13 papers
Scaling Laws for Neural-Network Quantum States
Riccardo Rende, Alessandro Sinibaldi, Luciano Loris Viteritti +3
Scaling laws, the power-law relations between loss, architecture size, and compute observed in modern neural networks, offer a quantitative way to characterize the complexity of a…
Pulse Quality Optimisation in Quantum Optimal Control
Dylan Lewis, Roeland Wiersema
Quantum optimal control methods are widely used to design experimental control pulses such as laser amplitudes, phases, or detunings, that implement a target unitary evolution. In…
Hessian-vector products for tensor networks via recursive tangent-state propagation
Isabel Nha Minh Le, Roeland Wiersema, Christian B. Mendl
Optimizing tensor networks with standard first-order methods often leads to slow convergence and entrapment in local minima. Although second-order optimization offers enhanced robu…
Removing nodal and support-mismatch pathologies in Variational Monte Carlo via blurred sampling
Zhou-Quan Wan, Roeland Wiersema, Shiwei Zhang
Variational Monte Carlo (VMC) is a powerful and fast-growing method for optimizing and evolving parameterized many-body wave functions, especially with modern neural-network quantu…
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…
Neural Quantum States in Mixed Precision
Massimo Solinas, Agnes Valenti, Nawaf Bou-Rabee +1
Scientific computing has long relied on double precision (64-bit floating point) arithmetic to guarantee accuracy in simulations of real-world phenomena. However, the growing avail…