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Deterministic improvements of quantum measurements with grouping of compatible operators, non-local transformations, and covariance estimates

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

Obtaining the expectation value of an observable on a quantum computer is a crucial step in the variational quantum algorithms. For complicated observables such as molecular electronic Hamiltonians, a common strategy is to present the observable as a linear combination of measurable fragments. The main problem of this approach is a large number of measurements required for accurate sampling of the observable's expectation value. We consider several partitioning schemes based on grouping of commuting multi-qubit Pauli products with the goal of minimizing the number of measurements. Three main directions are explored: 1) grouping commuting operators using the greedy approach, 2) involving non-local unitary transformations for measuring, and 3) taking advantage of compatibility of some Pauli products with several measurable groups. The last direction gives rise to a general framework that not only provides improvements over previous methods but also connects measurement grouping approaches with recent advances in techniques of shadow tomography. Following this direction, we develop two new measurement schemes that achieve a severalfold reduction in the number of measurements for a set of model molecules compared to previous state-of-the-art methods.

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2025 1

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representative citing papers

QB Ground State Energy Estimation Benchmark

quant-ph · 2025-08-14 · conditional · novelty 5.0

A new ground-state energy estimation benchmark rates SHCI, DMRG, and double-factorized QPE, reporting near-universal SHCI solvability from an ML extrapolation that its own empirical table only partially supports.

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Showing 1 of 1 citing paper.

  • QB Ground State Energy Estimation Benchmark quant-ph · 2025-08-14 · conditional · none · ref 72 · internal anchor

    A new ground-state energy estimation benchmark rates SHCI, DMRG, and double-factorized QPE, reporting near-universal SHCI solvability from an ML extrapolation that its own empirical table only partially supports.