An LLM System for Autonomous Variational Quantum Circuit Design

• URL: http://arxiv.org/abs/2606.13380v1
• Date: Thu, 11 Jun 2026 14:08:00 GMT
• Title: An LLM System for Autonomous Variational Quantum Circuit Design
• Abstract summary: We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs.<n>We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks: quantum feature map construction for quantum machine learning and ansatz generation for variational quantum eigensolver applications in quantum chemistry. In image classification benchmarks, the best generated feature map outperforms representative quantum feature maps and, when scaled to larger qubit counts, surpasses the classical radial basis function kernel. In molecular ground state estimation across seven molecules, the generated ansatz attains competitive accuracy with widely used chemically inspired and hardware-efficient constructions while satisfying the imposed scaling constraints. These results establish LLM driven agentic system as a viable paradigm for automated quantum circuit design and illustrate how AI systems can participate in iterative scientific optimization workflows across scientific domains.

Related papers

• Evaluating Verified Autonomy in Quantum Engineering [9.463290030138044]
  We introduce QIQCBench, a benchmark of $49$ expert-authored tasks spanning multiple layers including calibration and control, error correction and compilation, sensing and networking.<n>Across $17$ frontier agentic systems, QIQCBench reveals wide variation in verified performance.<n>These results expose a substantial gap between demonstrating capability and achieving reliable operation, and establish Quantum-Harbor as a foundation for measuring progress towards verified autonomy in quantum engineering.
  arXiv  Detail & Related papers  (2026-09-15T16:46:08Z)
• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• 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)
• Artificial intelligence for representing and characterizing quantum systems [49.29080693498154]
  Efficient characterization of large-scale quantum systems is a central challenge in quantum science.<n>Recent advances in artificial intelligence (AI) have emerged as a powerful tool to address this challenge.<n>This review discusses how each of these AI paradigms contributes to two core tasks in quantum systems characterization.
  arXiv  Detail & Related papers  (2025-09-05T08:41:24Z)
• Quantum Visual Fields with Neural Amplitude Encoding [70.86293548779774]
  We introduce a new type of Quantum Implicit Neural Representation (QINR) for 2D image and 3D geometric field learning.<n>QVF encodes classical data into quantum statevectors using neural amplitude encoding grounded in a learnable energy manifold.<n>Our ansatz follows a fully entangled design of learnable parametrised quantum circuits, with quantum (unitary) operations performed in the real Hilbert space.
  arXiv  Detail & Related papers  (2025-08-14T17:59:52Z)
• Automating quantum feature map design via large language models [0.8009842832476994]
  We propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models.<n> Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention.
  arXiv  Detail & Related papers  (2025-04-10T02:27:45Z)
• Learning agent-based approach to the characterization of open quantum systems [0.08496348835248901]
  We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism.<n>By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system.<n>We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors.
  arXiv  Detail & Related papers  (2025-01-09T16:25:17Z)
• QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
  Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning [1.0984331138780683]
  Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives.<n>In parallel, quantum machine learning (QML) is emerging with the ongoing improvement of quantum computing hardware combined with its increasing availability via cloud services.<n>QML enables quantum-enhanced ML in which quantum mechanics is exploited to facilitate ML tasks, typically in the form of quantum-classical hybrid algorithms that combine quantum and classical resources.
  arXiv  Detail & Related papers  (2023-01-22T15:17:12Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum Simulation [48.341084094844746]
  We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
  arXiv  Detail & Related papers  (2021-05-17T16:17:57Z)
• Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
  We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
  arXiv  Detail & Related papers  (2021-01-20T14:20:14Z)

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.