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Overlapped grouping measurement: A unified framework for measuring quantum states

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arxiv 2105.13091 v2 pith:HO5LYGR3 submitted 2021-05-27 quant-ph

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keywords quantummeasurementmeasurementsdifferentframeworkgroupingoverlappedscheme
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Quantum algorithms designed for realistic quantum many-body systems, such as chemistry and materials, usually require a large number of measurements of the Hamiltonian. Exploiting different ideas, such as {importance sampling,} observable compatibility, or classical shadows of quantum states, different advanced measurement schemes have been proposed to greatly reduce the large measurement cost. Yet, the underline cost reduction mechanisms seem distinct from each other, and how to systematically find the optimal scheme remains a critical challenge. Here, we address this challenge by proposing a unified framework of quantum measurements, incorporating advanced measurement methods as special cases. Our framework allows us to introduce a general scheme~ -- ~overlapped grouping measurement, which simultaneously exploits the advantages of most existing methods. An intuitive understanding of the scheme is to partition the measurements into overlapped groups with each one consisting of compatible measurements. We provide explicit grouping strategies and numerically verify its performance for different molecular Hamiltonians with up to 16 qubits. Our numerical result shows significant improvements over existing schemes. Our work paves the way for efficient quantum measurement and fast quantum processing with current and near-term quantum devices.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information

    quant-ph 2025-02 reject novelty 6.0 of 10

    An adaptive estimator based on empirical Bernstein stopping reduces the number of measurements needed to estimate ground-state energies with rigorous error bounds, by up to an order of magnitude in numerical benchmarks.

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