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Recent Developments and Perspectives in Variational Quantum Eigensolvers for Molecular Electronic Structure: Methods, Tradeoffs, and Benchmarking

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arxiv 2602.11384 v2 pith:CF6A7YPI submitted 2026-02-11 quant-ph

Recent Developments and Perspectives in Variational Quantum Eigensolvers for Molecular Electronic Structure: Methods, Tradeoffs, and Benchmarking

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keywords quantumapproacheschemicallyrecentstructureactivebenchmarkingclassical
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The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) hardware to estimate eigenvalues of many-body Hamiltonians. Unlike fully quantum approaches such as quantum phase estimation (QPE), VQE trades deep coherent circuits for repeated state preparation, measurement, and classical optimization, making it more compatible with limited qubit counts and finite coherence times. Recent developments have focused on reducing quantum resource requirements while retaining chemically meaningful wavefunction structure. In this paper, we examine recent progress in VQE methods for molecular electronic structure with an emphasis on three themes: (i) strategies for circuit and ansatz complexity reduction, including adaptive and selectively screened approaches, (ii) chemically motivated workflows that combine VQE with orbital optimization, fragmentation, and localized active-space ideas to better address strong correlation, and (iii) extensions of VQE to excited-state calculations. Throughout, we emphasize the tradeoffs among parameter count, gate depth, symmetry preservation, measurement overhead, and classical preprocessing, and discuss where these approaches may become most useful for chemically challenging active spaces. We also highlight benchmarking considerations for assessing both accuracy and resource requirements, and conclude with a perspective on regimes in which VQE may offer the greatest long-term value, particularly multireference active spaces and low-lying excited-state manifolds.

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    QLS applied to single- and multi-reference linearized coupled cluster shows polylog κ and sublinear sparsity on model systems, supporting prospects of exponential advantage over conjugate gradient.