https://doi.org/10.1140/epjqt/s40507-021-00105-y
Research
Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors
1
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
Received:
1
February
2021
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 original online version of this article was revised: Following the publication of the original article [1], we were notified that the first and last name of the second author had been swapped. Originally published name: Omar Yasser. Corrected name: Yasser Omar.
A correction to this article is available online at https://doi.org/10.1140/epjqt/s40507-021-00106-x.
Copyright comment corrected publication 2021
© The Author(s) 2021. corrected publication 2021
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