Compact Spin-Charge Separated Neural Quantum States for Valence-Bond States
Neural-network quantum states (NQS) provide a flexible nonlinear representation of quantum many-body wavefunctions, but their efficiency depends sensitively on whether the architecture reflects the sign structure and constrained Hilbert space of the target state. In this work, we propose a solvable-point-guided strategy: design the architecture at an exactly solvable point where the correct local rules can be read off, then refine to the non-exact regime by enlarging only the kernel size and hidden dimension. The strategy is built from four physics-motivated designs: a stride-matched local-rule convolution, geometric pooling, a sign-resolving $\tanh(x^{2k+1})$ activation, and explicit spin-hole sector separation. We test this approach on quasi-one-dimensional valence-bond-solid (VBS) states and their doped soliton variants (sVBS), the exact ground states of a $t$-$J$-like model with a single mobile hole. In finite-size benchmarks, this architecture reaches high fidelity for the exact sVBS state with substantially fewer parameters than generic fully connected, convolutional, and transformer baselines tested under the same setup. For the spin sector, the learned local rule transfers from small to larger systems without retraining. Away from the solvable point, increasing kernel size and hidden dimension systematically improves accuracy, and the model shows approximately $L^2$ parameter scaling in the gapless regime for system size $L$, compared with approximately $L^4$ for matrix-product states in the same regime. Our work establishes a recipe for compact NQS in sign-structured, constrained Hilbert spaces and paves the pathway to physics-informed architectures for the broader $t$-$J$ and Hubbard families.