Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
- URL: http://arxiv.org/abs/2407.14055v1
- Date: Fri, 19 Jul 2024 06:31:22 GMT
- Title: Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
- Authors: Peiyong Wang, Casey R. Myers, Lloyd C. L. Hollenberg, Udaya Parampalli,
- Abstract summary: One of the first considerations is the design of the quantum machine learning model itself.
Recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning.
In this paper, we take an alternative approach by incorporating the design of classical deep learning algorithms to the design of quantum neural networks.
- Score: 0.9374652839580181
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms relies on the ``quantisation" of classical learning algorithms, such as using quantum linear algebra to implement important subroutines of classical algorithms, if not the entire algorithm, seeking to achieve quantum advantage through possible run-time accelerations brought by quantum computing. However, recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning [1]. Research also has been undertaken to exploit properties that are unique to quantum systems, such as quantum contextuality, to better design quantum machine learning models [2]. In this paper, we take an alternative approach by incorporating the heuristics and empirical evidences from the design of classical deep learning algorithms to the design of quantum neural networks. We first construct a model based on the data reuploading circuit [3] with the quantum Hamiltonian data embedding unitary [4]. Through numerical experiments on images datasets, including the famous MNIST and FashionMNIST datasets, we demonstrate that our model outperforms the quantum convolutional neural network (QCNN)[5] by a large margin (up to over 40% on MNIST test set). Based on the model design process and numerical results, we then laid out six principles for designing quantum machine learning models, especially quantum neural networks.
Related papers
- Measurement-based quantum machine learning [0.0]
A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data.
We propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA)
arXiv Detail & Related papers (2024-05-14T05:17:01Z) - 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 Methods for Neural Networks and Application to Medical Image
Classification [5.817995726696436]
We introduce two new quantum methods for neural networks.
The first is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit.
The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation.
arXiv Detail & Related papers (2022-12-14T18:17:19Z) - A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - Multiclass classification using quantum convolutional neural networks
with hybrid quantum-classical learning [0.5999777817331318]
We propose a quantum machine learning approach based on quantum convolutional neural networks for solving multiclass classification problems.
We use the proposed approach to demonstrate the 4-class classification for the case of the MNIST dataset using eight qubits for data encoding and four acnilla qubits.
Our results demonstrate comparable accuracy of our solution with classical convolutional neural networks with comparable numbers of trainable parameters.
arXiv Detail & Related papers (2022-03-29T09:07:18Z) - Comparing concepts of quantum and classical neural network models for
image classification task [0.456877715768796]
This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network.
Although its simulation is time-consuming, the quantum network, although its simulation is time-consuming, overcomes the classical network.
arXiv Detail & Related papers (2021-08-19T18:49:30Z) - 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) - Mutual Reinforcement between Neural Networks and Quantum Physics [0.0]
Quantum machine learning emerges from the symbiosis of quantum mechanics and machine learning.
The use of classical machine learning as a tool applied to quantum physics problems.
The design of a quantum neural network based on the dynamics of a quantum perceptron with the application of shortcuts to adiabaticity gives rise to a short operation time and robust performance.
arXiv Detail & Related papers (2021-05-27T16:20:50Z) - 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) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z)
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.