Benchmarking Machine Learning Algorithms for Adaptive Quantum Phase Estimation with Noisy Intermediate-Scale Quantum Sensors

Abstract

Quantum phase estimation is a paradigmatic problem in quantum sensing andmetrology. Here we show that adaptive methods based on classical machinelearning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach-Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions,superconducting qubits and nitrogen-vacancy (NV) centers in diamond.

References in corpus (9)

• High-sensitivity diamond magnetometer with nanoscale resolution
• Quantum metrology from a quantum information science perspective
• Entanglement-free Heisenberg-limited phase estimation
• Ultrasensitive force and displacement detection using trapped ions
• Interaction-free measurements with superconducting qubits
• Ultra-precision quantum sensing and measurement based on nonlinear hybrid optomechanical systems containing ultracold atoms or atomic Bose-Einstein condensate
• A quantum no-reflection theorem and the speeding up of Grover's search algorithm
• Generalized partial measurements
• Linear Ascending Metrological Algorithm