbed into the local embedding. b, Conventional 3×33 \times3 convolution. The subsequent convolutional layers operate on the Bravais-cell indices using a standard 3×33 \times3 stencil with periodic (circular) padding. Each of the nine offsets δ∈{−1,0,1}2\boldsymbol{\delta} \in\{-1,0,1\}^{2} is associated with a weight matrix Wδ∈Rc×cW_{\boldsymbol{\delta}} \in\mathbb{R}^{c \times c}, including the central weight W0,0W_{0,0}. On the Bravais lattice, the stencil covers the six nearest-neighbour cells and two additional next-nearest-neighbour cells at δ=±(1,1)\boldsymbol{\delta}=\pm(1,1). Thus, although the local three-spin configuration is retained through the embedding, the convolution itself acts on the Bravais-cell grid and does not explicitly encode the microscopic nearest-neighbour connectivity of the kagome lattice. c, Network architecture of ResNet. The embedded input is processed by nn residual blocks, each consisting of normalization (Norm), GELU activations, and two standard 3×33 \times3 convolution layers connected by a residual shortcut. After a final normalization, the pair-complex activation combines real channels into complex outputs, which are summed to yield the wave-function amplitude ψ(s)\psi(\mathbf{s}). d, Variational-energy optimization with ResNet for L=6L=6 in the Γ\Gamma-point sector. The energy trajectory of the conventional ResNet is shown over the final 1,800 optimization iterations, with both calculations imposing Is=+1\mathcal{I}_s=+1 and the D6D_6 point-group symmetry in comparison with the KagomeResNet studied in the main text.

Here, the “Norm” operation shown in Fig. 1c is therefore a deterministic rescaling by 1/l+11/\sqrt{l+1}. All residual blocks preserve both the number of feature channels cc and the spatial dimensions L×LL \times L. Both convolutions in each residual block contain trainable biases, except for the second convolution in the final residual block, for which the bias is omitted.

After the final residual block, the feature tensor is rescaled once more,

Xn⟶Xnn+1.(16)X_n \longrightarrow\frac{X_n}{\sqrt{n+1}}. \tag*{(16)}

The variational parameters of the convolutional network are real. When a complex-valued wave function is required, the cc real output channels are divided into two equal sets and combined pairwise according to

Zμ,i,j=Xμ,i,j+iXμ+c/2,i,j,μ=1,…,c/2,(17)Z_{\mu,i,j}=X_{\mu,i,j}+iX_{\mu+c/2,i,j}, \qquad\mu=1,\ldots,c/2, \tag*{(17)}

which corresponds to the “Pair complex” operation in Fig. 1c. We therefore choose an even number of channels cc whenever a complex output is used. The complex features are subsequently passed through the default exponential output activation,

Zμ,i,j→exp⁡(Zμ,i,j),(18)Z_{\mu,i,j}\rightarrow\exp(Z_{\mu,i,j}), \tag*{(18)}

which is evaluated in the code using a numerically stabilized scaled representation to avoid overflow. Finally, the output is aggregated over the feature channels and symmetrized with respect to lattice translations. For the default translationally invariant sector, the final layer implements the zero-momentum projection, yielding the variational wave-function amplitude ψ(s)\psi(\mathbf{s}).

We note that the KagomeResNet considered in the main text follows the same overall residual architecture as the conventional ResNet shown in Fig. 1c. The two differences lie in the input representation and the convolution operator. In the following, we provide more details of KagomeResNet.

The first difference from the conventional ResNet concerns the representation of the spin configuration. As discussed above, the conventional ResNet groups the three spins within each kagome unit cell into one of eight local states and subsequently maps this state to a cc-dimensional trainable embedding. In contrast, as shown in the Fig. 1a of the main text, KagomeResNet does not employ such an embedding layer. Instead, the microscopic kagome structure is retained explicitly throughout the network. For a kagome lattice with N=3L2N = 3L^2 spins, the input spin configuration s={sα(i,j)}\mathbf{s} = \{s_{\alpha}(i,j)\} with α=A,B,C\alpha= A,B,C, is directly reshaped according to

s∈R3L2⟶X(0)∈R1×3×L×L,(19)\mathbf{s} \in\mathbb{R}^{3L^2} \longrightarrow X^{(0)} \in\mathbb{R}^{1\times3\times L\times L}, \tag*{(19)}

with X1,α,i,j(0)=sα(i,j)X^{(0)}_{1,\alpha,i,j} = s_{\alpha}(i,j). Here, the first dimension denotes the feature-channel index, while the second dimension explicitly labels the three kagome sublattices AA, BB, and CC. The remaining two indices (i,j)(i,j) specify the Bravais unit cell. The reshape operation contains no trainable parameters and preserves the original spin variables sα(i,j)=±1s_{\alpha}(i,j) = \pm1 without coarse graining the three sites of a unit cell into a single local state. Consequently, whereas the conventional ResNet represents the kagome lattice by a tensor of shape (c,L,L)(c,L,L) after the embedding layer, KagomeResNet starts from a tensor of shape (1,3,L,L)(1,3,L,L), and retains the explicit sublattice dimension throughout all convolutional layers. The feature-channel dimension is expanded from 1 to cc only by the first kagome convolution.

The second, and more important, difference is the convolution operator. As shown in the Fig. 1b of the main text, instead of applying a standard 3×33 \times3 convolution on the Bravais-cell grid, KagomeResNet employs a geometry-aware convolution that performs message passing directly along the microscopic nearest-neighbour bonds of the kagome lattice. For an input tensor X∈Rcin×3×L×LX \in\mathbb{R}^{c_{\mathrm{in}}\times3\times L\times L}, the kagome convolution produces Y∈Rcout×3×L×LY \in\mathbb{R}^{c_{\mathrm{out}}\times3\times L\times L}, while preserving the explicit AA, BB, and CC sublattice axis. The nearest-neighbour connectivity is encoded by a fixed set of directed bond classes. Using the sublattice labels (A,B,C)≡(0,1,2)(A,B,C) \equiv(0,1,2) and the Bravais-cell displacement δ=(δ1,δ2)\boldsymbol{\delta} = (\delta_1,\delta_2), the six undirected nearest-neighbour bond classes used in the implementation are

(A,B,−1,0),(A,B,0,0),(A,C,0,−1),(A,C,0,0),(B,C,0,0),(B,C,1,−1).(20)\begin{aligned} &(A,B,-1,0), \qquad(A,B,0,0),\\ &(A,C,0,-1), \qquad(A,C,0,0),\\ &(B,C,0,0), \qquad(B,C,1,-1). \tag*{(20)} \end{aligned}

Each tuple (α,β,δ1,δ2)(\alpha,\beta,\delta_1,\delta_2) denotes a bond connecting sublattice α\alpha in one unit cell to sublattice β\beta in a unit cell displaced by δ=δ1a1+δ2a2\boldsymbol{\delta} = \delta_1\mathbf{a}_1 + \delta_2\mathbf{a}_2. Both directions of every bond are included in the message-passing operation, resulting in twelve directed nearest-neighbour channels per unit cell. For every directed bond e=(α→β,δ)e = (\alpha\to\beta,\boldsymbol{\delta}), KagomeResNet associates a learnable matrix

We∈Rcout×cin,(21)W_e \in\mathbb{R}^{c_{\mathrm{out}}\times c_{\mathrm{in}}}, \tag*{(21)}

which transforms the feature vector on the source site before it is accumulated on the target site. For a directed bond class e=(α,β,δ)e = (\alpha,\beta,\boldsymbol{\delta}), with δ=δ1a1+δ2a2\boldsymbol{\delta} = \delta_1\mathbf{a}_1 + \delta_2\mathbf{a}_2, the contribution from sublattice α\alpha in unit cell r\mathbf{r} to sublattice β\beta in unit cell r+δ\mathbf{r}+\boldsymbol{\delta} is defined as

ΔYμ,β(e)(r+δ)=∑ν=1cin(We)μνXν,α(r).(22)\Delta Y^{(e)}_{\mu,\beta}(\mathbf{r}+\boldsymbol{\delta}) = \sum_{\nu=1}^{c_{\mathrm{in}}}(W_e)_{\mu\nu}X_{\nu,\alpha}(\mathbf{r}). \tag*{(22)}

where We∈Rcout×cinW_e \in\mathbb{R}^{c_{\mathrm{out}}\times c_{\mathrm{in}}} is the trainable weight matrix associated with the directed bond class ee. The output feature on each target site is obtained by summing the contributions from all directed nearest-neighbour bond classes terminating at that site, together with the corresponding on-site contribution. The same matrix WeW_e is shared over all lattice translations of a given directed bond class, thereby preserving translational equivariance while explicitly encoding the microscopic connectivity of the kagome lattice.

