Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning

• URL: http://arxiv.org/abs/2407.18202v1
• Date: Thu, 25 Jul 2024 17:11:00 GMT
• Title: Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
• Authors: Samuel Yen-Chi Chen,
• Abstract summary: We propose differentiable quantum architecture search (DiffQAS) to enable trainable circuit parameters and structure weights. We show that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures.
• Score: 3.6881738506505988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on variational quantum circuits (VQC). These advancements have proven successful in addressing sequential decision-making tasks. However, constructing effective QRL models demands significant expertise due to challenges in designing quantum circuit architectures, including data encoding and parameterized circuits, which profoundly influence model performance. In this paper, we propose addressing this challenge with differentiable quantum architecture search (DiffQAS), enabling trainable circuit parameters and structure weights using gradient-based optimization. Furthermore, we enhance training efficiency through asynchronous reinforcement learning (RL) methods facilitating parallel training. Through numerical simulations, we demonstrate that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures across considered environments, showcasing stability across diverse scenarios. This methodology offers a pathway for designing QRL models without extensive quantum knowledge, ensuring robust performance and fostering broader application of QRL.

Related papers

• 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)
• Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
  This thesis focuses on identifying functional quantum circuits in noisy quantum hardware. We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently. In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
  arXiv  Detail & Related papers  (2024-02-21T12:30:39Z)
• Classical-to-Quantum Transfer Learning Facilitates Machine Learning with Variational Quantum Circuit [62.55763504085508]
  We prove that a classical-to-quantum transfer learning architecture using a Variational Quantum Circuit (VQC) improves the representation and generalization (estimation error) capabilities of the VQC model. We show that the architecture of classical-to-quantum transfer learning leverages pre-trained classical generative AI models, making it easier to find the optimal parameters for the VQC in the training stage.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Asynchronous training of quantum reinforcement learning [0.8702432681310399]
  A leading method of building quantum RL agents relies on the variational quantum circuits (VQCs) In this paper, we approach this challenge through asynchronous training QRL agents. We demonstrate the results via numerical simulations that within the tasks considered, the asynchronous training of QRL agents can reach performance comparable to or superior.
  arXiv  Detail & Related papers  (2023-01-12T15:54:44Z)
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits [7.298440208725654]
  We introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models. We show that our method can significantly improve the performance of hybrid quantum-classical models.
  arXiv  Detail & Related papers  (2022-08-23T19:47:37Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• 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)
• Quantum Architecture Search via Continual Reinforcement Learning [0.0]
  This paper proposes a machine learning-based method to construct quantum circuit architectures. We present the Probabilistic Policy Reuse with deep Q-learning (PPR-DQL) framework to tackle this circuit design challenge.
  arXiv  Detail & Related papers  (2021-12-10T19:07:56Z)
• 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)
• 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.