Blockwise Optimization for Projective Variational Quantum Dynamics (BLOP-VQD): Algorithm and Implementation for Lattice Systems

• URL: http://arxiv.org/abs/2503.18279v1
• Date: Mon, 24 Mar 2025 01:48:37 GMT
• Title: Blockwise Optimization for Projective Variational Quantum Dynamics (BLOP-VQD): Algorithm and Implementation for Lattice Systems
• Authors: Harshdeep Singh, Sonjoy Majumder, Sabyashachi Mishra,
• Abstract summary: We present an efficient approach to simulate real-time quantum dynamics using Projected Variational Quantum Dynamics.<n>Our method selectively optimize one block at a time while keeping the others fixed, allowing for significant reductions in computational overhead.<n>We demonstrate the performance of the proposed methods in a series of spin-lattice models with varying sizes and complexity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an efficient approach to simulate real-time quantum dynamics using Projected Variational Quantum Dynamics (PVQD), where the computational cost is reduced by strategically optimizing only a subset of the variational parameters at each time step. Typically, the variational ansatz consists of repeated blocks of parameterized quantum circuits, where all parameters are updated in a standard optimization procedure. In contrast, our method selectively optimizes one block at a time while keeping the others fixed, allowing for significant reductions in computational overhead. This semi-global optimization strategy ensures that all qubits are still involved in the evolution, but the optimization is localized to specific blocks, thus avoiding the need to update all parameters simultaneously. We propose different approaches for choosing the next block for optimization, including sequential, random, and fidelity-based updation. We demonstrate the performance of the proposed methods in a series of spin-lattice models with varying sizes and complexity. Our method preserves the accuracy of the time evolution with a much lower computational cost. This new optimization strategy provides a promising path toward high-fidelity simulation of the time evolution of complex quantum systems with reduced computational resources.

Related papers

• Optimizing a parameterized controlled gate with Free Quaternion Selection [0.4353365283165517]
  In this study, we propose an algorithm to estimate the optimal parameters for locally minimizing the cost value of a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Eigensolver (VQE) for Ising and molecular Hamiltonians.
  arXiv  Detail & Related papers  (2024-09-20T14:46:00Z)
• 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)
• Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms [0.0]
  We propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. We implement a quasi-Newton algorithm where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA.
  arXiv  Detail & Related papers  (2024-01-10T14:08:04Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits [7.08228773002332]
  We propose a new variational quantum algorithm called MCTS-QAOA. It combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit. We find that MCTS-QAOA has excellent noise-resilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.
  arXiv  Detail & Related papers  (2022-03-30T23:15:21Z)
• An efficient quantum algorithm for the time evolution of parameterized circuits [0.0]
  We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems. We show that our approach is particularly advantageous over existing global optimization algorithms.
  arXiv  Detail & Related papers  (2021-01-12T16:21:08Z)
• 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)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)
• Global Optimization of Gaussian processes [52.77024349608834]
  We propose a reduced-space formulation with trained Gaussian processes trained on few data points. The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding. In total, we reduce time convergence by orders of orders of the proposed method.
  arXiv  Detail & Related papers  (2020-05-21T20:59:11Z)
• 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)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

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.