In Fig. 1d, we directly compare the energy trajectory of the final 1,800 optimization iterations after imposing Is=+1\mathcal{I}_s = +1 and the D6D_6 point-group symmetry for both the conventional ResNet and KagomeResNet. Here, we consider the conventional ResNet with n=32n = 32, c=42c = 42 and 3×33 \times3 kernel, whose number of trainable parameters is Npara=1019046N_{\mathrm{para}} = 1019046, which is slightly larger than Npara=974208N_{\mathrm{para}} = 974208 for the KagomeResNet with n=c=32n = c = 32 considered in our main text. It is seen that KagomeResNet outperforms conventional ResNet.

Details of variational optimization

For both the conventional ResNet and the KagomeResNet, we supplement the complex network output ψθ(s)\psi_{\theta}(\mathbf{s}) with a fixed three-sublattice phase factor associated with a coplanar pattern,

ϕq=0(s)=exp⁡[i∑j=1N1−sj2φα(j)],(φA,φB,φC)=(0,2π3,4π3),(23)\phi_{q=0}(\mathbf{s}) = \exp\left[i\sum_{j=1}^{N}\frac{1-s_j}{2}\varphi_{\alpha(j)}\right], \qquad(\varphi_A,\varphi_B,\varphi_C) = \left(0,\frac{2\pi}{3},\frac{4\pi}{3}\right), \tag*{(23)}
Spin and dimer structure factors in reciprocal-lattice coordinates

Figure 2 (PDF p. 11). Spin and dimer structure factors in reciprocal-lattice coordinates. a-c, Static spin structure factor Sfzz(q)S_f^{zz}(\mathbf{q}) for L=6L = 6, 8, and 10, respectively. d-f, Dimer structure factor SfD(q)S_f^D(\mathbf{q}) for L=6L = 6, 8, and 10, respectively. The momentum is expressed in reciprocal-lattice coordinates (q1,q2)(q_1,q_2).

where sj=±1s_j = \pm1 and α(j)∈{A,B,C}\alpha(j) \in\{A, B, C\} labels the sublattice of site jj. Each down spin contributes the phase associated with its sublattice. This factor has unit modulus and is held fixed throughout training. The complex-valued network ψθ(s)\psi_\theta(s) can learn additional configuration-dependent phase corrections. Thus, the reference phase provides a prior without fixing the full phase structure of the variational state.

We initially optimize the neural-network quantum state (NQS) with translational invariance imposed. After 10410^4 optimization steps, we additionally impose spin-flip symmetry and the spatial symmetries appropriate to the target symmetry sector, and continue training from the previously optimized network parameters. For the optimization method, we adopt AdamSR in Quantax, which combines MinSR [1] with Adam-type momentum and adaptive rescaling. We adopt the training rate is 10−310^{-3}.

Additional data for the spin and dimer structure factors

In the main text, we present the spin structure factor for L=10L = 10 as a function of the wave vector q=(qx,qy)\mathbf{q} = (q_x,q_y) expressed in Cartesian reciprocal-space coordinates (see Fig. 2a of the main text). For a direct comparison with Ref. [2], we additionally present the structure factors using the reciprocal-lattice coordinates (q1,q2)(q_1,q_2) employed therein. We emphasize that q1q_1 and q2q_2 are not Cartesian components of the momentum, but dimensionless coefficients with respect to the two primitive reciprocal-lattice vectors.

Using the real-space primitive vectors a1=(1,0)\mathbf{a}_1 = (1,0), a2=(12,32)\mathbf{a}_2 = \left(\frac{1}{2},\frac{\sqrt{3}}{2}\right), the corresponding reciprocal-lattice vectors are b1=2π(1,−13)\mathbf{b}_1 = 2\pi\left(1,-\frac{1}{\sqrt{3}}\right), b2=2π(0,23)\mathbf{b}_2 = 2\pi\left(0,\frac{2}{\sqrt{3}}\right), which satisfy ai⋅bj=2πδij\mathbf{a}_i \cdot\mathbf{b}_j = 2\pi\delta_{ij}. The two momentum representations are therefore related by q=q1b1+q2b2\mathbf{q} = q_1\mathbf{b}_1 + q_2\mathbf{b}_2. For an L×LL \times L torus, the momenta in the first reciprocal unit cell are quantized as

q1=n1L,q2=n2L,n1,n2=0,…,L−1.(24)q_1 = \frac{n_1}{L}, \qquad q_2 = \frac{n_2}{L}, \qquad n_1,n_2 = 0,\ldots,L-1. \tag*{(24)}

Momentum points in an extended-zone representation are obtained by reciprocal lattice translations,

(q1,q2)→(q1+m1,q2+m2),m1,m2∈Z,(25)(q_1,q_2) \to(q_1 + m_1,q_2 + m_2), \qquad m_1,m_2 \in\mathbb{Z}, \tag*{(25)}
Additional data of structure factors and largest eigenvalues of bond-correlation matrix

Figure 3 (PDF p. 12). Additional data of structure factors and largest eigenvalues of bond-correlation matrix. a-b, Static spin structure factor Sfzz(q)S_f^{zz}(\mathbf{q}) in Cartesian reciprocal-space coordinates q=(qx,qy)\mathbf{q}=(q_x,q_y) for L=6L=6 and 88, respectively. c-d, Largest eigenvalue of the connected bond-correction matrix λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] in the coordinates q=(qx,qy)\mathbf{q}=(q_x,q_y) for L=6L=6 and 88, respectively.

which corresponds in Cartesian coordinates to q→q+m1b1+m2b2\mathbf{q}\to\mathbf{q}+m_1\mathbf{b}_1+m_2\mathbf{b}_2.

Figure 2 shows the corresponding spin and dimer structure factors for L=6L=6, 88, and 1010 in the (q1,q2)(q_1,q_2) representation. For L=6L=6, Ref. [2] reported a dramatic enhancement of SfD(q)S_f^D(\mathbf{q}) relative to the L=4L=4 result. In particular, while the maximum value for L=4L=4 was approximately 0.933, the reported maximum increased to Smax⁡D≃87.37S_{\max}^D\simeq87.37 for N=108N=108, accompanied by numerous pronounced peaks throughout the extended reciprocal space. This strong enhancement was interpreted in Ref. [2] as a signature of the proposed spinon pair-density-wave state and its associated secondary orders. We do not observe such behavior. Already for the same L=6L=6 (N=108N=108) torus (Fig. 2d), the dimer structure factor remains of order unity and exhibits no anomalously enhanced peaks. Upon increasing the system size to L=8L=8 and L=10L=10 (Figs. 2e and f), no growth analogous to that reported in Ref. [2] develops. Instead, the maximum value Smax⁡DS_{\max}^D decreases with increasing LL, while Smax⁡D/NS_{\max}^D/N extrapolates toward zero, as shown in Fig. 2b of the main text. The absence of any size-enhanced dimer peak is therefore inconsistent with the anomalously large dimer response reported in Ref. [2] and provides no support for the associated SPDW-induced dimer ordering tendency.

