57 citations · 63 across the 4 of their papers we have counts for
4 papers
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…
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…
Quantum-Inspired Tempering for Ground State Approximation using Artificial Neural Networks
Tameem Albash, Conor Smith, Quinn Campbell +1
A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamilton…
Errors and pseudo-thresholds for incoherent and coherent noise
Mauricio Gutiérrez, Conor Smith, Livia Lulushi +2
We compare the effect of single qubit incoherent and coherent errors on the logical error rate of the Steane [[7,1,3]] quantum error correction code by performing an exact full-den…