FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design

• URL: http://arxiv.org/abs/2510.26688v1
• Date: Thu, 30 Oct 2025 16:57:13 GMT
• Title: FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
• Authors: Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau,
• Abstract summary: 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.
• Score: 8.70817825961863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, where circuit efficiency and resilience to errors are paramount. The search space of gate sequences grows combinatorially, and handcrafted templates often waste scarce qubit and depth budgets. We introduce \textsc{FlowQ-Net} (Flow-based Quantum design Network), a generative framework for automated quantum circuit synthesis based on Generative Flow Networks (GFlowNets). This framework learns a stochastic policy to construct circuits sequentially, sampling them in proportion to a flexible, user-defined reward function that can encode multiple design objectives such as performance, depth, and gate count. This approach uniquely enables the generation of a diverse ensemble of high-quality circuits, moving beyond single-solution optimization. We demonstrate the efficacy of \textsc{FlowQ-Net} through an extensive set of simulations. We apply our method to Variational Quantum Algorithm (VQA) ansatz design for molecular ground state estimation, Max-Cut, and image classification, key challenges in near-term quantum computing. Circuits designed by \textsc{FlowQ-Net} achieve significant improvements, yielding circuits that are 10$\times$-30$\times$ more compact in terms of parameters, gates, and depth compared to commonly used unitary baselines, without compromising accuracy. This trend holds even when subjected to error profiles from real-world quantum devices. Our results underline the potential of generative models as a general-purpose methodology for automated quantum circuit design, offering a promising path towards more efficient quantum algorithms and accelerating scientific discovery in the quantum domain.

Related papers

• Investigating Quantum Circuit Designs Using Neuro-Evolution [2.9631016562930537]
  We propose an evolutionary approach to the automated design and training of quantum circuits.<n>The proposed method searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints.<n>Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy.
  arXiv  Detail & Related papers  (2026-02-03T18:57:39Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation [4.358861563008207]
  Quantum neural networks (QNNs) have shown promise both empirically and theoretically.<n> Hardware imperfections and limited access to quantum devices pose practical challenges.<n>We propose an automated solution using differentiable optimization.
  arXiv  Detail & Related papers  (2025-05-13T19:01:08Z)
• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits.<n>Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• RH: An Architecture for Redesigning Quantum Circuits on Quantum Hardware Devices [6.959884576408311]
  We present an architecture that enables the redesign of large-scale quantum circuits on quantum hardware.<n>By prepending a random quantum circuit module to the standard EQ-GAN framework, we extend its capability from quantum state learning to unitary transformation learning.
  arXiv  Detail & Related papers  (2024-12-30T12:05:09Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Quantum Deep Dreaming: A Novel Approach for Quantum Circuit Design [0.0]
  Quantum Deep Dreaming (QDD) is an algorithm that generates optimal quantum circuit architectures for specified objectives. We demonstrate that QDD successfully generates, or 'dreams', circuits of six qubits close to ground state energy.
  arXiv  Detail & Related papers  (2022-11-05T22:16:10Z)
• 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 circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)
• Differentiable Quantum Architecture Search [15.045985536395479]
  We propose a general framework of differentiable quantum architecture search (DQAS) DQAS enables automated designs of quantum circuits in an end-to-end differentiable fashion.
  arXiv  Detail & Related papers  (2020-10-16T18:00:03Z)

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.