Variational quantum algorithm for experimental photonic multiparameter estimation

Abstract

Variational quantum metrology represents a powerful tool for optimizing generic estimation strategies, combining the principles of variational optimization with the techniques of quantum metrology. Such optimization procedures result particularly effective for multiparameter estimation problems, where traditional approaches, requiring prior knowledge of the system behavior, often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a multiparameter quantum phase sensor operating in a noisy environment. By exploiting the high reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances, combining classical optimization techniques with quantum circuit evaluations. The latter allows us to compute the system partial derivatives with respect to the variational parameters by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a derivative-free classical learning algorithm run to optimize the measurement settings. Our experimental results reveal significant improvements in terms of estimation accuracy and noise robustness, highlighting the potential of the implementation of variational techniques for practical applications in quantum sensing and more generally for quantum information processing with photonic circuits.

References in corpus (5)

• Integrated Photonic Quantum Technologies
• Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
• Distributed quantum phase estimation with entangled photons
• Femtosecond laser micromachining for integrated quantum photonics
• Probe optimization for quantum metrology via closed-loop learning control

Cited by in corpus (13)

• Quantum sensing with atomic, molecular, and optical platforms for fundamental physics
• Review: Quantum Metrology and Sensing with Many-Body Systems
• Fully-Optimized Quantum Metrology: Framework, Tools, and Applications
• Benchmarking Bayesian quantum estimation
• A Photonic Parameter-shift Rule: Enabling Gradient Computation for Photonic Quantum Computers
• Variational approach to photonic quantum circuits via the parameter shift rule
• Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm
• Quantum memristor with vacuum--one-photon qubits
• Improving quantum metrology protocols with programmable photonic circuits
• Multi-target quantum compilation algorithm
• Photonic variational quantum eigensolver for NISQ-compatible quantum technology
• Quantum multiphase estimation
• Optical Quantum Computing