Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering

• URL: http://arxiv.org/abs/2507.15845v1
• Date: Mon, 21 Jul 2025 17:53:15 GMT
• Title: Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering
• Authors: Saeed A. Khan, Sridhar Prabhu, Logan G. Wright, Peter L. McMahon,
• Abstract summary: We show protocols for performing nonlinear tasks using Hamiltonian-engineered bosonic systems and quantum signal processing with hybrid qubit-bosonic systems.<n>We have shown that substantial quantum computational-sensing advantages can be obtained even if the quantum system is small.
• Score: 1.3593246617391264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Combining quantum sensing with quantum computing can lead to quantum computational sensors that are able to more efficiently extract task-specific information from physical signals than is possible otherwise. Early examples of quantum computational sensing (QCS) have largely focused on protocols where only a single sensing operation appears before measurement -- with an exception being the recent application of Grover's algorithm to signal detection. In this paper we present, in theory and numerical simulations, the application of two quantum algorithms -- quantum signal processing and quantum neural networks -- to various binary and multiclass machine-learning classification tasks in sensing. Here sensing operations are interleaved with computing operations, giving rise to nonlinear functions of the sensed signals. We have evaluated tasks based on static and time-varying signals, including spatiotemporal signals. Our approach to optimizing the circuit parameters in a QCS protocol takes into account quantum sampling noise and allows us to engineer protocols that can yield accurate results with as few as just a single measurement shot. In all cases, we have been able to show a regime of operation where a quantum computational sensor can achieve higher accuracy than a conventional quantum sensor, with a simulated accuracy advantage of $>$20 percentage points for some tasks. We also present protocols for performing nonlinear tasks using Hamiltonian-engineered bosonic systems and quantum signal processing with hybrid qubit-bosonic systems. Overall, we have shown that substantial quantum computational-sensing advantages can be obtained even if the quantum system is small, including few-qubit systems, systems comprising a single qubit and a single bosonic mode, and even just a single qubit alone -- raising the prospects for experimental proof-of-principle and practical realizations.

Related papers

• 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 [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)
• 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)
• Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor [56.089799129458875]
  We present the results of benchmarking a quantum processor based on trapped $171$Yb$+$ ions.<n>We perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Microwave signal processing using an analog quantum reservoir computer [5.236242306967409]
  We show how a superconducting circuit can be used as an analog quantum reservoir for a variety of classification tasks. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage.
  arXiv  Detail & Related papers  (2023-12-26T18:54:36Z)
• Single-shot Quantum Signal Processing Interferometry [3.431120541553662]
  We present a general algorithmic framework, quantum signal processing interferometry (QSPI), for quantum sensing. We use our QSPI sensing framework to make efficient binary decisions on a displacement channel in the single-shot limit.
  arXiv  Detail & Related papers  (2023-11-22T21:44:14Z)
• Oblivious Quantum Computation and Delegated Multiparty Quantum Computation [61.12008553173672]
  We propose a new concept, oblivious computation quantum computation, where secrecy of the input qubits and the program to identify the quantum gates are required. Exploiting quantum teleportation, we propose a two-server protocol for this task. Also, we discuss delegated multiparty quantum computation, in which, several users ask multiparty quantum computation to server(s) only using classical communications.
  arXiv  Detail & Related papers  (2022-11-02T09:01:33Z)
• 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)
• Benchmarking of Quantum Protocols [0.9176056742068812]
  We consider several quantum protocols that enable promising functionalities and services in near-future quantum networks. We use NetSquid simulation platform to evaluate the effect of various sources of noise on the performance of these protocols.
  arXiv  Detail & Related papers  (2021-11-03T21:17:04Z)
• 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)
• 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)

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.