In the Fig. 2 of the main text, we present the data of spin structure factors Sfzz(q)S_f^{zz}(\mathbf{q}) and largest eigenvalue of the connected bond-correction matrix λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] in Cartesian reciprocal-space coordinates q=(qx,qy)\mathbf{q}=(q_x,q_y) with L=10L=10. Here, in Fig. 3, we also display the results of Sfzz(q)S_f^{zz}(\mathbf{q}) and λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] (q=(qx,qy)\mathbf{q}=(q_x,q_y)) with other two system sizes, i.e., L=6L=6 and 88 For all three system sizes, the maxima of Sfzz(q)S_f^{zz}(\mathbf{q}) occur at the reciprocal-lattice images GG of the Γ\Gamma point, whereas the maxima of λ1[Kconn(q)]\lambda_1[K^{\mathrm{conn}}(\mathbf{q})] are located at the MM points. The locations of these dominant spin and dimer fluctuations are therefore robust against changes in system size.

Symmetry-resolved spin and dimer correlations

In this section, we provide the symmetry construction underlying the Γ\Gamma-E2E_2 spin and MM-F2F_2 dimer correlations discussed in Fig. 2 of the main text. The purpose of this analysis is to go beyond the momentum dependence of the scalar structure factors and resolve the internal sublattice or bond structure of the dominant fluctuations. As we show below, the spin maxima at reciprocal-lattice images of Γ\Gamma predominantly probe the nonuniform E2E_2 sublattice sector, whereas the dominant dimer fluctuations at the three MM points belong to the F2F_2 bond sector. These are precisely the symmetry channels that can be compared with the monopole operator content of the Nf=4N_f=4 QED3_3 description of the kagome Dirac spin liquid (DSL).

-E2 spin channel

We first consider the spin correlations. For sublattices α,β=A,B,C\alpha,\beta=A,B,C, we define the 3×33\times3 real-space correlation matrix

Cαβzz(r)=1N∑R⟨Sαz(R)Sβz(R+r)⟩(26)C_{\alpha\beta}^{zz}(\mathbf{r})=\frac{1}{N}\sum_{\mathbf{R}}\langle S_\alpha^z(\mathbf{R})S_\beta^z(\mathbf{R}+\mathbf{r})\rangle \tag*{(26)}

where R\mathbf{R} labels the Bravais cells and N=3L2N=3L^2. Correspondingly, the sublattice-resolved momentum-space correlation matrix is obtained by Fourier transforming Cαβzz(r)C_{\alpha\beta}^{zz}(\mathbf{r}).

The physical spin structure factor defined in the main text contains, in addition, the kagome sublattice form factor. Denoting the position of sublattice α\alpha inside the unit cell by ρα\boldsymbol{\rho}_\alpha, it can be written as

Sfzz(q)=∑α,βeiq⋅(ρα−ρβ)Sαβzz(q),(27)S_f^{zz}(\mathbf{q})=\sum_{\alpha,\beta}e^{i\mathbf{q}\cdot(\boldsymbol{\rho}_\alpha-\boldsymbol{\rho}_\beta)}S_{\alpha\beta}^{zz}(\mathbf{q}), \tag*{(27)}

where q‾\overline{\mathbf{q}} denotes the momentum folded back into the first Brillouin zone. This expression also explains why the physical structure factor of a non-Bravais lattice is not generally periodic under q→q+G\mathbf{q} \to\mathbf{q}+\mathbf{G}: although q‾\overline{\mathbf{q}} is unchanged, the sublattice form factors eiG⋅ραe^{i\mathbf{G}\cdot\boldsymbol{\rho}_{\alpha}} need not be uniform.

At the Γ\Gamma point, the three-dimensional sublattice space decomposes into the uniform one-dimensional component and its two-dimensional orthogonal complement. The normalized uniform vector is

u=13(111),(28)\mathbf{u}=\frac{1}{\sqrt{3}}\begin{pmatrix}1\\1\\1\end{pmatrix}, \tag*{(28)}

with projector

PA1=uu†.(29)P_{A_1}=\mathbf{u}\mathbf{u}^{\dagger}. \tag*{(29)}

The complementary two-dimensional subspace transforms as the E2E_2 representation and is selected by

PE2=I−PA1=I−uu†.(30)P_{E_2}=I-P_{A_1}=I-\mathbf{u}\mathbf{u}^{\dagger}. \tag*{(30)}

For example, an orthonormal basis of this subspace can be chosen as

e1=12(1−10),e2=16(11−2),(31)\mathbf{e}_1=\frac{1}{\sqrt{2}}\begin{pmatrix}1\\-1\\0\end{pmatrix},\qquad \mathbf{e}_2=\frac{1}{\sqrt{6}}\begin{pmatrix}1\\1\\-2\end{pmatrix}, \tag*{(31)}

such that PE2=e1e1†+e2e2†P_{E_2}=\mathbf{e}_1\mathbf{e}_1^{\dagger}+\mathbf{e}_2\mathbf{e}_2^{\dagger}.

We therefore define the symmetry-resolved Γ\Gamma-E2E_2 correlation used in the main text as

CΓ−E2zz(r)=12∑α,β=A,B,C(PE2)αβCαβzz(r).(32)C_{\Gamma-E_2}^{zz}(\mathbf{r})=\frac{1}{2}\sum_{\alpha,\beta=A,B,C}(P_{E_2})_{\alpha\beta}C_{\alpha\beta}^{zz}(\mathbf{r}). \tag*{(32)}

The factor 1/21/2 averages over the two components of the E2E_2 representation. In contrast, contraction with PA1P_{A_1} gives the uniform Γ\Gamma-point spin channel. Equation (27) provides the connection to Fig. 2a of the main text. At a reciprocal-lattice image q=G\mathbf{q}=\mathbf{G} of Γ\Gamma, the Bravais momentum is again Γ\Gamma, but the vector of sublattice form factors

f(G)=(eiG⋅ρA,eiG⋅ρB,eiG⋅ρC)T(33)\mathbf{f}(\mathbf{G})=\left(e^{i\mathbf{G}\cdot\boldsymbol{\rho}_A},e^{i\mathbf{G}\cdot\boldsymbol{\rho}_B},e^{i\mathbf{G}\cdot\boldsymbol{\rho}_C}\right)^{T} \tag*{(33)}

contains a nonuniform component in the E2E_2 subspace. Consequently, the pronounced G\mathbf{G}-point maxima of Sfzz(q)S_f^{zz}(\mathbf{q}) probe predominantly the nonuniform Γ\Gamma-E2E_2 fluctuations rather than a distinct finite-momentum magnetic mode. The persistence of the same momentum and sublattice structure for L=6L=6, 88, and 1010, shown in Figs. 2 and 3 of the Supplemental Material and Fig. 2 of the main text, identifies Γ\Gamma-E2E_2 as the dominant symmetry-resolved spin channel.

This identification also has a direct field-theory interpretation. For the kagome U(1)U(1) DSL, Ref. [3] shows that the spin-triplet minimal monopoles carry zero crystal momentum and angular momentum l=2l=2 under the sixfold lattice rotation. On the kagome lattice these quantum numbers correspond precisely to the Γ\Gamma-E2E_2 sector. Thus, CΓ−E2zzC_{\Gamma-E_2}^{zz} provides a microscopic lattice observable sensitive to the spin-triplet monopole channel of the DSL.

