Quafu-RL: The Cloud Quantum Computers based Quantum Reinforcement Learning

• URL: http://arxiv.org/abs/2305.17966v2
• Date: Tue, 5 Mar 2024 10:20:41 GMT
• Title: Quafu-RL: The Cloud Quantum Computers based Quantum Reinforcement Learning
• Authors: BAQIS Quafu Group
• Abstract summary: In this work, we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on BAQIS Quafu quantum computing cloud. The experimental results demonstrate that the Reinforcement Learning (RL) agents are capable of achieving goals that are slightly relaxed both during the training and inference stages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advent of quantum computing, hybrid quantum-classical machine learning has shown promising computational advantages in many key fields. Quantum reinforcement learning, as one of the most challenging tasks, has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts. However, despite the progress of quantum processors and the emergence of quantum computing clouds in the noisy intermediate-scale quantum (NISQ) era, algorithms based on parameterized quantum circuits (PQCs) are rarely conducted on NISQ devices. In this work, we take the first step towards executing benchmark quantum reinforcement problems on various real devices equipped with at most 136 qubits on BAQIS Quafu quantum computing cloud. The experimental results demonstrate that the Reinforcement Learning (RL) agents are capable of achieving goals that are slightly relaxed both during the training and inference stages. Moreover, we meticulously design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to Quafu. We hope that the Quafu-RL be a guiding example to show how to realize machine learning task by taking advantage of quantum computers on the quantum cloud platform.

Related papers

• Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study [11.29246196323319]
  We describe NASA's work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware and with simulations. This work also includes physics-inspired classical algorithms that can be used at application scale today.
  arXiv  Detail & Related papers  (2024-06-21T19:05:42Z)
• Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators [0.0]
  Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model. We propose quantum machine learning with Kerrnon Parametric Hilberts (KPOs) as another promising quantum computing device.
  arXiv  Detail & Related papers  (2023-05-01T07:01:45Z)
• 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)
• 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)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• 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)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• 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)
• Reinforcement Learning with Quantum Variational Circuits [0.0]
  This work explores the potential for quantum computing to facilitate reinforcement learning problems. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. Results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space.
  arXiv  Detail & Related papers  (2020-08-15T00:13:01Z)

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.