2 papers
quant-ph2025
Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering
Conor Smith, Quinn T. Campbell, Tameem Albash
Parameterized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonian…
quant-ph2025
Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements
Kris Tucker, Amit Kiran Rege, Conor Smith +2
We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the…