Automated Design of Structured Variational Quantum Circuits with Reinforcement Learning

• URL: http://arxiv.org/abs/2507.16001v1
• Date: Mon, 21 Jul 2025 18:40:59 GMT
• Title: Automated Design of Structured Variational Quantum Circuits with Reinforcement Learning
• Authors: Gloria Turati, Simone Foderà, Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi,
• Abstract summary: This work represents the synthesis of variational quantum circuits as a sequential decision-making problem.<n>We introduce two reinforcement learning-based methods, RLVQC Global and RLVQC Block, tailored to optimization problems.<n>Our results show that both RLVQC methods exhibit strong results with RLVQC Block consistently outperforming QAOA and generally surpassing RLVQC Global.
• Score: 10.136215038345012
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Variational Quantum Algorithms (VQAs) are among the most promising approaches for leveraging near-term quantum hardware, yet their effectiveness strongly depends on the design of the underlying circuit ansatz, which is typically constructed with heuristic methods. In this work, we represent the synthesis of variational quantum circuits as a sequential decision-making problem, where gates are added iteratively in order to optimize an objective function, and we introduce two reinforcement learning-based methods, RLVQC Global and RLVQC Block, tailored to combinatorial optimization problems. RLVQC Block creates ansatzes that generalize the Quantum Approximate Optimization Algorithm (QAOA), by discovering a two-qubits block that is applied to all the interacting qubit pairs. While RLVQC Global further generalizes the ansatz and adds gates unconstrained by the structure of the interacting qubits. Both methods adopt the Proximal Policy Optimization (PPO) algorithm and use empirical measurement outcomes as state observations to guide the agent. We evaluate the proposed methods on a broad set of QUBO instances derived from classical graph-based optimization problems. Our results show that both RLVQC methods exhibit strong results with RLVQC Block consistently outperforming QAOA and generally surpassing RLVQC Global. While RLVQC Block produces circuits with depth comparable to QAOA, the Global variant is instead able to find significantly shorter ones. These findings suggest that reinforcement learning methods can be an effective tool to discover new ansatz structures tailored for specific problems and that the most effective circuit design strategy lies between rigid predefined architectures and completely unconstrained ones, offering a favourable trade-off between structure and adaptability.

Related papers

• Path Matters: Industrial Data Meet Quantum Optimization [2.7883868459582737]
  Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware.<n>We benchmark a representative subset of these transformation paths using a real-world industrial production planning problem with industry data.<n>Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance.
  arXiv  Detail & Related papers  (2025-04-23T10:45:38Z)
• Improving Variational Quantum Circuit Optimization via Hybrid Algorithms and Random Axis Initialization [0.0]
  Variational quantum circuits (VQCs) are an essential tool in applying noisy intermediate-scale quantum computers to practical problems.<n>We enhance the performance of the Rotosolve method, a gradient-free optimization algorithm specifically designed for VQCs.<n>We construct hybrid algorithms that benefit from the rapid initial convergence of Rotosolve while leveraging the superior expressivity of FQS.
  arXiv  Detail & Related papers  (2025-03-26T17:10:19Z)
• qNBO: quasi-Newton Meets Bilevel Optimization [26.0555315825777]
  Bilevel optimization, addressing challenges in hierarchical learning tasks, has gained significant interest in machine learning.<n>We introduce a general framework to address these computational challenges in a coordinated manner.<n>Specifically, we leverage quasi-Newton algorithms to accelerate the resolution of the lower-level problem while efficiently approximating the inverse Hessian-vector product.
  arXiv  Detail & Related papers  (2025-02-03T05:36:45Z)
• A Quantum Genetic Algorithm Framework for the MaxCut Problem [49.59986385400411]
  The proposed method introduces a Quantum Genetic Algorithm (QGA) using a Grover-based evolutionary framework and divide-and-conquer principles.<n>On complete graphs, the proposed method consistently achieves the true optimal MaxCut values, outperforming the Semidefinite Programming (SDP) approach.<n>On ErdHos-R'enyi random graphs, the QGA demonstrates competitive performance, achieving median solutions within 92-96% of the SDP results.
  arXiv  Detail & Related papers  (2025-01-02T05:06:16Z)
• Untrained Filtering with Trained Focusing for Superior Quantum Architecture Search [14.288836269941207]
  Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. We decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. QUEST-A develops an evolutionary mechanism with knowledge accumulation and reuse to enhance multi-level knowledge transfer.
  arXiv  Detail & Related papers  (2024-10-31T01:57:14Z)
• Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation [3.9857517408503567]
  Quantum Approximate optimisation algorithm (qaoa) is a widely studied quantum-classical iterative for optimisation. We introduce a new algorithmic variant based on non-iterative computation that is instance-independent, but problem-specific. Our approach is based on proving a long-standing conjecture regarding quantum-independent structures in qaoa.
  arXiv  Detail & Related papers  (2024-08-12T21:02:58Z)
• 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)
• Curriculum reinforcement learning for quantum architecture search under hardware errors [1.583327010995414]
  This work introduces a curriculum-based reinforcement learning QAS (CRLQAS) designed to tackle challenges in VQA deployment. The algorithm incorporates (i) a 3D architecture encoding and restrictions on environment dynamics to explore the search space of possible circuits efficiently. To facilitate studies, we developed an optimized simulator for our algorithm, significantly improving computational efficiency in noisy quantum circuits.
  arXiv  Detail & Related papers  (2024-02-05T20:33:00Z)
• Quantum Architecture Search with Unsupervised Representation Learning [24.698519892763283]
  Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS)<n>QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)
  arXiv  Detail & Related papers  (2024-01-21T19:53:17Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• Learning Pseudo-Backdoors for Mixed Integer Programs [48.36587539004464]
  We propose a machine learning approach for solving Mixed Programs (MIP) by learning to prioritize a set of decision variables, which we call pseudo-backdoors, for branching that results in faster solution times. Our approach takes inspiration from the concept of strong backdoors, which corresponds to a small set of variables such that only branching on these variables yields an optimal integral solution and a proof of optimality. A key advantage of pseudo-backdoors over strong backdoors is that they are much amenable to data-driven identification or prediction.
  arXiv  Detail & Related papers  (2021-06-09T13:59:53Z)
• Quantum variational optimization: The role of entanglement and problem hardness [0.0]
  We study the role of entanglement, the structure of the variational quantum circuit, and the structure of the optimization problem. Our numerical results indicate an advantage in adapting the distribution of entangling gates to the problem's topology. We find evidence that applying conditional value at risk type cost functions improves the optimization, increasing the probability of overlap with the optimal solutions.
  arXiv  Detail & Related papers  (2021-03-26T14:06:54Z)
• 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)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

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.