5 papers
One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
Juan AgustÃn Duque, Sergio GarcÃa Heredia, Vinicius Hernandes +4
Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are espe…
Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models
Vinicius Hernandes, Joseph Rogers, Rouven Koch +5
Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability dia…
Accurate ground states of lattice gauge theory in 2+1D and 3+1D
Thomas Spriggs, Eliska Greplova, Juan Carrasquilla +1
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of equivariant n…
Adiabatic Fine-Tuning of Neural Quantum States Enables Detection of Phase Transitions in Weight Space
Vinicius Hernandes, Thomas Spriggs, Saqar Khaleefah +1
Neural quantum states (NQS) have emerged as a powerful tool for approximating quantum wavefunctions using deep learning. While these models achieve remarkable accuracy, understandi…
Quantum resources of quantum and classical variational methods
Thomas Spriggs, Arash Ahmadi, Bokai Chen +1
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a…