VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

• URL: http://arxiv.org/abs/2301.03251v1
• Date: Mon, 9 Jan 2023 10:31:18 GMT
• Title: VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
• Authors: Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei Li, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo
• Abstract summary: VQNet 2.0 is a new generation of unified classical and quantum machine learning framework. The core library of the framework is implemented in C++, and the user level is implemented in Python.
• Score: 82.82331453802182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.

Related papers

• Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators [0.5224038339798622]
  We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning.<n>Qiskit ML started as a proof-of-concept code in 2019 and has since been developed to be a modular, intuitive tool for non-specialist users.
  arXiv  Detail & Related papers  (2025-05-23T11:27:03Z)
• CONQURE: A Co-Execution Environment for Quantum and Classical Resources [0.0]
  This work proposes CONQURE, a co-execution environment for quantum and classical resources.<n>We show our API has a low overhead averaging 12.7m tests, and we demonstrate functionality on an ion-trap device.<n>Our OpenMP extension enables the parallelization of VQE runs with a 3.1X reduction in runtime.
  arXiv  Detail & Related papers  (2025-05-04T20:38:50Z)
• 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)
• sQUlearn -- A Python Library for Quantum Machine Learning [0.0]
  sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML) The library's dual-layer architecture serves both QML researchers and practitioners.
  arXiv  Detail & Related papers  (2023-11-15T14:22:53Z)
• Foundations of Quantum Federated Learning Over Classical and Quantum Networks [59.121263013213756]
  Quantum federated learning (QFL) is a novel framework that integrates the advantages of classical federated learning (FL) with the computational power of quantum technologies. QFL can be deployed over both classical and quantum communication networks.
  arXiv  Detail & Related papers  (2023-10-23T02:56:00Z)
• 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)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Quantum Neural Network Compression [23.206283554472787]
  We show that there exist differences between the compression of quantum and classical neural networks. We propose the very first systematical framework, namely CompVQC, to compress quantum neural networks.
  arXiv  Detail & Related papers  (2022-07-04T16:57:46Z)
• NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems [1.0486135378491268]
  NetKet is a machine learning toolbox for many-body quantum physics. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework. The most significant new feature is the possibility to define arbitrary neural network ans"atze in pure Python code.
  arXiv  Detail & Related papers  (2021-12-20T13:41:46Z)
• Accelerating variational quantum algorithms with multiple quantum processors [78.36566711543476]
  Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages. Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
  arXiv  Detail & Related papers  (2021-06-24T08:18:42Z)
• QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity [13.152233840194473]
  We propose a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification. Powered by a quantum differentiation function along with a hybrid quantum-classic design, QuClassi encodes the data with a reduced number of qubits and generates the quantum circuit, pushing it to the quantum platform for the best states.
  arXiv  Detail & Related papers  (2021-03-21T05:28:37Z)
• A quantum-classical cloud platform optimized for variational hybrid algorithms [0.0]
  This work enumerates the architectural requirements of a quantum-classical cloud platform. We present a framework for benchmarking its runtime performance. We show that integrating these two features into the Rigetti Quantum Cloud Services (QCS) platform results in considerable improvements to the latencies that govern algorithm runtime.
  arXiv  Detail & Related papers  (2020-01-13T18:37:01Z)

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.