M-point bond space and symmetry projectors

We then resolve the dimer correlations. Since a kagome unit cell contains six nearest-neighbour bonds, retaining the complete internal bond structure requires a six-dimensional bond basis. We use the same bond convention in the measurements and throughout the analysis:

B1(R)=SA(R)⋅SB(R),B2(R)=SA(R)⋅SB(R−a1),B3(R)=SA(R)⋅SC(R),B4(R)=SA(R)⋅SC(R−a2),B5(R)=SB(R)⋅SC(R),B6(R)=SB(R)⋅SC(R+a1−a2).(34)\begin{aligned} B_1(\mathbf{R})&=\mathbf{S}_A(\mathbf{R})\cdot\mathbf{S}_B(\mathbf{R}),\\ B_2(\mathbf{R})&=\mathbf{S}_A(\mathbf{R})\cdot\mathbf{S}_B(\mathbf{R}-\mathbf{a}_1),\\ B_3(\mathbf{R})&=\mathbf{S}_A(\mathbf{R})\cdot\mathbf{S}_C(\mathbf{R}),\\ B_4(\mathbf{R})&=\mathbf{S}_A(\mathbf{R})\cdot\mathbf{S}_C(\mathbf{R}-\mathbf{a}_2),\\ B_5(\mathbf{R})&=\mathbf{S}_B(\mathbf{R})\cdot\mathbf{S}_C(\mathbf{R}),\\ B_6(\mathbf{R})&=\mathbf{S}_B(\mathbf{R})\cdot\mathbf{S}_C(\mathbf{R}+\mathbf{a}_1-\mathbf{a}_2). \tag*{(34)} \end{aligned}

We denote δBα(R)=Bα(R)−⟨Bα⟩\delta B_{\alpha}(\mathbf{R}) = B_{\alpha}(\mathbf{R}) - \langle B_{\alpha}\rangle and collect these operators into the vector δB(R)=(δB1,…,δB6)T\delta\mathbf{B}(\mathbf{R}) = (\delta B_1,\ldots,\delta B_6)^{\mathsf{T}}. The connected real-space bond-correlation matrix is

CαβD(r)=1N∑R⟨δBα(R)δBβ(R+r)⟩,(35)C_{\alpha\beta}^{D}(\mathbf{r}) = \frac{1}{N}\sum_{\mathbf{R}}\langle\delta B_{\alpha}(\mathbf{R})\delta B_{\beta}(\mathbf{R}+\mathbf{r})\rangle, \tag*{(35)}

whose Fourier transform gives the connected matrix Kαβconn(q)K_{\alpha\beta}^{\mathrm{conn}}(\mathbf{q}) defined in the main text.

The strongest eigenvalue of Kconn(q)K^{\mathrm{conn}}(\mathbf{q}) occurs at the three symmetry-related MM points. In the reduced reciprocal-lattice coordinates introduced in Sec. II, we choose M1=(12,0)M_1 = (\frac{1}{2},0), M2=(0,12)M_2 = (0,\frac{1}{2}), and M3=(12,12)M_3 = (\frac{1}{2},\frac{1}{2}). The three momenta form the MM star under the D6D_6 point group.

At an individual MiM_i, the little co-group is C2vC_{2v}. We take its generators to be a twofold rotation C2C_2 about a hexagon center and a reflection τi\tau_i whose axis leaves MiM_i invariant. A lattice symmetry acts not only by permuting the six bonds but, whenever a bond is mapped to a neighbouring reference unit cell, also generates the corresponding Bloch phase at MiM_i. We denote the resulting six-dimensional bond-space representation by DMi(g)D_{M_i}(g).

For a one-dimensional irreducible representation λ\lambda of the little group, the symmetry projector is

Pλ(i)=1∣C2v∣∑g∈C2vχλ∗(g)DMi(g),(36)P_{\lambda}^{(i)} = \frac{1}{|C_{2v}|}\sum_{g\in C_{2v}}\chi_{\lambda}^{*}(g)D_{M_i}(g), \tag*{(36)}

where χλ(g)\chi_{\lambda}(g) is the corresponding character. Equivalently, labeling a one-dimensional sector by its eigenvalues η2=±1\eta_2=\pm1 and ητ=±1\eta_{\tau}=\pm1 under C2C_2 and τi\tau_i, respectively, Eq. (36) becomes

Pη2,ητ(i)=14[I+η2DMi(C2)+ητDMi(τi)+η2ητDMi(C2τi)].(37)P_{\eta_2,\eta_{\tau}}^{(i)} = \frac{1}{4}\left[I+\eta_2D_{M_i}(C_2)+\eta_{\tau}D_{M_i}(\tau_i)+\eta_2\eta_{\tau}D_{M_i}(C_2\tau_i)\right]. \tag*{(37)}

This construction includes the momentum-dependent phases and therefore classifies the full microscopic bond patterns, rather than only their orientations inside one unit cell.

For our six-bond basis, the representation at a representative MM point decomposes as

BM=2A1⊕A2⊕B1⊕2B2.(38)\mathcal{B}_{M}=2A_1\oplus A_2\oplus B_1\oplus2B_2. \tag*{(38)}

In the MM-star notation used in the main text, the A1A_1 and A2A_2 sectors generate the F1F_1 and F2F_2 bond modes, respectively. The leading eigenmode of Kconn(Mi)K^{\mathrm{conn}}(M_i) belongs to the one-dimensional, mirror-odd A2A_2 sector, i.e. the F2F_2 sector. The next modes are classified analogously by applying the projectors in Eq. (37); the dominant sectors displayed in the inset of Fig. 2d of the main text are F2F_2, B2B_2, and F1F_1.

Explicit F2F_2 bond vectors

With the bond ordering in Eq. (34), a normalized choice of the F2F_2 vectors at the three members of the MM star is

vF2(1)=12(−110011),M1=(12,0),\mathbf{v}_{F_2}^{(1)}=\frac{1}{2} \begin{pmatrix} -1\\ 1\\ 0\\ 0\\ 1\\ 1 \end{pmatrix}, \qquad M_1=\left(\frac{1}{2},0\right),
vF2(2)=12(001−1−11),M2=(0,12),\mathbf{v}_{F_2}^{(2)}=\frac{1}{2} \begin{pmatrix} 0\\ 0\\ 1\\ -1\\ -1\\ 1 \end{pmatrix}, \qquad M_2=\left(0,\frac{1}{2}\right),
vF2(3)=12(−1−11100),M3=(12,12).(39)\mathbf{v}_{F_2}^{(3)}=\frac{1}{2} \begin{pmatrix} -1\\ -1\\ 1\\ 1\\ 0\\ 0 \end{pmatrix}, \qquad M_3=\left(\frac{1}{2},\frac{1}{2}\right). \tag*{(39)}

The three vectors are related by the C6C_6 lattice rotations that permute the members of the MM star, including the Bloch phases associated with bonds crossing the unit-cell boundary. Their overall signs are conventional and have no physical consequence, since all projected correlations involve the rank-one projectors

PF2(i)=vF2(i)[vF2(i)]†.(40)P_{F_2}^{(i)} = v_{F_2}^{(i)} [v_{F_2}^{(i)}]^\dagger. \tag*{(40)}

The F2F_2 contribution to the connected bond covariance at a given MiM_i is therefore

λF2(Mi)=[vF2(i)]†Kconn(Mi)vF2(i).(41)\lambda_{F_2}(M_i) = [v_{F_2}^{(i)}]^\dagger K^{\mathrm{conn}}(M_i) v_{F_2}^{(i)}. \tag*{(41)}

Numerically, the leading eigenvector of Kconn(Mi)K^{\mathrm{conn}}(M_i) has essentially unit overlap with this symmetry sector for all three system sizes. The ordering of the leading symmetry-resolved eigenvalues, F2>B2>F1F_2 > B_2 > F_1, also remains unchanged from L=6L = 6 to L=10L = 10. Thus, the identification of the dominant MM-point fluctuation with F2F_2 is not a consequence of a particular system size or of an unconstrained numerical eigenvector, but follows from a fixed symmetry projector.

