Portrait de Cunlu Zhou

Cunlu Zhou

Membre académique associé
cunlu.zhou@mila.quebec
Professeur adjoint, Université de Sherbrooke, Département d'informatique
Sujets de recherche
IA pour la science
Optimisation
Théorie de l'information quantique
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Publications

Generative Learning for Quantum Measurement Design
Jun Dai
Olivier Nahman-Lévesque
Guillaume Rabusseau
Hong-Ye Hu
Cunlu Zhou
Extracting quantum information from a quantum state is a fundamental task of quantum computation, often requiring the estimation of many non… (voir plus)-commuting observables under a finite measurement budget. For both near-term and early fault-tolerant settings, the measurement protocol must balance statistical efficiency against implementation resources such as circuit depth, connectivity, and entangling-gate count. Many existing strategies focus on two extremes: hardware-friendly product measurements with high sampling cost, and fully commuting measurements with deep circuits. Here we recast resource-constrained measurement design as a generative learning problem. We introduce FlowMeas, which uses a generative flow network to directly sample finite ensembles of shallow Clifford measurement circuits subject to a prescribed shot budget and hardware constraints. At zero entangling depth, FlowMeas learns qubit-wise commuting measurement schedules and already matches or improves leading product-measurement methods on nearly all molecular benchmarks. Allowing one or two entangling gate layers yields further reductions in energy estimation error of up to
2026-08-10
arXiv (prépublication)
doi.org
arxiv.org