A machine learning approach to Bayesian parameter estimation

Nolan, Samuel P.; Smerzi, Augusto; Pezzé, Luca

doi:10.1038/s41534-021-00497-w

Cited by 31 publications

(23 citation statements)

References 67 publications

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“…35,36 Their application to the metrology and sensing fields fosters the idea of self-calibrated quantum sensors not relying on explicit knowledge of the model describing the device operation [37][38][39] and retrieving Hamiltonian parameters directly from experimental data. 40 As an example, Nolan et al 18 reformulated the parameter estimation problem as a classification task to overcome the calibration requirements needed from Bayesian estimation.…”

Section: Artificial Intelligence Quantum Metrologymentioning

confidence: 99%

Deep reinforcement learning for quantum multiparameter estimation

et al. 2023

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Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology.

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Section: Artificial Intelligence Quantum Metrologymentioning

confidence: 99%

Deep reinforcement learning for quantum multiparameter estimation

et al. 2023

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“…This approach of continuous measurement while sensing can also be applied at the level of single quantum trajectories for a single quantum system 92,136,137 . In this case, a continuous measurement signal can be used to track a quantum system while also gaining information about the parameters of the system's Hamiltonian.…”

Section: Application: Quantum Sensingmentioning

confidence: 99%

Engineered Dissipation for Quantum Information Science

Harrington¹,

Mueller²,

Murch³

2022

Preprint

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Quantum information processing relies on precise control of non-classical states in the presence of many uncontrolled environmental degrees of freedom-requiring careful orchestration of how the relevant degrees of freedom interact with that environment. These interactions are often viewed as detrimental, as they dissipate energy and decohere quantum states. Nonetheless, when controlled, dissipation is an essential tool for manipulating quantum information: Dissipation engineering enables quantum measurement, quantum state preparation, and quantum state stabilization. The progress of quantum device technology, marked by improvements of characteristic coherence times and extensible architectures for quantum control, has coincided with the development of such dissipation engineering tools which interface quantum and classical degrees of freedom. This Review presents dissipation as a fundamental aspect of the measurement and control of quantum devices and highlights the role of dissipation engineering for quantum error correction and quantum simulation that enables quantum information processing on a practical scale.

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“…In another vein, machine learning (ML) tools are incorporated to address distinct problems in quantum technologies. In particular, neural networks (NNs) are valuable in distinct quantum sensing scenarios leading to adaptive protocols for phase estimation [17][18][19] , parameter estimation [20][21][22][23][24] , and quantum sensors calibration [25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

A neural network assisted 171Yb+ quantum magnetometer

Chen

Ban

et al. 2022

npj Quantum Inf

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A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of171Yb+ atomic sensors with adequately trained neural networks enables us to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses.

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A machine learning approach to Bayesian parameter estimation

Cited by 31 publications

References 67 publications

Deep reinforcement learning for quantum multiparameter estimation

Deep reinforcement learning for quantum multiparameter estimation

Engineered Dissipation for Quantum Information Science

A neural network assisted 171Yb+ quantum magnetometer

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