To compare this finite-momentum dimer fluctuation directly with the Γ\Gamma-point spin channel, we remove the oscillating MM-point phase and average over the three members of the MM star. This gives the real-space correlation used in the main text,

CF2D(r)=13∑i=13e−iMi⋅r[vF2(i)]†CD(r)vF2(i).(42)C_{F_2}^{D}(\mathbf{r}) = \frac{1}{3} \sum_{i=1}^{3} e^{-i\mathbf{M}_i \cdot\mathbf{r}} [v_{F_2}^{(i)}]^\dagger C^{D}(\mathbf{r}) v_{F_2}^{(i)}. \tag*{(42)}

The symmetry analysis above identifies the microscopic structure underlying Fig. 2c,d of the main text. The dominant bond fluctuations are centered at the MM star and exhibit an internal six-bond pattern that lies overwhelmingly in the mirror-odd F2F_2 sector.

Connection to the QED3 monopole multiplet

Next, we discuss why the two symmetry channels singled out above are particularly relevant for the Dirac-spin-liquid interpretation. The low-energy theory of the kagome U(1)U(1) DSL is Nf=4N_f = 4 QED3_3. The six lowest charge-one monopole operators transform in the antisymmetric representation 66 of SU(4), or equivalently as a vector of the emergent SO(6) symmetry. They decompose into three spin-singlet and three spin-triplet components. Their lattice quantum numbers on the kagome lattice were clarified in Ref. [3]. In particular, the spin-triplet monopoles have zero crystal momentum and angular momentum l=2l = 2 under C6C_6, identifying them with the Γ\Gamma-E2E_2 spin sector considered above.

The spin-singlet monopoles instead carry the translation quantum numbers of the three MM points and correspond to VBS fluctuations. Matching the translation and little-group transformation laws in the Table 3 of Ref. [3] to the bond-space representation constructed above identifies the mirror-odd component with the MM-F2F_2 sector. This observation is useful because the symmetry resolution separates the physical dimer response into operators with different continuum quantum numbers, which are mixed together in the scalar dimer structure factor SfD(q)S_f^D(q).

A central consequence of the emergent SO(6) structure is that all six components of the minimal-monopole multiplet have the same scaling dimension ΔΦ\Delta_\Phi. In the continuum theory their leading two-point function takes the form

⟨Φa†(x)Φb(0)⟩∝δab∣x∣2ΔΦ,a,b=1,…,6,(43)\langle\Phi_a^\dagger(\mathbf{x}) \Phi_b(0) \rangle\propto\frac{\delta_{ab}}{|\mathbf{x}|^{2\Delta_\Phi}}, \qquad a,b = 1,\ldots,6, \tag*{(43)}

as discussed in Refs. [3, 4]. The microscopic spin and dimer operators generally have different nonuniversal overlaps with their corresponding continuum monopole operators. Consequently, their long-distance correlations need not have the same amplitude, but they are expected to share the same scaling dependence,

CΓ−E2zz(r)≃Asr−2ΔΦ,CF2D(r)≃Adr−2ΔΦ,(44)C_{\Gamma-E_2}^{zz}(r) \simeq A_s r^{-2\Delta_\Phi}, \\ C_{F_2}^{D}(r) \simeq A_d r^{-2\Delta_\Phi}, \tag*{(44)}

up to subleading lattice and finite-size corrections. It follows that in the regime controlled by the common monopole operator,

CF2D(r)≃ACΓ−E2zz(r),A=AdAs,(45)C_{F_2}^{D}(r) \simeq A C_{\Gamma-E_2}^{zz}(r), \qquad A = \frac{A_d}{A_s}, \tag*{(45)}

where AA is nonuniversal. This is the motivation for the comparison in Fig. 2d of the main text. Across different separations and the three system sizes L=6L = 6, 88, and 1010, the two independently constructed symmetry-resolved correlations collapse approximately onto a common proportional relation.

Additional data for spin and dimer correlation functions

For comparison with the symmetry-resolved channels, here we define the raw real-space spin correlation by averaging over all physical site pairs belonging to the same periodic-boundary-condition distance shell,

Crawzz(r)=1Nr(s)∑dPBC(i,j)=r⟨SizSjz⟩,(46)C^{zz}_{\mathrm{raw}}(r) = \frac{1}{N_r^{(s)}} \sum_{d_{\mathrm{PBC}}(i,j)=r} \langle S_i^z S_j^z \rangle, \tag*{(46)}

where Nr(s)N_r^{(s)} denotes the number of site pairs in the shell at distance rr. We also define the raw connected dimer correlation by averaging over all physical nearest-neighbour bond pairs with the same periodic-boundary-condition distance between their bond midpoints,

CrawD(r)=1Nr(b)∑dPBC(rb,rb′)=r[⟨BbBb′⟩−⟨Bb⟩⟨Bb′⟩],(47)C^{D}_{\mathrm{raw}}(r) = \frac{1}{N_r^{(b)}} \sum_{d_{\mathrm{PBC}}(r_b,r_{b'})=r} \left[\langle B_b B_{b'}\rangle- \langle B_b\rangle\langle B_{b'}\rangle\right], \tag*{(47)}

where rbr_b denotes the midpoint of bond bb, and Nr(b)N_r^{(b)} is the number of bond pairs in the corresponding distance shell.

The raw spin and dimer correlations are shown in Figure 4a and d, respectively. Both quantities exhibit pronounced oscillations between positive and negative values as a function of distance rr, together with an overall decrease in magnitude toward longer distances. Such behavior reflects the superposition of different internal sublattice or bond-symmetry components in the symmetry-unresolved correlators, and the raw correlations therefore do not exhibit a clear scaling form.

Real-space spin and dimer correlations and their effective scaling behavior

Figure 4. Real-space spin and dimer correlations and their effective scaling behavior for L=10L = 10. a, Symmetry-unresolved spin correlation Crawzz(r)C^{zz}_{\mathrm{raw}}(r), shown on a signed symmetric-logarithmic y-axis scale. b, c, Symmetry-resolved Γ\Gamma-E2E_2 spin correlation CΓ−E2zz(r)C^{zz}_{\Gamma-E_2}(r) fitted to a power-law form Ar−pAr^{-p} and an exponential form Ae−r/ξA e^{-r/\xi}, respectively. The fits use the axial separations r=2,4,6,8,10r = 2, 4, 6, 8, 10 and yield p=3.13p = 3.13 with R2=0.99997R^2 = 0.99997 for the power-law fit and ξ=0.94\xi= 0.94 with R2=0.99943R^2 = 0.99943 for the exponential fit. d, Symmetry-unresolved connected dimer correlation CrawD(r)C^{D}_{\mathrm{raw}}(r), also shown on a signed symmetric-logarithmic scale. e, f, Symmetry-resolved MM-F2F_2 dimer correlation CF2D(r)C^{D}_{F_2}(r) fitted to the same power-law and exponential forms, giving p=2.98p = 2.98 with R2=0.99984R^2 = 0.99984 and ξ=1.01\xi= 1.01 with R2=0.99862R^2 = 0.99862, respectively. The raw spin and dimer correlations use physical site and bond-midpoint separations, respectively, whereas the symmetry-resolved correlations use Bravais-cell displacements.

To further examine the scaling behavior of the correlation functions, we now consider the symmetry-resolved correlation functions CΓ−E2zz(r)C^{zz}_{\Gamma-E_2}(r) and CF2D(r)C^{D}_{F_2}(r) defined by Eq. (32) and (42), respectively. The results of CΓ−E2zz(r)C^{zz}_{\Gamma-E_2}(r) for L=10L = 10 are shown in Figure 4b and c, with the power-law, i.e., Eq. (44), and exponential fits, respectively. We emphasize that for the fitting analysis, we restrict ourselves to the axial r=2,4,6,8r = 2, 4, 6, 8 and 1010, which correspond to displacements by one to five Bravais unit cells along the principal lattice directions. Since the primitive Bravais vectors have length 2 in units of the nearest-neighbor spacing, these are precisely the nonzero axial separations available on the L=10L = 10 torus up to the half-system distance. Restricting the fits to these axial shells allows us to compare the distance dependence along a fixed crystallographic direction and avoids mixing inequivalent lattice directions, which can exhibit different finite-size and short-distance corrections.

