Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms

• URL: http://arxiv.org/abs/2405.04499v1
• Date: Tue, 7 May 2024 17:15:58 GMT
• Title: Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms
• Authors: Shuwen Kan, Miguel Palma, Zefan Du, Samuel A Stein, Chenxu Liu, Juntao Chen, Ang Li, Ying Mao,
• Abstract summary: There has been a growing interest in qumodes due to advancements in the field and their potential applications. This paper aims to bridge this gap by providing performance benchmarks of various parameters used in state preparation with Variational Quantum Algorithms.
• Score: 10.941053143198092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum state preparation involves preparing a target state from an initial system, a process integral to applications such as quantum machine learning and solving systems of linear equations. Recently, there has been a growing interest in qumodes due to advancements in the field and their potential applications. However there is a notable gap in the literature specifically addressing this area. This paper aims to bridge this gap by providing performance benchmarks of various optimizers used in state preparation with Variational Quantum Algorithms. We conducted extensive testing across multiple scenarios, including different target states, both ideal and sampling simulations, and varying numbers of basis gate layers. Our evaluations offer insights into the complexity of learning each type of target state and demonstrate that some optimizers perform better than others in this context. Notably, the Powell optimizer was found to be exceptionally robust against sampling errors, making it a preferred choice in scenarios prone to such inaccuracies. Additionally, the Simultaneous Perturbation Stochastic Approximation optimizer was distinguished for its efficiency and ability to handle increased parameter dimensionality effectively.

Related papers

• A coherent approach to quantum-classical optimization [0.0]
  Hybrid quantum-classical optimization techniques have been shown to allow for the reduction of quantum computational resources. We identify the coherence entropy as a crucial metric in determining the suitability of quantum states. We propose a quantum-classical optimization protocol that significantly improves on previous approaches for such tasks.
  arXiv  Detail & Related papers  (2024-09-20T22:22:53Z)
• Bayesian optimization for state engineering of quantum gases [1.6144007597015433]
  State engineering of quantum objects is a central requirement in most implementations. We propose Bayesian optimization based on multi-output Gaussian processes to learn the quantum state's physical properties from few simulations only. This paves the way to efficient state engineering of complex quantum systems.
  arXiv  Detail & Related papers  (2024-04-28T16:24:28Z)
• 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)
• 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)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Quantum algorithm for robust optimization via stochastic-gradient online learning [0.0]
  We consider the online robust optimization meta-algorithm by Ben-Tal et al. and show that for a large range of subgradients, this algorithm has the same guarantee as the original non-stochastic version. We develop a quantum version of this algorithm and show that an at most quadratic improvement in terms of the dimension can be achieved.
  arXiv  Detail & Related papers  (2023-04-05T07:25:07Z)
• Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
  Bilevel optimization is a tool for many machine learning problems. We propose a novel stoc-efficientgradient estimator named stoc-BiO.
  arXiv  Detail & Related papers  (2020-10-15T18:09:48Z)
• 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)
• Using models to improve optimizers for variational quantum algorithms [1.7475326826331605]
  Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. We introduce two optimization methods and numerically compare their performance with common methods in use today.
  arXiv  Detail & Related papers  (2020-05-22T05:23:23Z)
• 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.