Abstract Quantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum‐enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.
Abstract Quantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum‐enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.
Quantum sensors and metrology harness quantum phenomena—such as superposition, entanglement, and squeezing—to achieve precision measurements beyond classical limits. This review presents an overview of the fundamental principles underlying quantum sensing, including quantum coherence, entanglement-assisted measurement, and quantum-enhanced interferometry. Various quantum sensor platforms are discussed, including atomic clocks, nitrogen-vacancy (NV) center magnetometers, superconducting quantum interference devices (SQUIDs), optomechanical sensors, and trapped-ion systems. We explore their operational mechanisms, performance metrics, and technological implementations. Comparative analyses highlight sensitivity, spatial resolution, bandwidth, and environmental robustness. Advances in materials, control techniques, and hybrid architectures are examined, and emerging applications in navigation, geophysics, medical imaging, and fundamental physics are reviewed. Finally, future prospects for scalable and robust quantum metrology are discussed.
This paper explores the potential of quantum sensors for achieving quantum precision measurement. The core claim is that leveraging the inherent properties of quantum sensors, specifically quantum effects like tunneling and entanglement, can enable significantly more accurate measurements of physical quantities compared to classical techniques. This work outlines the fundamental principles and theoretical frameworks underpinning this approach, focusing on the mechanisms by which quantum sensors achieve this enhanced precision. We examine various quantum sensor modalities, including SQUID magnetometers, atomic clocks, and NV centers, and discuss the challenges and future directions in this burgeoning field. The primary objective is to demonstrate how quantum measurement, exploiting the principles of quantum mechanics, can surpass the limitations imposed by classical measurement methodologies. The discussion includes a review of relevant theoretical concepts and a conceptual framework for understanding the relationship between sensor design, quantum effects, and measurement precision. Ultimately, this paper aims to provide a foundational understanding of the transformative potential of quantum sensing for precision measurement applications.
Entanglement-enhanced quantum metrology explores the utilization of quantum entanglement to enhance measurement precision. When particles in a probe are prepared into a suitable quantum entangled state, they may collectively accumulate information about the physical quantity to be measured, leading to an improvement in measurement precision beyond the standard quantum limit and approaching the Heisenberg limit. The rapid advancement of techniques for quantum manipulation and detection has enabled the generation, manipulation, and detection of multi-particle entangled states in synthetic quantum systems such as cold atoms and trapped ions. This article aims to review and illustrate the fundamental principles and experimental progresses that demonstrate multi-particle entanglement for quantum metrology, as well as discuss the potential applications of entanglement-enhanced quantum sensors.
Quantum entanglement offers powerful opportunities for enhancing measurement sensitivity beyond classical limits, with optical atomic clocks serving as a leading platform for such advances. This chapter introduces the principles of entanglement-enhanced quantum metrology and explores their applications to timekeeping. We review the theoretical framework of quantum phase estimation, comparing frequentist and Bayesian approaches, and discuss paradigmatic entangled states such as spin-squeezed and GHZ states. Particular emphasis is placed on the challenges posed by decoherence, which constrain the practical advantages that can be realized in large-scale devices. The discussion then turns to frequency estimation in atomic clocks, highlighting how experimental constraints shape the translation of abstract quantum limits into real performance gains. Finally, we outline emerging directions of contemporary quantum metrology. Together, these developments underscore the increasingly close interplay between quantum information processing and precision metrology.
We present a review on quantum metrology and sensing, from its foundations to current applications. Highlights of the review include consideration of both frequentist and Bayesian approaches to parameter estimation; single as well as multiparameter estimation; estimation for different encoding processes comprising unitary as well as noisy channels, quantum thermometry, and channels involving indefinite causal order; different estimation strategies incorporating also recent advances like quantum error correction-aided methods and reservoir engineering; usefulness of quantum Fisher information to detect resources; applications of quantum metrology in diverse arenas covering quantum many-body sensors, sensing protocols in atomic ensembles, atom-photon systems, and continuous-variable systems, quantum imaging, quantum illumination, atomic clocks and atom interferometry, etc; and experimental realizations of quantum sensors in different physical platforms.
Abstract In recent years, machine and quantum learning have gained considerable momentum sustained by growth in computational power and data availability and have shown exceptional aptness for solving recognition- and classification-type problems, as well as problems that require complex, strategic planning. In this work, we discuss and analyze the role machine and quantum learning are playing in the development of diamond-based quantum technologies. This matters as diamond and its optically addressable spin defects are becoming prime hardware candidates for solid state-based applications in quantum information, computing and metrology. Through a selected number of demonstrations, we show that machine and quantum learning are leading to both practical and fundamental improvements in measurement speed and accuracy. This is crucial for quantum applications, especially for those where coherence time and signal-to-noise ratio are scarce resources. We summarize some of the most prominent machine and quantum learning approaches that have been conducive to the presented advances and discuss their potential, as well as their limits, for proposed and future quantum applications.