Filtering variational quantum algorithms for combinatorial optimization

• URL: http://arxiv.org/abs/2106.10055v3
• Date: Thu, 10 Feb 2022 18:16:50 GMT
• Title: Filtering variational quantum algorithms for combinatorial optimization
• Authors: David Amaro, Carlo Modica, Matthias Rosenkranz, Mattia Fiorentini, Marcello Benedetti, Michael Lubasch
• Abstract summary: We introduce the Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. We also explore the use of causal cones to reduce the number of qubits required on a quantum computer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the Filtering Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the Quantum Approximate Optimization Algorithm (QAOA). We also demonstrate the experimental feasibility of our algorithms on a Honeywell trapped-ion quantum processor.

Related papers

• 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)
• 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)
• 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)
• Parallel circuit implementation of variational quantum algorithms [0.0]
  We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution. We apply this specifically to optimization problems, where inherent structures from the problem can be identified. We show that not only can our method address larger problems, but that it is also possible to run full VQA models while training parameters using only one slice.
  arXiv  Detail & Related papers  (2023-04-06T12:52:29Z)
• Squeezing and quantum approximate optimization [0.6562256987706128]
  Variational quantum algorithms offer fascinating prospects for the solution of optimization problems using digital quantum computers. However, the achievable performance in such algorithms and the role of quantum correlations therein remain unclear. We show numerically as well as on an IBM quantum chip how highly squeezed states are generated in a systematic procedure.
  arXiv  Detail & Related papers  (2022-05-20T18:00:06Z)
• Efficient Use of Quantum Linear System Algorithms in Interior Point Methods for Linear Optimization [0.0]
  We develop an Inexact Infeasible Quantum Interior Point Method to solve linear optimization problems. We also discuss how can we get an exact solution by Iterative Refinement without excessive time of quantum solvers.
  arXiv  Detail & Related papers  (2022-05-02T21:30:56Z)
• Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm [7.581898299650999]
  We introduce a simple yet efficient algorithm named Quantum Qubit Rotation Algorithm (QQRA) The approximate solution of the max-cut problem can be obtained with probability close to 1. We compare it with the well known quantum approximate optimization algorithm and the classical Goemans-Williamson algorithm.
  arXiv  Detail & Related papers  (2021-10-15T11:19:48Z)
• 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)
• 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)
• Quantum mean value approximator for hard integer value problems [19.4417702222583]
  We show that an optimization can be improved substantially by using an approximation rather than the exact expectation. Together with efficient classical sampling algorithms, a quantum algorithm with minimal gate count can thus improve the efficiency of general integer-value problems.
  arXiv  Detail & Related papers  (2021-05-27T13:03:52Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.