Efficient quantum recurrent reinforcement learning via quantum reservoir computing

• URL: http://arxiv.org/abs/2309.07339v1
• Date: Wed, 13 Sep 2023 22:18:38 GMT
• Title: Efficient quantum recurrent reinforcement learning via quantum reservoir computing
• Authors: Samuel Yen-Chi Chen
• Abstract summary: Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks. This work presents a novel approach to address this challenge by constructing QRL agents utilizing QRNN-based quantum long short-term memory (QLSTM)
• Score: 3.6881738506505988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages. A notable development is through quantum recurrent neural networks (QRNNs) for memory-intensive tasks such as partially observable environments. However, QRL models incorporating QRNN encounter challenges such as inefficient training of QRL with QRNN, given that the computation of gradients in QRNN is both computationally expensive and time-consuming. This work presents a novel approach to address this challenge by constructing QRL agents utilizing QRNN-based reservoirs, specifically employing quantum long short-term memory (QLSTM). QLSTM parameters are randomly initialized and fixed without training. The model is trained using the asynchronous advantage actor-aritic (A3C) algorithm. Through numerical simulations, we validate the efficacy of our QLSTM-Reservoir RL framework. Its performance is assessed on standard benchmarks, demonstrating comparable results to a fully trained QLSTM RL model with identical architecture and training settings.

Related papers

• Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning [3.6881738506505988]
  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.
  arXiv  Detail & Related papers  (2024-07-25T17:11:00Z)
• Learning to Program Variational Quantum Circuits with Fast Weights [3.6881738506505988]
  This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge. The proposed QFWP model achieves learning of temporal dependencies without necessitating the use of quantum recurrent neural networks. Numerical simulations conducted in this study showcase the efficacy of the proposed QFWP model in both time-series prediction and RL tasks.
  arXiv  Detail & Related papers  (2024-02-27T18:53:18Z)
• Federated Quantum Long Short-term Memory (FedQLSTM) [58.50321380769256]
  Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models. No prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions. A novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed.
  arXiv  Detail & Related papers  (2023-12-21T21:40:47Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• 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)
• Reservoir Computing via Quantum Recurrent Neural Networks [0.5999777817331317]
  Existing VQC or QNN-based methods require significant computational resources to perform gradient-based optimization of quantum circuit parameters. In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) Our numerical simulations show that the QRNN-RC can reach results comparable to fully trained QRNN models for several function approximation and time series tasks.
  arXiv  Detail & Related papers  (2022-11-04T17:30:46Z)
• Quantum deep recurrent reinforcement learning [0.8702432681310399]
  Reinforcement learning (RL) is one of the machine learning (ML) paradigms which can be used to solve complex sequential decision making problems. We build a quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep $Q$-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN.
  arXiv  Detail & Related papers  (2022-10-26T17:29:19Z)
• Accelerating the training of single-layer binary neural networks using the HHL quantum algorithm [58.720142291102135]
  We show that useful information can be extracted from the quantum-mechanical implementation of Harrow-Hassidim-Lloyd (HHL) This paper shows, however, that useful information can be extracted from the quantum-mechanical implementation of HHL, and used to reduce the complexity of finding the solution on the classical side.
  arXiv  Detail & Related papers  (2022-10-23T11:58:05Z)
• 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)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.