A Binary Optimisation Algorithm for Near-Term Photonic Quantum Processors

• URL: http://arxiv.org/abs/2510.08274v1
• Date: Thu, 09 Oct 2025 14:30:50 GMT
• Title: A Binary Optimisation Algorithm for Near-Term Photonic Quantum Processors
• Authors: Alexander Makarovskiy, Mateusz Slysz, Łukasz Grodzki, Dawid Siera, Thorin Farnsworth, William R. Clements, Piotr Rydlichowski, Krzysztof Kurowski,
• Abstract summary: We propose a new algorithm for binary optimisation, designed for near-term photonic quantum processors.<n>This variational algorithm uses samples from a quantum optical circuit, which are post-processed using trainable classical bit-flip probabilities.<n>A gradient-based training loop finds progressively better solutions until convergence.
• Score: 32.80760571694025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binary optimisation tasks are ubiquitous in areas ranging from logistics to cryptography. The exponential complexity of such problems means that the performance of traditional computational methods decreases rapidly with increasing problem sizes. Here, we propose a new algorithm for binary optimisation, the Bosonic Binary Solver, designed for near-term photonic quantum processors. This variational algorithm uses samples from a quantum optical circuit, which are post-processed using trainable classical bit-flip probabilities, to propose candidate solutions. A gradient-based training loop finds progressively better solutions until convergence. We perform ablation tests that validate the structure of the algorithm. We then evaluate its performance on an illustrative range of binary optimisation problems, using both simulators and real hardware, and perform comparisons to classical algorithms. We find that this algorithm produces high-quality solutions to these problems. As such, this algorithm is a promising method for leveraging the scalable nature of photonic quantum processors to solve large-scale real-world optimisation problems.

Related papers

• Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA [55.2480439325792]
  We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators.<n>This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
  arXiv  Detail & Related papers  (2025-03-03T17:15:23Z)
• Solving quadratic binary optimization problems using quantum SDP methods: Non-asymptotic running time analysis [1.9081120388919084]
  Quantum computers can solve semidefinite programs (SDPs) using resources that scale better than state-of-the-art classical methods.<n>We present an analysis of the non-asymptotic resource requirements of a quantum SDP solver.
  arXiv  Detail & Related papers  (2025-02-21T12:54:05Z)
• Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling [96.47086913559289]
  gradient-based algorithms are widely used in bilevel optimization. We introduce a without-replacement sampling based algorithm which achieves a faster convergence rate. We validate our algorithms over both synthetic and real-world applications.
  arXiv  Detail & Related papers  (2024-11-07T17:05:31Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Automatic and effective discovery of quantum kernels [41.61572387137452]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• Stochastic optimization algorithms for quantum applications [0.0]
  We review the use of first-order, second-order, and quantum natural gradient optimization methods, and propose new algorithms defined in the field of complex numbers. The performance of all methods is evaluated by means of their application to variational quantum eigensolver, quantum control of quantum states, and quantum state estimation.
  arXiv  Detail & Related papers  (2022-03-11T16:17:05Z)
• Quantum Levenberg--Marquardt Algorithm for optimization in Bundle Adjustment [0.0]
  We implement a quantum algorithm for solving the linear system of normal equations that calculates the optimization step in Levenberg--Marquardt. The proposed quantum algorithm dramatically reduces the complexity of this operation with respect to the number of points.
  arXiv  Detail & Related papers  (2022-03-04T13:38:21Z)
• Quadratic Unconstrained Binary Optimisation via Quantum-Inspired Annealing [58.720142291102135]
  We present a classical algorithm to find approximate solutions to instances of quadratic unconstrained binary optimisation. We benchmark our approach for large scale problem instances with tuneable hardness and planted solutions.
  arXiv  Detail & Related papers  (2021-08-18T09:26:17Z)
• 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)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization [21.555331273873175]
  We consider the task of decentralized minimization of the sum of smooth strongly convex functions stored across the nodes of a network. We propose two new algorithms for this decentralized optimization problem and equip them with complexity guarantees.
  arXiv  Detail & Related papers  (2020-06-21T11:23:20Z)

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.