3 papers
quant-ph2026
Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Hang Zhang, Victor Armegioiu, Juan Carrasquilla +4
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization m…
quant-ph2026
Entanglement and optimization within autoregressive neural quantum states
Andrew Jreissaty, Hang Zhang, Jairo C. Quijano +2
Neural quantum states (NQSs) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties o…
cond-mat.str-el2025
Functional Neural Wavefunction Optimization
Victor Armegioiu, Juan Carrasquilla, Siddhartha Mishra +4
We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function spac…