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Model validation and error attribution for a drifting qubit
Malick A. Gaye, Dylan Albrecht, Steve Young +2
Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value…
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…
Cost of Emulating a Small Quantum Annealing Problem in the Circuit-Model
Javier Gonzalez-Conde, Zachary Morrell, Marc Vuffray +2
Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-m…