Online adaptive quantum characterization of a nuclear spin

Joas, Timo; Santagati, Raffaele; Gentile, Antonio A.; Bonato, Cristian; Laing, Anthony; McGuinness, Liam P.; Jelezko, Fedor

doi:10.1038/s41534-021-00389-z

Cited by 16 publications

(10 citation statements)

References 34 publications

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“…Efforts in the domain of quantum system characterisation and validation have encompassed machine learning (ML) techniques [1]. ML methodologies and statistical inference have found broad application in the wider development of quantum technologies, from error correction [2,3] to nuclear magnetic resonance spectroscopy [4], and device calibration [5,6]. Here we introduce an ML algorithm which infers a model for quantum systems, allowing for automatic characterisation of such systems and devices.…”

Section: Introductionmentioning

confidence: 99%

Quantum model learning agent: characterisation of quantum systems through machine learning

et al. 2022

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Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distill empirically. Here, we report an algorithm – the Quantum Model Learning Agent (QMLA) – to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F1-score ≥ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250,000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

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Section: Introductionmentioning

confidence: 99%

Quantum model learning agent: characterisation of quantum systems through machine learning

et al. 2022

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“…However, this method can hardly be used in traditional interferometric schemes for measuring oscillating fields, in which spins interact with the fields and accumulate the quantum phase for readout. PEA can effectively improve the dynamic range for interferometric schemes by using different quantum-phase integration time as resources for the algorithm, in either a non-adaptive or adaptive scheme 28 , 29 , 31 , 32 . The precision of PEA depends on how many resources are used, and the PEA readout also has cross talk between the signal frequency offset and the phase 32 .…”

Section: Introductionmentioning

confidence: 99%

Quantum-assisted distortion-free audio signal sensing

et al. 2022

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Quantum sensors are known for their high sensitivity in sensing applications. However, this sensitivity often comes with severe restrictions on other parameters which are also important. Examples are that in measurements of arbitrary signals, limitation in linear dynamic range could introduce distortions in magnitude and phase of the signal. High frequency resolution is another important feature for reconstructing unknown signals. Here, we demonstrate a distortion-free quantum sensing protocol that combines a quantum phase-sensitive detection with heterodyne readout. We present theoretical and experimental investigations using nitrogen-vacancy centers in diamond, showing the capability of reconstructing audio frequency signals with an extended linear dynamic range and high frequency resolution. Melody and speech based signals are used for demonstrating the features. The methods could broaden the horizon for quantum sensors towards applications, e.g. telecommunication in challenging environment, where low-distortion measurements are required at multiple frequency bands within a limited volume.

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“…By employing previous outcomes to improve subsequent measurements, adaptive techniques iteratively seek the most sensitive measurement protocol. In the context of NV sensing, adaptive methods have yielded significant speedups in DC magnetometry [20][21][22][23], characterization of nuclear spins [24], and charge state detection [25].…”

Section: Introductionmentioning

confidence: 99%

Robust spin relaxometry with fast adaptive Bayesian estimation

Caouette-Mansour¹,

Solyom²,

Brandon³

et al. 2022

Preprint

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Spin relaxometry with nitrogen-vacancy (NV) centers in diamond offers a spectrally selective, atomically localized, and calibrated measurement of microwave-frequency magnetic noise, presenting a versatile probe for condensed matter and biological systems. Typically, relaxation rates are estimated with curve-fitting techniques that do not provide optimal sensitivity, often leading to long acquisition times that are particularly detrimental in systems prone to drift or other dynamics of interest. Here we show that adaptive Bayesian estimation is well suited to this problem, producing dynamic relaxometry pulse sequences that rapidly find an optimal operating regime. In many situations (including the system we employ), this approach can speed the acquisition by an order of magnitude. We also present a four-signal measurement protocol that is robust to drifts in spin readout contrast, polarization, and microwave pulse fidelity while still achieving near-optimal sensitivity. The combined technique offers a practical, hardware-agnostic approach for a wide range of NV relaxometry applications.

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Online adaptive quantum characterization of a nuclear spin

Cited by 16 publications

References 34 publications

Quantum model learning agent: characterisation of quantum systems through machine learning

Quantum model learning agent: characterisation of quantum systems through machine learning

Quantum-assisted distortion-free audio signal sensing

Robust spin relaxometry with fast adaptive Bayesian estimation

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