Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization

• URL: http://arxiv.org/abs/2504.11109v2
• Date: Mon, 01 Sep 2025 14:13:59 GMT
• Title: Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
• Authors: Linus Jern, Valter Uotila, Cong Yu, Bo Zhao,
• Abstract summary: Large language models (LLMs) have achieved remarkable outcomes in complex problems, including math, coding, and analyzing scientific reports.<n>We describe Agent-Q, an LLM fine-tuning system to generate and optimize quantum circuits.
• Score: 5.2374261493530065
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) have achieved remarkable outcomes in complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet, few works have explored the potential of LLMs in quantum computing. The most challenging problem is to leverage LLMs to automatically generate quantum circuits at a large scale. Fundamentally, the existing pre-trained LLMs lack the knowledge of quantum circuits. In this paper, we address this challenge by fine-tuning LLMs and injecting the domain-specific knowledge of quantum computing. We describe Agent-Q, an LLM fine-tuning system to generate and optimize quantum circuits. In particular, Agent-Q implements the mechanisms to generate training data sets and constructs an end-to-end pipeline to fine-tune pre-trained LLMs to generate parameterized quantum circuits for various optimization problems. Agent-Q provides 14,000 quantum circuits covering a large spectrum of the quantum optimization landscape: 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. Based thereon, Agent-Q fine-tunes LLMs and constructs syntactically correct parametrized quantum circuits in OpenQASM 3.0. We have evaluated the quality of the LLM-generated circuits and parameters by comparing them to the optimized expectation values and distributions. Experimental results show superior performance of Agent-Q, compared to several state-of-the-art LLMs and better parameters than random. Agent-Q can be integrated into an agentic workflow, and the generated parametrized circuits with initial parameters can be used as a starting point for further optimization, e.g., as templates in quantum machine learning and as benchmarks for compilers and hardware.

Related papers

• 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)
• QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL [8.823588193058727]
  Large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution.<n>We propose QUASAR, an agentic reinforcement learning framework for quantum circuits generation and optimization.
  arXiv  Detail & Related papers  (2025-10-01T14:40:04Z)
• QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming [8.73473101831257]
  We present QAgent, a multi-agent system that fully automates OpenQASM programming.<n>Our evaluations demonstrate substantial improvements across multiple LLMs of varying sizes.<n>We envision this multi-agent system as a key enabler for democratizing quantum programming, bridging expertise gaps, and accelerating the practical adoption of quantum computing.
  arXiv  Detail & Related papers  (2025-08-26T18:40:02Z)
• Circuit Partitioning Using Large Language Models for Quantum Compilation and Simulations [6.180118431476265]
  Quantum computers are limited by noisy gates, some of which are more error-prone than others and can render the final incomprehensible.<n>Quantum circuit compilation algorithms attempt to minimize these noisy gates when mapping quantum algorithms onto quantum hardware.<n>Large language models (LLMs) have the potential to change this and help improve quantum circuit partitions.
  arXiv  Detail & Related papers  (2025-05-12T16:18:48Z)
• Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach [0.7560883489000579]
  We study whether pretrained QAOA parameters of MaxCut can be used as is or to warm start the Maximum Independent Set (MIS) circuits. Our experimental results show that such parameter transfer can significantly reduce the number of optimization iterations required.
  arXiv  Detail & Related papers  (2025-04-14T21:56:11Z)
• Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms [4.525216077859531]
  We introduce a new framework for optimizing parameterized quantum circuits (PQCs) that employs a classical, inspired by Model-Agnostic Meta-Learning (MAML) technique.<n>Our framework features a classical neural network, called Learner, which interacts with a PQC using the output of Learner as an initial parameter.<n>In the adaptation phase, the framework requires only a few PQC updates to converge to a more accurate value, while the learner remains unchanged.
  arXiv  Detail & Related papers  (2025-01-10T12:07:00Z)
• Learning to Measure Quantum Neural Networks [10.617463958884528]
  We introduce a novel approach that makes the observable of the quantum system-specifically, the Hermitian matrix-learnable.<n>Our method features an end-to-end differentiable learning framework, where the parameterized observable is trained alongside the ordinary quantum circuit parameters.<n>Using numerical simulations, we show that the proposed method can identify observables for variational quantum circuits that lead to improved outcomes.
  arXiv  Detail & Related papers  (2025-01-10T02:28:19Z)
• Quantum Optimization for Optimal Power Flow: CVQLS-Augmented Interior Point Method [0.0]
  This paper presents a quantum-enhanced optimization approach for solving optimal power flow (OPF)<n>We integrate the interior point method (IPM) with a coherent variational quantum linear solver (CVQLS)<n>We evaluate our approaches on multiple systems to show their effectiveness in providing reliable OPF solutions.
  arXiv  Detail & Related papers  (2024-12-18T17:43:51Z)
• 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)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• 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 Circuit Unoptimization [0.6449786007855248]
  We construct a quantum algorithmic primitive called quantum circuit unoptimization. It makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence. We use quantum circuit unoptimization to generate compiler benchmarks and evaluate circuit optimization performance.
  arXiv  Detail & Related papers  (2023-11-07T08:38:18Z)
• Pre-optimizing variational quantum eigensolvers with tensor networks [1.4512477254432858]
  We present and benchmark an approach where we find good starting parameters for parameterized quantum circuits by simulating VQE. We apply this approach to the 1D and 2D Fermi-Hubbard model with system sizes that use up to 32 qubits. In 2D, the parameters that VTNE finds have significantly lower energy than their starting configurations, and we show that starting VQE from these parameters requires non-trivially fewer operations to come down to a given energy.
  arXiv  Detail & Related papers  (2023-10-19T17:57:58Z)
• Parallel circuit implementation of variational quantum algorithms [0.0]
  We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution. We apply this specifically to optimization problems, where inherent structures from the problem can be identified. We show that not only can our method address larger problems, but that it is also possible to run full VQA models while training parameters using only one slice.
  arXiv  Detail & Related papers  (2023-04-06T12:52:29Z)
• 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)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• 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)
• FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits [105.54048699217668]
  We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters. We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
  arXiv  Detail & Related papers  (2021-03-15T17:38:33Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• 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.