Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization

• URL: http://arxiv.org/abs/2209.01799v1
• Date: Mon, 5 Sep 2022 07:23:03 GMT
• Title: Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization
• Authors: Enrico Blanzieri, Davide Pastorello, Valter Cavecchia, Alexander Rumyantsev and Mariia Maltseva
• Abstract summary: We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper we introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model. The results of numerical evaluation of the algorithm on quantum hardware as well as on a classical semiconductor hardware model are also demonstrated.

Related papers

• Optimization-based Unfolding in High-Energy Physics [0.0]
  In High-Energy Physics, unfolding is the process of reconstructing true distributions of physical observables from detector-distorted measurements.<n>We develop a framework to tackle this problem using both classical and quantum-compatible methods.
  arXiv  Detail & Related papers  (2026-02-26T09:11:34Z)
• Hot-Starting Quantum Portfolio Optimization [39.916647837440316]
  Combinatorial optimization with a smooth and convex objective function arises naturally in applications such as discrete mean-variance portfolio optimization.<n>We introduce a novel approach that restricts the search space to discrete solutions in the vicinity of the continuous optimum by constructing a compact Hilbert space.<n> Experiments on software solvers and a D-Wave Advantage quantum annealer demonstrate that our method outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-10-13T08:47:43Z)
• Quantum Approximate Optimization Algorithm for MIMO with Quantized b-bit Beamforming [47.98440449939344]
  Multiple-input multiple-output (MIMO) is critical for 6G communication, offering improved spectral efficiency and reliability.<n>This paper explores the use of the Quantum Approximate Optimization Algorithm (QAOA) and alternating optimization to address the problem of b-bit quantized phase shifters both at the transmitter and the receiver.<n>We demonstrate that the structure of this quantized beamforming problem aligns naturally with hybrid-classical methods like QAOA, as the phase shifts used in beamforming can be directly mapped to rotation gates in a quantum circuit.
  arXiv  Detail & Related papers  (2025-10-07T17:53:02Z)
• An em algorithm for quantum Boltzmann machines [40.40469032705598]
  We develop a quantum version of the em algorithm for training quantum Boltzmann machines.<n>We implement the algorithm on a semi-quantum restricted Boltzmann machine, where quantum effects are confined to the hidden layer.
  arXiv  Detail & Related papers  (2025-07-29T07:59:22Z)
• Optimal Control by Variational Quantum Algorithms [0.0]
  We introduce a general metric termed control optimality, which accounts for constraints on both classical and quantum components.<n>We discuss the emergent gradient behavior and error robustness, demonstrating the feasibility of applying hybrid quantum algorithms to solve quantum optimal control problems.
  arXiv  Detail & Related papers  (2025-05-29T11:55:37Z)
• Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement [35.436358464279785]
  This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning.
  arXiv  Detail & Related papers  (2024-11-24T20:14:57Z)
• Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
  We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO) Our purely quantum approach eliminates the dependency on classical variational quantum algorithms. It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2024-05-22T18:11:42Z)
• 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)
• Quantum Advantage Actor-Critic for Reinforcement Learning [5.579028648465784]
  We propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits. We empirically test multiple quantum Advantage Actor-Critic configurations with the well known Cart Pole environment to evaluate our approach in control tasks with continuous state spaces.
  arXiv  Detail & Related papers  (2024-01-13T11:08:45Z)
• Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes [32.07657827173262]
  We introduce an innovative quantum framework for the agent's engagement with an unknown MDP. We show that the quantum advantage in mean estimation leads to exponential advancements in regret guarantees for infinite horizon Reinforcement Learning.
  arXiv  Detail & Related papers  (2023-10-18T03:17:51Z)
• Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization [41.94295877935867]
  We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2023-08-29T17:53:08Z)
• Photonic counterdiabatic quantum optimization algorithm [3.2174634059872154]
  We propose a hybrid quantum- approximate optimization algorithm for quantum computing that is tailored for continuous-variable problems. We conduct proof-of-principle experiments on an-photo quantum chip.
  arXiv  Detail & Related papers  (2023-07-27T13:33:33Z)
• Performance Bounds for Quantum Control [0.06999740786886534]
  Quantum feedback controllers often lack performance targets and optimality certificates. We combine quantum filtering theory and moment-sum-of-squares techniques to construct a hierarchy of convex optimization problems.
  arXiv  Detail & Related papers  (2023-04-06T20:48:51Z)
• Multi-disk clutch optimization using quantum annealing [34.82692226532414]
  We develop a new quantum algorithm to solve a problem with significant practical relevance in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector.
  arXiv  Detail & Related papers  (2022-08-11T16:34:51Z)
• 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)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Feedback-based quantum optimization [0.0]
  We introduce a feedback-based strategy for quantum optimization, where the results of qubit measurements are used to constructively assign values to quantum circuit parameters. We show that this procedure results in an estimate of the optimization problem solution that improves monotonically with the depth of the quantum circuit.
  arXiv  Detail & Related papers  (2021-03-15T18:01:03Z)

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.