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About this paper
Jun Dai, Olivier Nahman-Lévesque, Guillaume Rabusseau, Hong-Ye Hu, Cunlu Zhou
FlowMeas uses a generative flow network to design finite schedules of shallow Clifford measurement circuits under constraints such as entangling depth and connectivity. In numerical molecular benchmarks, its schedules often reduce energy-estimation error relative to product-measurement baselines, and the design pipeline scales to a 54-qubit Hubbard model; warm-starting also speeds retraining across related molecular geometries.
Key insights
- Optimizing a complete finite measurement schedule can improve shot allocation beyond choosing compatible groups of observables alone.
- Shallow entangling circuits often improve accuracy over product-measurement baselines, but additional depth does not guarantee better results.
- The reward proxy matters: the DSS confidence-based objective performed more consistently than the tested variance-plus-bias proxy.
Summary by alphaXiv.
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