Quantum policy gradient algorithms

• URL: http://arxiv.org/abs/2212.09328v1
• Date: Mon, 19 Dec 2022 09:45:58 GMT
• Title: Quantum policy gradient algorithms
• Authors: Sofiene Jerbi, Arjan Cornelissen, M\=aris Ozols, Vedran Dunjko
• Abstract summary: We show that speed-ups in learning are possible when given quantum access to reinforcement learning environments. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies. We find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved.
• Score: 1.5293427903448025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments. Yet, the applicability of quantum algorithms in this setting remains very limited, notably in environments with large state and action spaces. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies by exploiting quantum interactions with an environment. However, these algorithms only offer full quadratic speed-ups in sample complexity over their classical analogs when the trained policies satisfy some regularity conditions. Interestingly, we find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved with respect to these conditions, which showcases the benefit of a fully-quantum reinforcement learning framework.

Related papers

• Quantum Algorithms and Applications for Open Quantum Systems [1.7717834336854132]
  We provide a succinct summary of the fundamental theory of open quantum systems. We then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.
  arXiv  Detail & Related papers  (2024-06-07T19:02:22Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Quantum computational intelligence for traveltime seismic inversion [0.0]
  We implement an approach for traveltime seismic inversion through a near-term quantum algorithm based on gradient-free quantum circuit learning. We demonstrate that a quantum computer with thousands of qubits, even if noisy, can solve geophysical problems.
  arXiv  Detail & Related papers  (2022-08-11T12:36:58Z)
• Quantum Reinforcement Learning via Policy Iteration [6.961253535504979]
  We provide a general framework for performing quantum reinforcement learning via policy iteration. We validate our framework by designing and analyzing: emphquantum policy evaluation methods for infinite horizon discounted problems. We study the theoretical and experimental performance of our quantum algorithms on two environments from OpenAI's Gym.
  arXiv  Detail & Related papers  (2022-03-03T18:08:17Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Variational quantum policies for reinforcement learning [0.0]
  Variational quantum circuits have recently gained popularity as quantum machine learning models. In this work, we investigate how to construct and train reinforcement learning policies based on variational quantum circuits.
  arXiv  Detail & Related papers  (2021-03-09T17:33:09Z)
• How to enhance quantum generative adversarial learning of noisy information [5.8010446129208155]
  We show how different training problems may occur during the optimization process. We propose new strategies to achieve a faster convergence in any operating regime. Our results pave the way for new experimental demonstrations of such hybrid classical-quantum protocols.
  arXiv  Detail & Related papers  (2020-12-10T21:48:26Z)
• Statistical Limits of Supervised Quantum Learning [90.0289160657379]
  We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms for supervised learning cannot achieve polylogarithmic runtimes in the input dimension. We conclude that, when no further assumptions on the problem are made, quantum machine learning algorithms for supervised learning can have at most speedups over efficient classical algorithms.
  arXiv  Detail & Related papers  (2020-01-28T17:35:32Z)

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.