Experimental realization of quantum-computing-enhanced sensing

• URL: http://arxiv.org/abs/2609.07613v1
• Date: Mon, 07 Sep 2026 15:20:32 GMT
• Title: Experimental realization of quantum-computing-enhanced sensing
• Abstract summary: We realize quantum-computing-enhanced sensing in a spin-oscillator architecture.<n>We experimentally demonstrate the first Grover search in a bosonic mode.<n>Our results establish signal-driven oracles as a route for harnessing oracle-based quantum algorithms.
• Score: 8.854392964542727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum algorithms offer computational advantages, yet incorporating them into quantum sensing and converting these advantages into enhanced information acquisition remains challenging. Here, we realize quantum-computing-enhanced sensing in a spin-oscillator architecture, where a microwave cavity provides a high-dimensional quantum register and a coupled superconducting qubit serves as the sensor. The unknown signal directly generates the phase oracle operation, establishing a natural physical interface between quantum computation and sensing. We experimentally demonstrate the first Grover search in a bosonic mode and observe quantum amplitude amplification in a Hilbert space spanning more than 128 photons. For the same number of sensing iterations, our protocol extracts more information and resolves more frequency candidates than classical sequential search. Our results establish signal-driven oracles as a route for harnessing oracle-based quantum algorithms and realizing quantum-computing-enhanced sensing.

Related papers

• Exponential quantum advantage for learning signals with a single qubit [1.2495457694473517]
  We show that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals.<n>These quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations of physical observables.
  arXiv  Detail & Related papers  (2026-08-13T17:40:39Z)
• Quantum Machine Learning Using Quantum Illumination With Quantum Enhanced Interference [0.0]
  Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques.<n>Superposition and entanglement control are deeply needed for the information-qubit processing in quantum computing.<n>This paper motivated that using quantum-enhanced technique how we can analysis previous superposition of qubit state.
  arXiv  Detail & Related papers  (2026-01-25T15:05:56Z)
• Quantum Computing Enhanced Sensing [0.6407952035735351]
  We present a quantum computing enhanced sensing protocol that outperforms all existing approaches.<n>The key idea is to robustly digitize the continuous, analog signal into a discrete operation, which is then integrated into a quantum algorithm.<n>This work establishes quantum computation as a powerful new resource for advancing sensing capabilities.
  arXiv  Detail & Related papers  (2025-01-13T19:00:00Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Quantum information processing with superconducting circuits: realizing and characterizing quantum gates and algorithms in open quantum systems [0.0]
  This thesis focuses on quantum information processing using the superconducting device. For the realization of quantum gates and algorithms, a one-step approach is used. We suggest faster and more efficient schemes for realizing $X$-rotation and entangling gates for two and three qubits.
  arXiv  Detail & Related papers  (2024-01-14T14:31:17Z)
• 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 process tomography of continuous-variable gates using coherent states [49.299443295581064]
  We demonstrate the use of coherent-state quantum process tomography (csQPT) for a bosonic-mode superconducting circuit. We show results for this method by characterizing a logical quantum gate constructed using displacement and SNAP operations on an encoded qubit.
  arXiv  Detail & Related papers  (2023-03-02T18:08:08Z)
• A quantum processor based on coherent transport of entangled atom arrays [44.62475518267084]
  We show a quantum processor with dynamic, nonlocal connectivity, in which entangled qubits are coherently transported in a highly parallel manner. We use this architecture to realize programmable generation of entangled graph states such as cluster states and a 7-qubit Steane code state.
  arXiv  Detail & Related papers  (2021-12-07T19:00:00Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator [41.74498230885008]
  We demonstrate a programmable quantum simulator based on deterministically prepared two-dimensional arrays of neutral atoms. We benchmark the system by creating and characterizing high-fidelity antiferromagnetically ordered states. We then create and study several new quantum phases that arise from the interplay between interactions and coherent laser excitation.
  arXiv  Detail & Related papers  (2020-12-22T19:00:04Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)

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.