mlsanityData to 14 Oct 2026

Approximate Quantum State Preparation Through Proximal Policy Optimization

arXiv 2607.21121 ↗First posted 23 Jul 2026quant-ph cs.ET cs.LGcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since the search space grows exponentially with the number of qubits, making the identification of the optimal circuit non-trivial. To address this problem, deep reinforcement learning is employed through an agent based on proximal policy optimization. The objective of the agent is to identify the best possible approximation of the target state while simultaneously minimizing the number of gates used. At each step, the agent appends a new gate to the circuit and recomputes the fidelity between the approximated state and the target states. Various experiments have been performed from 2 to 5 qubits. Both predefined states, such as Bell, GHZ, W, and Dicke states, and completely random states are considered. The proposed framework is able to achieve approximation errors of 10-14.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 31 references that are in the map. First posted on arXiv 23 Jul 2026.

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12 of 31 references.

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  1. 2024Reinforcement learning-based architecture search for quantum machine learning4 Jun

Other references · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Parameterized quantum circuits as machine learning modelscited by 332
  3. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
  4. 2023Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracycited by 39
  5. 2024Quantum Architecture Search: A Surveycited by 32
  6. 2023Challenges for Reinforcement Learning in Quantum Circuit Designcited by 9
  7. 2025Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesiscited by 4
  8. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generationcited by 4
  9. 2025Quantum State Preparation with the QNN-based SRBB Algorithmcited by 2
  10. 2024A Novel Single-Layer Quantum Neural Network for Approximate SRBB-Based Unitary Synthesiscited by 1
  11. 2025Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networkscited by 1

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  1. 2026Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study
  2. 2024A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
  3. 2026Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
  4. 2024Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations
  5. 2026Reinforcement Learning for Quantum Technology
  6. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  7. 2026One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
  8. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning