Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors
Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
2 Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Lisbon, Portugal
3 QTF Centre of Excellence, Department of Applied Physics, Aalto University School of Science, Helsinki, Finland
Accepted: 25 May 2021
Published online: 3 June 2021
Quantum phase estimation is a paradigmatic problem in quantum sensing and metrology. Here we show that adaptive methods based on classical machine learning 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.
Key words: Machine learning / Quantum phase estimation / Qubit
© The Author(s) 2021. corrected publication 2021
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