4 papers · 1 filter
Generative Learning for Quantum Measurement Design
Jun Dai, Olivier Nahman-Lévesque, Guillaume Rabusseau +2
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non-commuting observables under a finite me…
FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau
Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, wher…
Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing
Isaac L. Huidobro-Meezs, Jun Dai, Rodrigo A. Vargas-Hernández
Estimating molecular Hamiltonians to chemical accuracy requires a large number of measurements. Hamiltonian overlapping grouping methods focus on reducing measurement counts, emplo…
GFlowNets for Hamiltonian decomposition in groups of compatible operators
Isaac L. Huidobro-Meezs, Jun Dai, Guillaume Rabusseau +1
Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical…