From the 1 of 10 linked papers with an AI index.
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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…
Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing
Isaac L. Huidobro-Meezs, Jun Dai, Rodrigo A. Vargas-Hernández
Achieving chemical accuracy in quantum simulations is often constrained by the measurement bottleneck: estimating operators requires a large number of shots, which remains costly e…
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…
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…