A quantum advantage over classical for local max cut

• URL: http://arxiv.org/abs/2304.08420v4
• Date: Thu, 14 Sep 2023 18:57:34 GMT
• Title: A quantum advantage over classical for local max cut
• Authors: Charlie Carlson, Zackary Jorquera, Alexandra Kolla, Steven Kordonowy
• Abstract summary: Quantum optimization approximation algorithm (QAOA) has a computational advantage over comparable local classical techniques on degree-3 graphs. Results hint that even small-scale quantum computation, which is relevant to the current state-of the art quantum hardware, could have significant advantages over comparably simple classical.
• Score: 48.02822142773719
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We compare the performance of a quantum local algorithm to a similar classical counterpart on a well-established combinatorial optimization problem LocalMaxCut. We show that a popular quantum algorithm first discovered by Farhi, Goldstone, and Gutmannn [1] called the quantum optimization approximation algorithm (QAOA) has a computational advantage over comparable local classical techniques on degree-3 graphs. These results hint that even small-scale quantum computation, which is relevant to the current state-of the art quantum hardware, could have significant advantages over comparably simple classical computation.

Related papers

• Hybrid Quantum-Classical Multilevel Approach for Maximum Cuts on Graphs [1.7720089167719628]
  We introduce a scalable hybrid multilevel approach to solve large instances of Max-Cut. We show that using QAOA within our framework is comparable to classical approaches.
  arXiv  Detail & Related papers  (2023-09-15T23:54:46Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
  Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle. We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm. We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
  arXiv  Detail & Related papers  (2022-05-26T20:32:36Z)
• Quantum version of the k-NN classifier based on a quantum sorting algorithm [0.0]
  We develop a new quantum version of the classical machine learning algorithm known as k-nearest neighbors (k-NN) Both the efficiency and performance of this new quantum version of the k-NN algorithm are compared to those of the classical k-NN and another quantum version proposed by Schuld et al.
  arXiv  Detail & Related papers  (2022-04-07T22:31:01Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Accelerating variational quantum algorithms with multiple quantum processors [78.36566711543476]
  Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages. Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
  arXiv  Detail & Related papers  (2021-06-24T08:18:42Z)
• Progress toward favorable landscapes in quantum combinatorial optimization [0.0]
  We focus on algorithms for solving the algorithm optimization problem MaxCut. We study how the structure of the classical optimization landscape relates to the quantum circuit used to evaluate the MaxCut function.
  arXiv  Detail & Related papers  (2021-05-03T18:38:53Z)
• Quantum algorithmic differentiation [0.0]
  We present an algorithm to perform algorithmic differentiation in the context of quantum computing. We present two versions of the algorithm, one which is fully quantum and one which employees a classical step.
  arXiv  Detail & Related papers  (2020-06-23T22:52:22Z)
• To quantum or not to quantum: towards algorithm selection in near-term quantum optimization [0.0]
  We study the problem of detecting problem instances of where QAOA is most likely to yield an advantage over a conventional algorithm. We achieve cross-validated accuracy well over 96%, which would yield a substantial practical advantage.
  arXiv  Detail & Related papers  (2020-01-22T20:42:02Z)

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.