Quantum

Bayes-Optimal Quantum Computational Sensing via Convex Spectral Design

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 10/11/2026, 08:43:24 UTC.

Briefings

A new method for quantum computational sensing uses convex spectral design to achieve Bayes-optimal performance. By formulating binary classification directly in terms of measurement responses, the approach avoids nonconvex optimization issues. The method yields an exact finite-dimensional semidefinite program for optimal sensor response, which can be recovered via Fejér-Riesz factorization and generalized quantum signal processing. Numerical tests demonstrate improved accuracy and reliability compared to direct control-angle training, with noise-aware redesign reducing error under phase uncertainty.

Key points

• The method formulates binary phase classification directly in terms of the sensor's measurement response to achieve Bayes-optimal performance.
• For a fixed query budget, generalized quantum signal processing restricts the response to a bounded trigonometric polynomial, making the Bayes classification error linear in the response.

Context

Quantum computational sensing performs task-specific inference directly through coherent sensing dynamics, avoiding an intermediate estimation step. Existing designs typically optimize quantum control parameters through nonconvex programs whose performance can depend strongly on initialization. This contribution formulates binary phase classification directly in terms of the sensor's measurement response.

Limitations

The study relies on numerical tests and assumes a fixed query budget. The exact finite-dimensional semidefinite program is derived analytically, but physical realizability is recovered via synthesis. Noise-aware redesign is supported by the evidence, but specific error reduction metrics under phase uncertainty are not quantified in the provided text.

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