As shown in Figure 4b and Figure 4c, the power-law form provides the better fit. Similarly, based on the results of CF2D(r)C_{F_2}^{D}(r) shown in Figure 4e and Figure 4f, the power-law form describes the dimer correlation function better than the exponential form within the same fitting window. For the power-law fits, the decay rates of CΓ−E2Z2(r)C_{\Gamma-E_2}^{Z_2}(r) and CF2D(r)C_{F_2}^{D}(r) are 2ΔΦ=3.132\Delta_{\Phi}=3.13 and 2.982.98, respectively, which are close to one another.

Gutzwiller-projected spin-liquid reference states and wave-function fidelity

In this section, we provide the explicit construction of the three Gutzwiller-projected fermionic reference states used in Fig. 3 of the main text and describe the evaluation of their fidelities with the optimized KagomeResNet wave functions. Following the notation of the main text, the three reference states are labeled by

μ=Dirac,μ=Z2,μ=Z2∗.(48)\mu=\mathrm{Dirac},\qquad\mu=Z_2,\qquad\mu=Z_2^*. \tag*{(48)}

Here, μ=Dirac\mu=\mathrm{Dirac} denotes the optimized extended U(1)U(1) Dirac state of Ref. [5]; μ=Z2\mu=Z_2 denotes a finite-pairing Z2[0,π]βZ_2[0,\pi]\beta state obtained by perturbing the same Dirac state as considered in Ref. [5]; and μ=Z2∗\mu=Z_2^* denotes the fully optimized Z2[0,π]βZ_2[0,\pi]\beta resonating-valence-bond (RVB) state of Ref. [6]. The auxiliary-spinon boundary condition is denoted by ν\nu, so that the corresponding projected reference state is written as ∣Φμ,ν⟩\lvert\Phi_{\mu,\nu}\rangle.

Unified fermionic parent Hamiltonian

We use the fermionic parton representation

Si=12fiα†σαβfiβ,(49)S_i=\frac{1}{2}f^\dagger_{i\alpha}\sigma_{\alpha\beta}f_{i\beta}, \tag*{(49)}

subject to the local single-occupancy constraint

ni↑+ni↓=1.(50)n_{i\uparrow}+n_{i\downarrow}=1. \tag*{(50)}

All three reference states in Eq. (48) can be generated from the same general real singlet-BCS parent Hamiltonian,

Hpar=∑ij,σtijfiσ†fjσ+12∑ij[Δij(fi↑†fj↓†−fi↓†fj↑†)+H.c.],(51)H_{\mathrm{par}}=\sum_{ij,\sigma}t_{ij}f^\dagger_{i\sigma}f_{j\sigma} +\frac{1}{2}\sum_{ij}\left[\Delta_{ij}\left(f^\dagger_{i\uparrow}f^\dagger_{j\downarrow}-f^\dagger_{i\downarrow}f^\dagger_{j\uparrow}\right)+\mathrm{H.c.}\right], \tag*{(51)}

where tij=tjit_{ij}=t_{ji} and Δij=Δji\Delta_{ij}=\Delta_{ji} are real in the gauges considered below. The case i=ji=j is included, allowing for on-site particle-hole and pairing terms.

Introducing the Nambu spinor

ψi=(fi↑fi↓†),(52)\psi_i=\begin{pmatrix}f_{i\uparrow}\\f^\dagger_{i\downarrow}\end{pmatrix}, \tag*{(52)}

the parent Hamiltonian may equivalently be expressed in terms of matrices

Uij=tijτ3+Δijτ1(53)U_{ij}=t_{ij}\tau_3+\Delta_{ij}\tau_1 \tag*{(53)}

in the τ1\tau_1–τ3\tau_3 plane of the SU(2) gauge space.

For a given reference family μ\mu and auxiliary-spinon boundary condition ν\nu, we denote the mean-field ground state of Eq. (51) by ∣ΨMF(μ,ν)⟩\lvert\Psi_{\mathrm{MF}}^{(\mu,\nu)}\rangle. The physical reference state entering the fidelity calculation is

∣Φμ,ν⟩=PG∣ΨMF(μ,ν)⟩,(54)\lvert\Phi_{\mu,\nu}\rangle=P_G\lvert\Psi_{\mathrm{MF}}^{(\mu,\nu)}\rangle, \tag*{(54)}

where

PG=∏iδni↑+ni↓,1(55)P_G=\prod_i\delta_{n_{i\uparrow}+n_{i\downarrow},1} \tag*{(55)}

projects onto exactly one spinon per kagome site.

Equation (44) provides a common parent-Hamiltonian framework for all three reference states. The specific hopping and pairing amplitudes, however, must be implemented in the gauge convention of the corresponding literature Ansatz. In particular, the parametrization of Ref. [6] should not be obtained by directly identifying its parameters with those of Ref. [5]; the two Ansätze use different bond sign conventions and the former additionally contains third-neighbour RVB amplitudes.

Dirac reference state: μ=Dirac\mu= \mathrm{Dirac}

For μ=Dirac\mu= \mathrm{Dirac}, we use the optimized extended U(1)U(1) Dirac state of Ref. [5]. In the gauge used therein, the NN and second-neighbour mean-field matrices are

Uij(1)=sijχ1τ3,Uij(2)=νijIχ2τ3,(56)\begin{aligned} U_{ij}^{(1)} &= s_{ij}\chi_{1}\tau_{3},\\ U_{ij}^{(2)} &= \nu_{ij}^{I}\chi_{2}\tau_{3}, \tag*{(56)} \end{aligned}

where sij=±1s_{ij}=\pm1 and νijI=±1\nu_{ij}^{I}=\pm1 follow the bond-sign convention of Fig. 1 of Ref. [5]. The Ansatz has zero gauge flux through the elementary triangles and π\pi flux through the hexagons.

The NN hopping defines the overall energy scale,

χ1=1.(57)\chi_{1}=1. \tag*{(57)}

The optimized extended Dirac state reported in Ref. [5] has

χ2=−0.0186(2),μI=−0.5124(5),Δ2=ζ=0,(58)\chi_{2}=-0.0186(2),\qquad\mu_{I}=-0.5124(5),\qquad\Delta_{2}=\zeta=0, \tag*{(58)}

where μI\mu_{I} denotes the uniform on-site chemical potential of the mean-field Hamiltonian. We use the subscript “I” here to distinguish this parameter from the reference-state label μ\mu employed throughout the main text.

Because the state is number conserving and the projected wave function is constructed at fixed half filling, the uniform chemical-potential term does not change the occupied single-particle orbitals. The projected reference state is therefore fully determined by the hopping Ansatz in Eq. (56) at half filling.

Ref. [5] performed the variational calculation on a 432-site cluster with mixed periodic-antiperiodic spinon boundary conditions and obtained

EDiracN=−0.42872(1)J.(59)\frac{E_{\mathrm{Dirac}}}{N}=-0.42872(1)J. \tag*{(59)}
Finite-pairing Z2Z_{2} reference state: μ=Z2\mu= Z_{2}

The reference state denoted by μ=Z2\mu= Z_{2} in the main text is a finite-pairing Z2[0,π]βZ_{2}[0,\pi]_{\beta} state in the immediate variational neighbourhood of the Dirac state of Ref. [5]. Its mean-field matrices are

Uii(0)=μIτ3+ζτ1,Uij(1)=sijχ1τ3,Uij(2)=νijI(χ2τ3+Δ2τ1),Uij(3)=0.(60)\begin{aligned} U_{ii}^{(0)} &= \mu_{I}\tau_{3}+\zeta\tau_{1},\\ U_{ij}^{(1)} &= s_{ij}\chi_{1}\tau_{3},\\ U_{ij}^{(2)} &= \nu_{ij}^{I}\left(\chi_{2}\tau_{3}+\Delta_{2}\tau_{1}\right),\\ U_{ij}^{(3)} &= 0. \tag*{(60)} \end{aligned}

A nonzero Δ2\Delta_{2} generates noncommuting SU(2)\mathrm{SU}(2) fluxes and breaks the emergent U(1)U(1) gauge structure of the Dirac Ansatz down to Z2Z_{2}.

