Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs

• URL: http://arxiv.org/abs/2109.03806v1
• Date: Wed, 8 Sep 2021 17:47:54 GMT
• Title: Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
• Authors: Zhepeng Wang, Zhiding Liang, Shanglin Zhou, Caiwen Ding, Jinjun Xiong, Yiyu Shi, Weiwen Jiang
• Abstract summary: This paper makes the first attempt to mix quantum neuron designs to build quantum neural architectures. Existing quantum neuron designs may be quite different but complementary, such as neurons from variation quantum circuits (VQC) and QuantumFlow. We propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement.
• Score: 23.747282946165097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers are becoming possible. Along with this trend, there emerge quantum neural architectures based on different designs of quantum neurons. A fundamental question in quantum deep learning arises: what is the best quantum neural architecture? Inspired by the design of neural architectures for classical computing which typically employs multiple types of neurons, this paper makes the very first attempt to mix quantum neuron designs to build quantum neural architectures. We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variation quantum circuits (VQC) and Quantumflow. More specifically, VQC can apply real-valued weights but suffer from being extended to multiple layers, while QuantumFlow can build a multi-layer network efficiently, but is limited to use binary weights. To take their respective advantages, we propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement. We further investigate the design principles to mix quantum neurons, which can provide guidance for quantum neural architecture exploration in the future. Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90.62% of accuracy on the MNIST dataset, compared with 52.77% and 69.92% on the VQC and QuantumFlow, respectively.

Related papers

• What is my quantum computer good for? Quantum capability learning with physics-aware neural networks [0.0]
  We present a quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $sim50%$ reductions in mean absolute error on both experimental and simulated data.
  arXiv  Detail & Related papers  (2024-06-09T04:11:41Z)
• 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 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)
• Parametrized constant-depth quantum neuron [56.51261027148046]
  We propose a framework that builds quantum neurons based on kernel machines. We present here a neuron that applies a tensor-product feature mapping to an exponentially larger space. It turns out that parametrization allows the proposed neuron to optimally fit underlying patterns that the existing neuron cannot fit.
  arXiv  Detail & Related papers  (2022-02-25T04:57:41Z)
• A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification [0.0]
  We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. On simulated hardware, we observe that the hybrid neural network achieves roughly 10% higher classification accuracy and 20% better minimization of cost than an individual variational quantum circuit.
  arXiv  Detail & Related papers  (2022-01-05T21:06:30Z)
• 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)
• On quantum neural networks [91.3755431537592]
  We argue that the concept of a quantum neural network should be defined in terms of its most general function. Our reasoning is based on the use of the Feynman path integral formulation in quantum mechanics.
  arXiv  Detail & Related papers  (2021-04-12T18:30:30Z)
• 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)
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks [5.067768639196139]
  Quantum computing enables quantum neural networks (QNNs) to have great potentials to surpass artificial neural networks (ANNs) Various models related to QNNs have been developed, but they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. We establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron.
  arXiv  Detail & Related papers  (2020-11-06T15:25:52Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)

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.