Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection

• URL: http://arxiv.org/abs/2205.03045v3
• Date: Wed, 25 Jan 2023 10:20:58 GMT
• Title: Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection
• Authors: Christa Zoufal and Ryan V. Mishmash and Nitin Sharma and Niraj Kumar and Aashish Sheshadri and Amol Deshmukh and Noelle Ibrahim and Julien Gacon and Stefan Woerner
• Abstract summary: We introduce a variational quantum algorithm to solve unconstrained black box binary problems. This is in contrast to the typical setting of quantum algorithms for optimization. We show that the quantum method produces competitive and in certain aspects even better performance than traditional feature selection techniques.
• Score: 1.9182522142368683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution. To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: feature selection. This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection -- when formulated in terms of a generic loss function -- offers little structure on which to build classical heuristics, thus resulting primarily in 'greedy methods'. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with 20 and 59 input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry.

Related papers

• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems [65.268245109828]
  We compare the performance of classicals across a series of partially-randomized tasks. We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
  arXiv  Detail & Related papers  (2023-10-14T02:13:26Z)
• Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming [0.0]
  In this work, we integrate the potentials of quantum and classical techniques for optimization. We reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. We present numerous computational results from real quantum hardware.
  arXiv  Detail & Related papers  (2023-02-10T20:12:53Z)
• Quantum-Enhanced Selection Operators for Evolutionary Algorithms [0.0]
  We study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. We benchmark these quantum-enhanced algorithms against classical algorithms.
  arXiv  Detail & Related papers  (2022-06-21T21:36:39Z)
• Quantum Feature Selection [2.5934039615414615]
  In machine learning, fewer features reduce model complexity. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization problem. In contrast to iterative or greedy methods, our direct approach yields higherquality solutions.
  arXiv  Detail & Related papers  (2022-03-24T16:22:25Z)
• 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)
• 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)
• GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models [62.997667081978825]
  We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. We focus on a quantum-inspired version of GEO relying on tensor-network Born machines. We show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls.
  arXiv  Detail & Related papers  (2021-01-15T18:18:38Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

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.