International Conference on Information Technology: New Generations

Machine Learning for Adaptive Quantum Measurement

Alexander Hentschel, Barry C. Sanders

Published Jan 1, 2010• 5 citations

Abstract

One of the most immediate practical applications of quantum information processing is performing precise quantum measurements. Quantum measurement schemes employing adaptive feedback are most effective, since accumulated information from measurements is exploited to maximize the information gain in subsequent measurements. Yet devising such feedback policies is complicated and often involves clever guesswork. Here we present an automated method, based on machine learning, to generate adaptive feedback measurement policies. We apply our technique to adaptive quantum phase measurement, which is important for applications such as atomic clocks and gravitational wave detection. Our algorithm autonomously learns to perform phase estimation based on experimental trial runs, which can be either simulated or performed using a real world experiment.

DOI: 10.1109/itng.2010.109

Related papers

Machine Learning for Precise Quantum Measurement

Alexander Hentschel, Barry C. Sanders

Adaptive feedback schemes are promising for quantum-enhanced measurements yet are complicated to design. Machine learning can autonomously generate algorithms in a classical setting. Here we adapt machine learning for quantum information and use our framework to generate autonomous adaptive feedback schemes for quantum measurement. In particular, our approach replaces guesswork in quantum measurement by a logical, fully automatic, programable routine. We show that our method yields schemes that outperform the best known adaptive scheme for interferometric phase estimation.

arXiv:0910.0762Physical Review LettersFeb 11, 2010197 citations

Experimental Phase Estimation Enhanced by Machine Learning

Alessandro Lumino, Emanuele Polino, Adil S. Rab +4 more

Phase estimation has applications from quantum imaging to gravitational-wave detection. In areas such as biological-system sampling or quantum metrology, it is crucial to optimally acquire information from a very limited number of probes. To address this need, the authors describe and experimentally verify a machine-learning method for optimal adaptive single-photon phase estimation based on a small number of trials. This approach could be used to optimize quantum metrology protocols, and can be extended to general multiparameter scenarios.

arXiv:1712.07570Physical Review AppliedOct 12, 201897 citations

Adaptive Quantum Measurement via Swarm-intelligence Machine Learning

Barry C. Sanders, Alexander Hentschel

We construct an algorithm that learns through trial-and-error training how to devise optimal feedback-based single-shot phase estimation in interferometry. Our algorithm is robust against experimental imperfections, losses and decoherence.

Research in Optical SciencesJan 1, 20120 citations

Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

Nelson Filipe Costa, Yasser Omar, Aidar Sultanov +1 more

Abstract 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.

arXiv:2108.06978EPJ Quantum TechnologyJun 3, 202120 citations

Application of machine learning to experimental design in quantum mechanics

Federico Belliardo, Fabio Zoratti, Vittorio Giovannetti

The recent advances in Machine Learning hold great promises for the field of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, which is typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system the agent is interacting with, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system and only some parameters of the evolution or of the initial state are unknown. We present here a general Machine Learning technique that can optimize the precision of quantum sensors, and in doing so it exploits the knowledge we have on the system. We have developed a Python package to automate a broad class of optimizations that can be found in the tasks of quantum parameter estimation, quantum metrology and quantum hypothesis testing. What the agent is learning here is an optimal adaptive strategy, that, on the basis of the previous outcomes, decides the next measurements to perform. It works both for Bayesian estimation and for frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknowns. The functions of the library allow then to easily train a neural network agent for optimizing the precision of the sensor, by simulating the experiment. We have explored some applications of this technique to magnetometry on NV centers (both DC and AC), to state discrimination in quantum optics and to phase estimation. So far, we were able to certify better results than the current state-of-the-art controls for many examples. The Machine Learning technique developed here can be applied in all those scenarios where the quantum system is well characterized and relatively simple and small. In these cases, we can squeeze every last bit of information from a quantum sensor by controlling it with a neural network appropriately trained.

arXiv:2403.10317International Journal of Quantum InformationJan 20, 20244 citations

Adaptive phase estimation through a genetic algorithm

Kartikeya Rambhatla, Simone Evaldo D’Aurelio, Mauro Valeri +3 more

Quantum metrology is one of the most relevant applications of quantum information theory to quantum technologies.Here, quantum probes are exploited to overcome classical bounds in the estimation of unknown parameters.In this context, phase estimation, where the unknown parameter is a phase shift between two modes of a quantum system, is a fundamental problem.In practical and realistic applications, it is necessary to devise methods to optimally estimate an unknown phase shift by using a limited number of probes.Here we introduce and experimentally demonstrate a machine learning-based approach for the adaptive estimation of a phase shift in a Mach-Zehnder interferometer, tailored for optimal performances with limited resources.The employed technique is a genetic algorithm used to devise the optimal feedback phases employed during the estimation in an offline fashion.The results show the capability to retrieve the true value of the phase by using few photons, and to reach the sensitivity bounds in such small probe regime.We finally investigate the robustness of the protocol with respect to common experimental errors, showing that the protocol can be adapted to a noisy scenario.Such approach promises to be a useful tool for more complex and general tasks where optimization of feedback parameters is required.

Physical Review ResearchJul 15, 202030 citations