The specific finite-pairing reference used in Fig. 3 is defined by

χ1=1,χ2=−0.0186,μI=−0.5124,Δ2=0.05,ζ=0.1780.(61)\begin{aligned} \chi_{1} &= 1,\\ \chi_{2} &= -0.0186,\\ \mu_{I} &= -0.5124,\\ \Delta_{2} &= 0.05,\\ \zeta&= 0.1780. \tag*{(61)} \end{aligned}

For Δ2=0.05\Delta_{2}=0.05, Ref. [5] reports the corresponding optimized on-site pairing ζ=0.1780(2)\zeta=0.1780(2). Within the statistical resolution of the VMC calculation, introducing this finite pairing produces no detectable change in the variational energy relative to the Dirac state. Therefore, on the same N=432N=432 cluster,

EZ2N≃−0.42872(1)J,(62)\frac{E_{Z_{2}}}{N}\simeq-0.42872(1)J, \tag*{(62)}

where Ref. [5] does not quote a separate independent energy value for this particular finite-pairing point.

We stress that the state defined by Eq. (61) is not the fully optimized minimum of the Z2[0,π]βZ_2[0,\pi]\beta variational family. Upon complete stochastic-reconfiguration optimization, Ref. [5] finds Δ2,ζ→0\Delta_2,\zeta\to0, such that the optimized state returns to the U(1)U(1) Dirac state. We retain the finite-pairing state as an independent reference in order to test whether the optimized NQS shows an enhanced affinity to an explicit U(1)→Z2U(1) \to Z_2 pairing perturbation whose energy is nearly indistinguishable from that of the Dirac state.

Optimized Z2Z_2 reference state: μ=Z2∗\mu= Z_2^*

For the second Z2Z_2 reference, denoted by μ=Z2∗\mu= Z_2^*, we use the fully optimized Z2[0,π]βZ_2[0,\pi]\beta RVB state of Ref. [6]. This Ansatz is defined in a different gauge from that of Ref. [5] and contains RVB amplitudes up to third-neighbour bonds. Following Ref. [6], we introduce

nϕ=(sin⁡ϕ,0,cos⁡ϕ),(63)\mathbf{n}_{\phi}=(\sin\phi,0,\cos\phi), \tag*{(63)}

such that

nϕ⋅τ=sin⁡ϕ τ1+cos⁡ϕ τ3.(64)\mathbf{n}_{\phi}\cdot\boldsymbol{\tau}=\sin\phi\,\tau_1+\cos\phi\,\tau_3. \tag*{(64)}

With the NN amplitude fixed to unity, the parent matrices are

Uij={−μZnϕ1⋅τ,i=j,−sijZτ3,⟨ij⟩1,−sijZρnϕ2⋅τ,⟨ij⟩2,−sijZηnϕ3⋅τ,⟨ij⟩3.(65)U_{ij}= \begin{cases} -\mu_Z\mathbf{n}_{\phi_1}\cdot\boldsymbol{\tau}, & i=j,\\ -s_{ij}^{Z}\tau_3, & \langle ij\rangle_1,\\ -s_{ij}^{Z}\rho\mathbf{n}_{\phi_2}\cdot\boldsymbol{\tau}, & \langle ij\rangle_2,\\ -s_{ij}^{Z}\eta\mathbf{n}_{\phi_3}\cdot\boldsymbol{\tau}, & \langle ij\rangle_3. \end{cases} \tag*{(65)}

Here sijZ=±1s_{ij}^{Z}=\pm1 follows the six-site spinon-unit-cell gauge shown in Fig. 1 of Ref. [6]. The spinon unit cell is doubled along a1\mathbf{a}_1 and therefore contains six kagome sites.

In terms of the hopping and pairing amplitudes of the unified parent Hamiltonian in Eq. (51), the second- and third-neighbour terms are

tii=−μZcos⁡ϕ1,Δii=−μZsin⁡ϕ1,tij(2)=−sijZρcos⁡ϕ2,Δij(2)=−sijZρsin⁡ϕ2,tij(3)=−sijZηcos⁡ϕ3,Δij(3)=−sijZηsin⁡ϕ3.(66)\begin{aligned} t_{ii}&=-\mu_Z\cos\phi_1, & \Delta_{ii}&=-\mu_Z\sin\phi_1,\\ t_{ij}^{(2)}&=-s_{ij}^{Z}\rho\cos\phi_2, & \Delta_{ij}^{(2)}&=-s_{ij}^{Z}\rho\sin\phi_2,\\ t_{ij}^{(3)}&=-s_{ij}^{Z}\eta\cos\phi_3, & \Delta_{ij}^{(3)}&=-s_{ij}^{Z}\eta\sin\phi_3. \tag*{(66)} \end{aligned}

Thus, η\eta is the magnitude of the third-neighbour RVB parameter and should not be identified with the on-site pairing ζ\zeta in Eq. (60). Similarly, ρ\rho cannot be identified directly with the Iqbal parameter χ2\chi_2 without an explicit gauge transformation of the complete bond-sign structure.

For the three system sizes studied in the present work, we use the finite-size optimized parameters shown in Fig. 3 of Ref. [6]. The values used in our numerical implementation are The optimized parameters are presented graphically rather than tabulated numerically in Ref. [6]; the values in Table I (PDF p. 19) were therefore obtained by digitizing the published Fig. 3. The retained digits are used only to define the numerical reference states reproducibly and should not be interpreted as table-level precision of the original publication.

The corresponding variational energies reported directly in Table I (PDF p. 19) of Ref. [6] are. The L=18L=18 cluster is the largest size for which Ref. [6] reports well-converged optimized RVB parameters. At this size, the corresponding optimized U(1)U(1) state has energy density −0.428719(1)J-0.428719(1)J, while the optimized Z2Z_2 state has −0.428737(1)J-0.428737(1)J. Thus, within this projected-RVB variational family, the Z2Z_2 state lies slightly below the optimized U(1)U(1) state in energy.

LLN=3L2N = 3L^2EZ2∗/NE_{Z_2^*}/N
6108−0.428899(1)J-0.428899(1)J
8192−0.428783(1)J-0.428783(1)J
10300−0.428753(1)J-0.428753(1)J
18972−0.428737(1)J-0.428737(1)J

Table II. Variational energy density of the optimized Z2∗Z_2^* reference state reported in [6].

LLPAAP
6open shellclosed shell
8closed shellclosed shell
10open shellclosed shell

Table III. Closed-shell structure of the two mixed spinon boundary conditions for the Dirac reference state.

The three reference states used in Fig. 3 are therefore complementary. The state μ=Dirac\mu= \mathrm{Dirac} provides an optimized extended U(1)U(1) Dirac reference; μ=Z2\mu= Z_2 probes an explicit finite-pairing perturbation in the immediate neighbourhood of this Dirac state; and μ=Z2∗\mu= Z_2^* provides an independently optimized and energetically competitive Z2[0,π]βZ_2[0,\pi]\beta reference with additional third-neighbour variational freedom.

Auxiliary-spinon boundary conditions

The physical kagome Heisenberg Hamiltonian and the KagomeResNet NQS are always defined with periodic boundary conditions. The boundary conditions discussed here apply only to the auxiliary fermions used to construct the Gutzwiller-projected reference states.

