We introduce a programmable Heisenberg-limited bosonic quantum sensor based on a nonlinear quantum energy pump, implemented with a Kerr-nonlinear resonator coupled to multiple high-Q microwave terminal resonators. For parameter estimation encoded in an arbitrary number-conserving Hamiltonian acting on the terminal modes, we analytically construct optimal sensing protocols that attain the maximal quantum Fisher information, including initial-state loading, probe preparation, and readout. For diagonal multiparameter signals, we further show that the full phase-sensing quantum Fisher information matrix can be obtained from correlations of locally measured physical terminal works, providing a signal-free calibration of the metrological resource. We numerically demonstrate the construction and its robustness using realistic circuit-QED parameters while including experimentally relevant imperfections.
We introduce a programmable Heisenberg-limited bosonic quantum sensor based on a nonlinear quantum energy pump, implemented with a Kerr-nonlinear resonator coupled to multiple high-Q microwave terminal resonators. For parameter estimation encoded in an arbitrary number-conserving Hamiltonian acting on the terminal modes, we analytically construct optimal sensing protocols that attain the maximal quantum Fisher information, including initial-state loading, probe preparation, and readout. For diagonal multiparameter signals, we further show that the full phase-sensing quantum Fisher information matrix can be obtained from correlations of locally measured physical terminal works, providing a signal-free calibration of the metrological resource. We numerically demonstrate the construction and its robustness using realistic circuit-QED parameters while including experimentally relevant imperfections.
Quantum sensors have been shown to enable more precise measurements than classical sensors. By using entanglement and coherent evolution, the estimation error can be made to scale better in interrogation time, photon count, and other resources, achieving Heisenberg-limited scaling. However, environmental noise and hardware errors typically destroy the coherence of the sensitive quantum states. This motivates the use of fault-tolerant techniques to create more robust quantum sensing protocols. Such fault-tolerant quantum sensing approaches have been shown to enhance sensitivity for qubit systems, but many practical sensors require bosonic probes. In this paper, we propose a fault-tolerant protocol using bosons to estimate displacements in the presence of dephasing noise. We construct an optimal error-correcting code under a mean photon-number constraint. Controlled displacements couple the probe to a repetition-encoded qubit register, while repeated boson-parity measurements and resets remove probe noise without erasing the stored logical phase. Under circuit-level bit-flip noise, a distance-d code suppresses logical failures to order p(d+1)/2. We show that, for a decay rate γA, a total sensing time T and a mean photon number budget N, and for sufficiently small residual signals, the Fisher information scales as NT2 over interrogation times of order γA−(d+1)/2. This is a significant improvement over the non-fault-tolerant approach that achieves Heisenberg scaling only for a total time O(γA−1).
We study optimal quantum sensing of multiple physical parameters using repeated measurements. In this scenario, the Fisher information framework sets the fundamental limits on sensing performance, yet the optimal states and corresponding measurements that attain these limits remain to be discovered. To address this, we extend the Fisher information approach with a second optimality requirement for a sensor to provide unambiguous estimation of unknown parameters. We propose a systematic method integrating Fisher information and Bayesian approaches to quantum metrology to identify the combination of input states and measurements that satisfies both optimality criteria. Specifically, we frame the optimal sensing problem as an optimization of an asymptotic Bayesian cost function that can be efficiently solved numerically and, in many cases, analytically. We refer to the resulting optimal sensor as a `quantum compass' solution, which serves as a direct multiparameter counterpart to the Greenberger-Horne-Zeilinger state-based interferometer, renowned for achieving the Heisenberg limit in single-parameter metrology. We provide exact quantum compass solutions for paradigmatic multiparameter problem of sensing two and three parameters using an SU(2) sensor. Our metrological cost function opens avenues for quantum variational techniques to design low-depth quantum circuits approaching the optimal sensing performance in the many-repetition scenario. We demonstrate this by constructing simple quantum circuits that achieve the Heisenberg limit for vector field and 3D rotations estimation using a limited set of gates available on a trapped-ion platform. Our work introduces and optimizes sensors for a practical notion of optimality, keeping in mind the ultimate goal of quantum sensors to precisely estimate unknown parameters.
We develop a quantum metrological framework for resonant nanophotonic sensors based on subwavelength Fabry--Perot slit cavities. Building on classical Fisher-information analyses of resonant transmission sensors, we model parameter encoding as a phase-and-loss quantum channel embedded in one arm of a Mach-Zehnder interferometer. We derive the quantum Fisher information (QFI) for coherent and Gaussian probe states under linear loss and show that, even at the quantum limit, optimal estimation precision is governed by the generator of parameter-dependent phase shifts rather than by the cavity quality factor. Consequently, the operating point that maximizes the QFI does not generally coincide with the maximum-Q resonance. Quantum resources enhance sensitivity but do not redefine the optimal geometry. Our results provide physically transparent design principles for quantum-enhanced nanophotonic sensing.
Quantum metrology achieves Heisenberg-limited precision in single-parameter estimation, but its multiparameter extension is fundamentally constrained by both parameter-encoding and measurement incompatibility. Noncommuting signal generators may cause incompatible parameter-encoding, preventing the quantum Fisher information matrix from simultaneously achieving the Heisenberg scale for all parameters. Due to incompatible optimal measurements, the classical Fisher information matrix represents the practical attainable precision. Here, we introduce a multiparameter metrology framework based on indefinite evolution (IE), in which different control operations and signal reversal are placed in a coherent superposition. For a single-qubit probe with mutually orthogonal signal generators, IE enables compatible parameter encoding and optimal measurement without the signal reversal. For parallel generators, where only signal reversal realized by its generator is available, IE can achieve the same performance. We further extend this mechanism to noisy, many-body, and high-dimensional probes, and establish general conditions for achieving the simultaneous Heisenberg-limit. In contrast, definite evolution cannot achieve the same performance under compatible optimal measurements, even when signal reversal is available. Our results identify IE as an operational resource for overcoming multiparameter incompatibility and open a route toward attainable Heisenberg-limited sensing in interferometric platforms.
Abstract Quantum Fisher information quantifies the sensitivity encoded in a parameter-dependent quantum state, whereas the performance of a practical sensor is limited by the information that survives the physical channel and can be extracted by the available receiver. We introduce an accessible Fisher-information framework based on a physically admissible family of measurements and use it to separate state-level sensitivity, channel degradation, and receiver limitations. The framework is applied to Ramsey and NOON interferometry, squeezed-state displacement sensing, two-level thermometry, and multiparameter qubit sensing. We show that resource normalization, finite visibility, readout errors, measurement rank, shot allocation, calibration uncertainty, and nuisance parameters can substantially modify the experimentally available advantage. For squeezed-state sensing, homodyne-axis misalignment produces a finite optimal squeezing, while bounded calibration uncertainty leads to a robust operating rule. In the pure-loss model considered here, attenuation reduces the attainable information without shifting this optimum. The results provide practical design rules that connect quantum-enhanced state preparation with realistic measurement constraints and experimentally extractable sensing performance.
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.