Gravitational-wave matched filtering with variational quantum algorithms

• URL: http://arxiv.org/abs/2408.13177v1
• Date: Fri, 23 Aug 2024 15:53:56 GMT
• Title: Gravitational-wave matched filtering with variational quantum algorithms
• Authors: Jason Pye, Edric Matwiejew, Aidan Smith, Manoj Kovalam, Jingbo B. Wang, Linqing Wen,
• Abstract summary: We explore the application of variational quantum algorithms to the problem of matched filtering in the detection of gravitational waves. We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore the application of variational quantum algorithms designed for classical optimization to the problem of matched filtering in the detection of gravitational waves. Matched filtering for detecting gravitational wave signals requires searching through a large number of template waveforms, to find one which is highly correlated with segments of detector data. This computationally intensive task needs to be done quickly for low latency searches in order to aid with follow-up multi-messenger observations. The variational quantum algorithms we study for this task consist of quantum walk-based generalizations of the Quantum Approximate Optimization Algorithm (QAOA). We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO. These results show that the tested variational quantum algorithms are outperformed by an unstructured restricted-depth Grover search algorithm, suggesting that the latter is optimal for this computational task.

Related papers

• Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
  This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems. Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver. Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
  arXiv  Detail & Related papers  (2024-10-30T08:41:29Z)
• Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice [0.0]
  Variational quantum algorithms and, in particular, variants of the varational quantum eigensolver have been proposed to address optimization (CO) problems. We numerically investigate what this scaling result means in practice for solving CO problems using Max-Cut as a benchmark.
  arXiv  Detail & Related papers  (2024-08-06T09:57:34Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• An introduction to variational quantum algorithms for combinatorial optimization problems [0.0]
  This tutorial provides a mathematical description of the class of Variational Quantum Algorithms. We introduce precisely the key aspects of these hybrid algorithms on the quantum side and the classical side. We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions.
  arXiv  Detail & Related papers  (2022-12-22T14:27:52Z)
• Quantum-inspired optimization for wavelength assignment [51.55491037321065]
  We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
  arXiv  Detail & Related papers  (2022-11-01T07:52:47Z)
• Gravitational wave matched filtering by quantum Monte Carlo integration and quantum amplitude amplification [0.0]
  We propose a new quantum algorithm for matched filtering in gravitational wave (GW) data analysis. Our approach uses the quantum algorithm for Monte Carlo integration for the signal-to-noise ratio (SNR) calculation.
  arXiv  Detail & Related papers  (2022-05-12T09:07:44Z)
• A quantum algorithm for gravitational wave matched filtering [0.0]
  We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. In comparison to the classical method, this provides a speed-up proportional to the square-root of the number of templates. We demonstrate both a proof-of-principle quantum circuit implementation, and a simulation of the algorithm's application to the detection of the first gravitational wave signal GW150914.
  arXiv  Detail & Related papers  (2021-09-03T13:58:58Z)
• Quantum Error Mitigation Relying on Permutation Filtering [84.66087478797475]
  We propose a general framework termed as permutation filters, which includes the existing permutation-based methods as special cases. We show that the proposed filter design algorithm always converges to the global optimum, and that the optimal filters can provide substantial improvements over the existing permutation-based methods.
  arXiv  Detail & Related papers  (2021-07-03T16:07:30Z)
• Efficient Algorithms for Causal Order Discovery in Quantum Networks [44.356294905844834]
  Given black-box access to the input and output systems, we develop the first efficient quantum causal order discovery algorithm. We model the causal order with quantum combs, and our algorithms output the order of inputs and outputs that the given process is compatible with. Our algorithms will provide efficient ways to detect and optimize available transmission paths in quantum communication networks.
  arXiv  Detail & Related papers  (2020-12-03T07:12:08Z)
• Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra [53.46106569419296]
  We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression. We argue that the previous quantum-inspired algorithms for these problems are doing leverage or ridge-leverage score sampling in disguise.
  arXiv  Detail & Related papers  (2020-11-09T01:13:07Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.