Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization

• URL: http://arxiv.org/abs/2412.19150v2
• Date: Fri, 21 Feb 2025 11:37:11 GMT
• Title: Scaling the Variational Quantum Eigensolver for Dynamic Portfolio Optimization
• Abstract summary: This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier.<n>We analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and on a real Quantum Processing Unit.
• Score: 0.24776665860114822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier. We systematically analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and optimizer on a real Quantum Processing Unit. We achieve best results by using a combination of a Differential Evolution classical optimizer and an ansatz circuit tailored to both the problem and the properties of the Quantum Processing Unit.

Related papers

• Multiclass Portfolio Optimization via Variational Quantum Eigensolver with Dicke State Ansatz [0.0]
  We introduce a novel quantum framework for portfolio optimization that explicitly incorporates diversification.<n>A key strength of this ansatz is that it initializes the quantum system in a superposition of only feasible states.<n>Our findings demonstrate that, when combined with the CMA-ES, the Dicke state ansatz achieves superior performance in terms of convergence rate, approximation ratio, and measurement probability.
  arXiv  Detail & Related papers  (2025-08-19T15:45:07Z)
• 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)
• Variational Quantum Multi-Objective Optimization [5.381539115778766]
  We present a variational quantum optimization algorithm to solve discrete multi-objective optimization problems on quantum computers. We show the effectiveness of the proposed algorithm on several benchmark problems with up to five objectives.
  arXiv  Detail & Related papers  (2023-12-21T18:59:21Z)
• 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)
• Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
  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.
  arXiv  Detail & Related papers  (2022-09-05T07:23:03Z)
• Lyapunov control-inspired strategies for quantum combinatorial optimization [0.0]
  We provide an expanded description of Lyapunov control-inspired strategies for quantum optimization. Instead, these strategies utilize feedback from qubit measurements to assign values to the quantum circuit parameters in a deterministic manner.
  arXiv  Detail & Related papers  (2021-08-12T19:47:59Z)
• 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)
• 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)
• Approaches to Constrained Quantum Approximate Optimization [0.4588028371034407]
  We study the costs and benefits of different quantum approaches to finding approximate solutions of constrained optimization problems. A new algorithm based on a "Dynamic Quantum Variational Ansatz" (DQVA) is proposed.
  arXiv  Detail & Related papers  (2020-10-13T19:51:12Z)
• 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)
• 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.