Portrait de Cunlu Zhou

Cunlu Zhou

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

Publications

A Symmetry-Enabled Direct Quantum Protocol for Many-Body Green's Functions
Changhao Yi
Cunlu Zhou
We present a symmetry-enabled direct quantum protocol for computing many-body Green's functions, a central tool for studying strongly correl… (voir plus)ated quantum systems. Our protocol relies only on native time evolution and straightforward measurements available on current hardware platforms. By exploiting parity symmetry—satisfied by a broad class of Hamiltonians in condensed matter physics and quantum chemistry, including the Fermi–Hubbard and Heisenberg models—we introduce a tailored quench spectroscopy scheme that recovers both the real and imaginary parts of two-point time correlators, from which Green's functions can be reconstructed via efficient classical signal processing. We further develop a tailored symmetric quantum Gibbs sampler that prepares parity-resolved (symmetric and antisymmetric) thermal states, enabling finite-temperature extensions within the same framework. Finally, we show that the same symmetry-based measurement primitive extends naturally to out-of-time-ordered correlators (OTOCs). Our results provide a practical route to estimating symmetry-resolved dynamical correlators on near-term and early fault-tolerant quantum hardware.
2026-10-21
Quantum (publié)
doi.org
arxiv.org
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