Quantum

Delta-Corrected Shot Reduction for Bypassing Optimization Plateaus in Variational Quantum Eigensolvers

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 10/11/2026, 08:43:25 UTC.

Briefings

A new method called delta-corrected shot reduction (DCSR) addresses convergence instability in variational quantum eigensolvers (VQE) by using higher-order statistical moments like skewness and kurtosis to correct finite-sample bias. This approach accelerates convergence compared to standard variance-preserved shot reduction while maintaining or reducing cumulative measurement costs, offering a mathematically rigorous framework for variance-stabilized shot allocation on near-term quantum hardware.

Key points

• The study introduces delta-corrected shot reduction (DCSR) as an analytically enhanced shot allocation strategy for variational quantum eigensolvers (VQE).
• The method utilizes the multivariate delta method to derive explicit higher-order bias correction terms, incorporating skewness and kurtosis to bypass noise-induced barren plateaus.

Context

Variational quantum eigensolvers (VQE) are hybrid quantum-classical algorithms for quantum simulation, but their scalability is limited by heavy measurement overheads. Standard variance-preserved shot reduction (VPSR) mitigates cost but suffers from convergence instability and optimization stagnation due to finite-sample bias in limited pilot shots. This finding addresses a critical bottleneck for near-term quantum hardware.

Limitations

The evidence is from an arXiv preprint (arXiv:2611.10524v1) and represents initial theoretical analysis and numerical simulations. Results are subject to validation against experimental data and may vary across different noise and resource regimes. Specific hardware implementations and long-term performance metrics remain unverified.

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