For an L×LL \times L torus, we define

fr+La1=eiθ1fr,fr+La2=eiθ2fr,(67)f_{\mathbf r+L\mathbf a_1}=e^{i\theta_1}f_{\mathbf r}, \qquad f_{\mathbf r+L\mathbf a_2}=e^{i\theta_2}f_{\mathbf r}, \tag*{(67)}

with θ1,2=0\theta_{1,2}=0 or π\pi. We denote periodic and antiperiodic boundary conditions by P and A, respectively. Following the mixed periodic-antiperiodic boundary conditions used in the original VMC calculations Refs. [5, 6], the fidelity comparison in the main text is restricted to

ν∈{PA,AP}.(68)\nu\in\{\mathrm{PA},\mathrm{AP}\}. \tag*{(68)}

Here, the first and second letters refer to the spinon boundary conditions along a1\mathbf a_1 and a2\mathbf a_2, respectively. For μ=Dirac\mu=\mathrm{Dirac}, the number-conserving mean-field state is used only when the half-filled spinon spectrum forms a nondegenerate closed shell. For the system sizes considered here, the two mixed sectors satisfy An open-shell sector is excluded because the degenerate single-particle level at the Fermi energy does not define a unique half-filled Slater determinant without an additional prescription for the zero-mode occupations.

For the paired references μ=Z2\mu=Z_2 and μ=Z2∗\mu=Z_2^*, the BdG ground state is well defined in both mixed sectors, and both PA and AP are retained. Consequently, the quantity reported in Fig. 3 of the main text is

Fμmax⁡=max⁡ν∈Bμ(L)Fμ,ν,(69)F_\mu^{\max}=\max_{\nu\in\mathcal B_\mu(L)}F_{\mu,\nu}, \tag*{(69)}

where Bμ(L)\mathcal B_\mu(L) denotes the admissible set of mixed boundary sectors for reference state μ\mu. For μ=Dirac\mu=\mathrm{Dirac}, Bμ(L)\mathcal B_\mu(L) contains only nondegenerate closed-shell sectors; for μ=Z2\mu=Z_2 and Z2∗Z_2^*, both mixed sectors are included.

Projected amplitudes and Monte Carlo evaluation of the fidelity

For the number-conserving reference μ=Dirac\mu=\mathrm{Dirac}, the mean-field ground state is obtained by filling the lowest N/2N/2 single-particle orbitals for each spin species. For a spin configuration s\mathbf s with sets of up- and down-spin sites U\mathcal U and D\mathcal D, respectively, the Gutzwiller-projected amplitude can be expressed as the product of the corresponding Slater determinants,

ΦDirac,ν(s)∝(−1)P(s)det⁡MU(ν)det⁡MD(ν),(70)\Phi_{\mathrm{Dirac},\nu}(\mathbf s)\propto(-1)^{P(\mathbf s)}\det M_{\mathcal U}^{(\nu)}\det M_{\mathcal D}^{(\nu)}, \tag*{(70)}

where (−1)P(s)(-1)^{P(s)} accounts for the fermionic reordering required to express the projected state in the spin-configuration basis used for the NQS.

For μ=Z2\mu= Z_2 and Z2∗Z_2^*, diagonalization of the corresponding BdG Hamiltonian yields the BCS pair matrix F(μ,ν)F^{(\mu,\nu)}. After projection to N↑=N↓=N/2N_{\uparrow}=N_{\downarrow}=N/2, the amplitude is

Φμ,ν(s)∝(−1)P(s)det⁡F↑,↓(μ,ν),μ=Z2,Z2∗.(71)\Phi_{\mu,\nu}(\mathbf{s}) \propto(-1)^{P(\mathbf{s})}\det F_{\uparrow,\downarrow}^{(\mu,\nu)}, \qquad\mu=Z_2,Z_2^*. \tag*{(71)}

Thus, all three projected references and the optimized NQS are evaluated in exactly the same physical spin basis.

For each admissible pair (μ,ν)(\mu,\nu), the normalized squared many-body fidelity is

Fμ,ν=∣⟨Ψθopt∣Φμ,ν⟩∣2⟨Ψθopt∣Ψθopt⟩⟨Φμ,ν∣Φμ,ν⟩.(72)F_{\mu,\nu}=\frac{\left|\langle\Psi_\theta^{\mathrm{opt}}|\Phi_{\mu,\nu}\rangle\right|^2}{\langle\Psi_\theta^{\mathrm{opt}}|\Psi_\theta^{\mathrm{opt}}\rangle\langle\Phi_{\mu,\nu}|\Phi_{\mu,\nu}\rangle}. \tag*{(72)}

This is the same quantity defined in the main text.

We evaluate Eq. (72) using configurations sampled from the optimized NQS probability distribution

pθ(s)=∣Ψθopt(s)∣2⟨Ψθopt∣Ψθopt⟩.(73)p_\theta(\mathbf{s})=\frac{\left|\Psi_\theta^{\mathrm{opt}}(\mathbf{s})\right|^2}{\langle\Psi_\theta^{\mathrm{opt}}|\Psi_\theta^{\mathrm{opt}}\rangle}. \tag*{(73)}

For each reference state, we define the amplitude ratio

Rμ,ν(s)=Φμ,ν(s)Ψθopt(s).(74)R_{\mu,\nu}(\mathbf{s})=\frac{\Phi_{\mu,\nu}(\mathbf{s})}{\Psi_\theta^{\mathrm{opt}}(\mathbf{s})}. \tag*{(74)}

It then follows that

⟨Rμ,ν⟩pθ=⟨Ψθopt∣Φμ,ν⟩⟨Ψθopt∣Ψθopt⟩,(75)\left\langle R_{\mu,\nu}\right\rangle_{p_\theta}=\frac{\langle\Psi_\theta^{\mathrm{opt}}|\Phi_{\mu,\nu}\rangle}{\langle\Psi_\theta^{\mathrm{opt}}|\Psi_\theta^{\mathrm{opt}}\rangle}, \tag*{(75)}

and

⟨∣Rμ,ν∣2⟩pθ=⟨Φμ,ν∣Φμ,ν⟩⟨Ψθopt∣Ψθopt⟩.(76)\left\langle|R_{\mu,\nu}|^2\right\rangle_{p_\theta}=\frac{\langle\Phi_{\mu,\nu}|\Phi_{\mu,\nu}\rangle}{\langle\Psi_\theta^{\mathrm{opt}}|\Psi_\theta^{\mathrm{opt}}\rangle}. \tag*{(76)}

Therefore,

Fμ,ν=∣⟨Rμ,ν⟩pθ∣2⟨∣Rμ,ν∣2⟩pθ.(77)F_{\mu,\nu}=\frac{\left|\left\langle R_{\mu,\nu}\right\rangle_{p_\theta}\right|^2}{\left\langle|R_{\mu,\nu}|^2\right\rangle_{p_\theta}}. \tag*{(77)}

Neither the NQS nor the projected reference state therefore needs to be normalized explicitly.

The fidelity provides information complementary to the symmetry-resolved correlation functions discussed in the preceding section. Whereas the latter probe selected long-distance operators, Fμ,νF_{\mu,\nu} compares the complete many-body amplitude and phase structure of the optimized NQS with a given projected spin-liquid reference state.

As shown in Fig. 3 of the main text, the finite-pairing state μ=Z2\mu=Z_2 has a substantially smaller maximum fidelity than μ=Dirac\mu=\mathrm{Dirac}, despite the two reference states having indistinguishable variational energies at the resolution reported in Ref. [5]. The more competitive reference μ=Z2∗\mu=Z_2^* has a fidelity close to that of the Dirac state for L=6L=6, but its FZ2∗max⁡F_{Z_2^*}^{\max} becomes systematically smaller than FDiracmax⁡F_{\mathrm{Dirac}}^{\max} with increasing system size. Importantly, the Z2∗Z_2^* reference was found in Ref. [6] to have a slightly lower projected-VMC energy than the corresponding optimized U(1)U(1) state. The fidelity comparison is therefore not simply tracking the variational-energy ordering within the projected-fermion family, but provides an independent wave-function-level diagnostic supporting the Dirac interpretation of the optimized KagomeResNet ground state.

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