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Adaptive Observation Cost Control for Variational Quantum Eigensolvers

ICML 2024 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Sparse variational Gaussian processes

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Proposes SubsCoRe, an adaptive shot-allocation method for sequential minimal optimization (NFT) of VQE that uses GP confident regions and equidistant observations to minimize total measurement shots.

Problem

VQE optimization is costly because each objective observation requires hundreds to thousands of quantum measurement shots; existing SMO (NFT) uses a fixed shot count per observation, wasting quantum budget.

Approach

Requires the GP posterior uncertainty over the 1D subspace being updated to lie within a confident region (CoRe), with the required accuracy adapted to optimization progress; proves that observing 1+2V_d equidistant points gives uniform, min-max optimal posterior uncertainty, so shots can be allocated per point via a simple variance-based rule (SubsCoRe-Center/-Bound variants).

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SubsCoRe-Center with adaptive shot control instead of the evaluated baselinesVQE optimization · Ising Hamiltonian, L=3, Q=5ΔEnergyHelped
use SubsCoRe-Center with adaptive shot control instead of the evaluated baselinesVQE optimization · Ising Hamiltonian, L=3, Q=5ΔFidelityHelped
use SubsCoRe-Bound instead of SubsCoRe-Center (ablation)SubsCoRe · Ising Hamiltonian, L=3, Q=5measurement-shot efficiencyHurt
use SubsCoRe-Bound instead of SubsCoRe-Center (ablation)SubsCoRe · Ising Hamiltonian, L=3, Q=5ΔEnergyHurt
use SubsCoRe-Bound instead of SubsCoRe-Center (ablation)SubsCoRe · Ising Hamiltonian, L=3, Q=5ΔFidelityHurt

Limitations

Experimental setup

Metrics: energy error from ground truth fidelity error total shot count

Baselines: NFT SGLBO EMICoRe SubsCoRe-Bound

Builds on: Nakanishi-Fuji-Todo (NFT) EMICoRe SGLBO VQE kernel

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