Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models

• URL: http://arxiv.org/abs/2503.12884v1
• Date: Mon, 17 Mar 2025 07:29:05 GMT
• Title: Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models
• Authors: Kento Ueda, Atsushi Matsuo,
• Abstract summary: We present a novel approach for improving the design of ansatzes in Quantum Generative Adversarial Networks (qGANs) by leveraging Large Language Models (LLMs)<n>Our approach iteratively refines ansatz structures to improve accuracy while reducing circuit depth and the number of parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach for improving the design of ansatzes in Quantum Generative Adversarial Networks (qGANs) by leveraging Large Language Models (LLMs). By combining the strengths of LLMs with qGANs, our approach iteratively refines ansatz structures to improve accuracy while reducing circuit depth and the number of parameters. This study paves the way for further exploration in AI-driven quantum algorithm design. The flexibility of our proposed workflow extends to other quantum variational algorithms, providing a general framework for optimizing quantum circuits in a variety of quantum computing tasks.

Related papers

• FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design [8.70817825961863]
  We introduce textscFlowQ-Net (Flow-based Quantum design Network), a generative framework for automated quantum circuit synthesis.<n>This framework learns a policy to construct circuits sequentially, sampling them in to a flexible user-defined reward function.<n>We demonstrate the efficacy of textscFlowQ-Net through an extensive set of simulations.
  arXiv  Detail & Related papers  (2025-10-30T16:57:13Z)
• Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance [0.0]
  Variational quantum algorithms have emerged as a leading paradigm that extracts practical computation from near-term intermediate-scale quantum devices.<n>To be effective, ansatze must be expressive enough to capture target states but shallow enough to be trainable.<n>We propose a genetic algorithm-inspired framework for designing ansatze that achieve high expressibility while maintaining shallow depth and low parameter count.
  arXiv  Detail & Related papers  (2025-09-06T18:39:10Z)
• QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development [3.8425905067219492]
  QuanUML is an extension of the Unified Modeling Language (UML) tailored for quantum software systems.<n>It integrates quantum-specific constructs, such as qubits and quantum gates, into the Algorithm framework.<n>We demonstrate its utility in designing and visualizing quantum algorithms.
  arXiv  Detail & Related papers  (2025-06-05T05:19:22Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits. Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity [3.6881738506505988]
  The design of high-performance quantum machine learning (QML) models requires expert-level knowledge.<n>Key challenges include the design of data encoding mechanisms and parameterized quantum circuits.<n>We propose a novel method that encodes quantum circuit architecture information to enable the evolution of quantum circuit designs.
  arXiv  Detail & Related papers  (2024-12-17T02:40:35Z)
• Optimal Quantum Circuit Design via Unitary Neural Networks [0.0]
  We present an automated method for synthesizing the functionality of a quantum algorithm into a quantum circuit model representation. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm.
  arXiv  Detail & Related papers  (2024-08-23T16:41:15Z)
• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [59.07692103357675]
  This survey explores the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware.<n>It becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• 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)
• Quantum-Informed Recursive Optimization Algorithms [0.0]
  We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps. We use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware.
  arXiv  Detail & Related papers  (2023-08-25T18:02:06Z)
• Quantum-inspired optimization for wavelength assignment [51.55491037321065]
  We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
  arXiv  Detail & Related papers  (2022-11-01T07:52:47Z)
• DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks [54.03701670739067]
  We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
  arXiv  Detail & Related papers  (2022-09-16T02:44:52Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization [2.20200533591633]
  We propose a new quantum gates distance that characterizes the gates' action over every quantum state. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
  arXiv  Detail & Related papers  (2022-06-28T16:23:24Z)
• 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.