Quantum Computing Enhanced Sensing

• URL: http://arxiv.org/abs/2501.07625v1
• Date: Mon, 13 Jan 2025 19:00:00 GMT
• Title: Quantum Computing Enhanced Sensing
• Authors: Richard R. Allen, Francisco Machado, Isaac L. Chuang, Hsin-Yuan Huang, Soonwon Choi,
• Abstract summary: We present a quantum computing enhanced sensing protocol that outperforms all existing approaches. The key idea is to robustly digitize the continuous, analog signal into a discrete operation, which is then integrated into a quantum algorithm. This work establishes quantum computation as a powerful new resource for advancing sensing capabilities.
• Score: 0.6407952035735351
• License:
• Abstract: Quantum computing and quantum sensing represent two distinct frontiers of quantum information science. In this work, we harness quantum computing to solve a fundamental and practically important sensing problem: the detection of weak oscillating fields with unknown strength and frequency. We present a quantum computing enhanced sensing protocol that outperforms all existing approaches. Furthermore, we prove our approach is optimal by establishing the Grover-Heisenberg limit -- a fundamental lower bound on the minimum sensing time. The key idea is to robustly digitize the continuous, analog signal into a discrete operation, which is then integrated into a quantum algorithm. Our metrological gain originates from quantum computation, distinguishing our protocol from conventional sensing approaches. Indeed, we prove that broad classes of protocols based on quantum Fisher information, finite-lifetime quantum memory, or classical signal processing are strictly less powerful. Our protocol is compatible with multiple experimental platforms. We propose and analyze a proof-of-principle experiment using nitrogen-vacancy centers, where meaningful improvements are achievable using current technology. This work establishes quantum computation as a powerful new resource for advancing sensing capabilities.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
  We present a quantum approach to solve a well-studied problem in the context of data sharing. We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-12T20:44:46Z)
• Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis [31.203764035373677]
  We develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations are designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits.
  arXiv  Detail & Related papers  (2023-07-18T04:51:28Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Quantum Computing Quantum Monte Carlo [8.69884453265578]
  We propose a hybrid quantum-classical algorithm that integrates quantum computing and quantum Monte Carlo. Our work paves the way to solving practical problems with intermediatescale and early-fault tolerant quantum computers.
  arXiv  Detail & Related papers  (2022-06-21T14:26:24Z)
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer [0.0]
  We introduce an amplitude-based implementation for approximating non-linearity in the form of the unit step function on a quantum computer. We describe two distinct circuit types which receive their input either directly from a classical computer, or as a quantum state when embedded in a more advanced quantum algorithm.
  arXiv  Detail & Related papers  (2022-06-07T07:14:12Z)
• Optimal metrology with programmable quantum sensors [1.2495977992702094]
  We implement a programmable quantum sensor operating close to the fundamental limits imposed by the laws of quantum mechanics. With 26 ions, we approach the fundamental sensing limit up to a factor of 1.45. This ability illustrates that this next generation of quantum sensor can be employed without prior knowledge of the device or its noise environment.
  arXiv  Detail & Related papers  (2021-07-05T08:30:12Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.