Encoded Quantum Signal Processing for Heisenberg-Limited Metrology

TL;DR

This paper introduces encoded quantum signal processing, a framework that enhances quantum sensors' robustness to noise, enabling Heisenberg-limited measurement precision through error detection, syndrome measurements, and entanglement strategies.

Contribution

The paper proposes a novel encoded quantum signal processing framework that unifies error detection and signal processing, allowing for noise-robust, Heisenberg-limited quantum metrology without recovery operations.

Findings

Encoding into a repetition code restores Heisenberg scaling under noise.

Product-state sensing with syndrome post-processing is limited to SQL scaling.

Protocols using entanglement or sequential amplification achieve exponential error suppression.

Abstract

Entangled quantum probes can achieve Heisenberg-limited measurement precision, but this advantage is typically destroyed by noise. We address this issue by introducing a framework that we call encoded quantum signal processing, which unifies quantum error detection and quantum signal processing into an effective single-qubit framework, and provides a paradigm for constructing logical sensors that are robust to noise while remaining sensitive to the signal of interest. We show that encoding sensor qubits into a repetition code and using syndrome measurements as a signal-processing primitive restores Heisenberg scaling under realistic noise, without applying recovery operations. We prove that product-state sensing with syndrome post-processing is fundamentally limited to standard quantum limit (SQL) scaling, and develop four protocols that overcome this barrier through entanglement or sequential signal amplification, achieving Heisenberg-limited precision with exponential error suppression in code distance. For spatially inhomogeneous fields, Bayesian marginalization preserves Heisenberg scaling provided noise decreases sufficiently with system size. The underlying mechanism, which we formalize as encoded quantum signal processing, reduces multi-qubit metrology to an effective single-qubit problem where syndrome measurement implements nonlinear signal transformations. Numerical simulations validate the theoretical predictions: syndrome-based inference achieves near-Heisenberg scaling at noise levels where bare probes approach the SQL, and a concatenated protocol maintains this scaling under joint transverse noise and longitudinal inhomogeneities.