Mutual Reinforcement between Neural Networks and Quantum Physics
- URL: http://arxiv.org/abs/2105.13273v2
- Date: Mon, 14 Feb 2022 18:30:43 GMT
- Title: Mutual Reinforcement between Neural Networks and Quantum Physics
- Authors: Yue Ban, Javier Echanobe, Erik Torrontegui, Jorge Casanova
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum machine learning emerges from the symbiosis of quantum mechanics and
machine learning. In particular, the latter gets displayed in quantum sciences
as: (i) the use of classical machine learning as a tool applied to quantum
physics problems, (ii) or the use of quantum resources such as superposition,
entanglement, or quantum optimization protocols to enhance the performance of
classification and regression tasks compare to their classical counterparts.
This paper reviews examples in these two scenarios. On the one hand, a
classical neural network is applied to design a new quantum sensing protocol.
On the other hand, 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. These examples
demonstrate the mutual reinforcement of both neural networks and quantum
physics.
Related papers
- Neural networks with quantum states of light [2.621434923709917]
Photonic artificial neural networks offer the opportunity to exploit the advantages of both classical and quantum optics.
Photonic neuro-inspired computation and machine learning have been successfully demonstrated in classical settings.
Quantum optical networks have triggered breakthrough applications such as teleportation, quantum key distribution and quantum computing.
arXiv Detail & Related papers (2024-10-23T09:23:49Z) - Let the Quantum Creep In: Designing Quantum Neural Network Models by
Gradually Swapping Out Classical Components [1.024113475677323]
Modern AI systems are often built on neural networks.
We propose a framework where classical neural network layers are gradually replaced by quantum layers.
We conduct numerical experiments on image classification datasets to demonstrate the change of performance brought by the systematic introduction of quantum components.
arXiv Detail & Related papers (2024-09-26T07:01:29Z) - 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 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) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Quantum Neural Network Classifiers: A Tutorial [1.4567067583556714]
We focus on quantum neural networks in the form of parameterized quantum circuits.
We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks.
benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language.
arXiv Detail & Related papers (2022-06-06T18:00:01Z) - Quantum neural networks force fields generation [0.0]
We design a quantum neural network architecture and apply it successfully to different molecules of growing complexity.
The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances.
arXiv Detail & Related papers (2022-03-09T12:10:09Z) - 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.