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Quantum neural networks with deep residual learning.

Yanying Liang, Wei Peng, Zhu‐Jun Zheng, Olli Sílven, Guoying Zhao

Published Dec 14, 2020• 0 citations
OpenAlex

Abstract

Inspired by the success of neural networks in the classical machine learning tasks, there has been tremendous effort to develop quantum neural networks (QNNs), especially for quantum data or tasks that are inherently quantum in nature. Currently, with the imminent advent of quantum computing processors to evade the computational and thermodynamic limitation of classical computations,} designing an efficient quantum neural network becomes a valuable task in quantum machine learning. In this paper, a novel quantum neural network with deep residual learning (ResQNN) is proposed. {Specifically, a multiple layer quantum perceptron with residual connection is provided. Our ResQNN is able to learn an unknown unitary and get remarkable performance. Besides, the model can be trained with an end-to-end fashion, as analogue of the backpropagation in the classical neural networks. To explore the effectiveness of our ResQNN , we perform extensive experiments on the quantum data under the setting of both clean and noisy training data. The experimental results show the robustness and superiority of our ResQNN, when compared to current remarkable work, which is from \textit{Nature communications, 2020}. Moreover, when training with higher proportion of noisy data, the superiority of our ResQNN model can be even significant, which implies the generalization ability and the remarkable tolerance for noisy data of the proposed method.

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Inspired by the success of neural networks in the classical machine learning tasks, there has been tremendous effort to develop quantum neural networks (QNNs), especially for quantum data or tasks that are inherently quantum in nature. Currently, with the imminent advent of quantum computing processors to evade the computational and thermodynamic limitation of classical computations,} designing an efficient quantum neural network becomes a valuable task in quantum machine learning. In this paper, a novel quantum neural network with deep residual learning (ResQNN) is proposed. {Specifically, a multiple layer quantum perceptron with residual connection is provided. Our ResQNN is able to learn an unknown unitary and get remarkable performance. Besides, the model can be trained with an end-to-end fashion, as analogue of the backpropagation in the classical neural networks. To explore the effectiveness of our ResQNN , we perform extensive experiments on the quantum data under the setting of both clean and noisy training data. The experimental results show the robustness and superiority of our ResQNN, when compared to current remarkable work, which is from \textit{Nature communications, 2020}. Moreover, when training with higher proportion of noisy data, the superiority of our ResQNN model can be even significant, which implies the generalization ability and the remarkable tolerance for noisy data of the proposed method.
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analysis how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.
The Internet of Things (IoT) is a global network of millions of devices connected in wireless that exchange data. Multiple data are aiming to be observed through a single platform, and it becomes necessary to evaluate accuracy in order to realize the ideal IoT platform. IoT data collection is more important to meet the growing data analysis in demanding time-sensitive and real-time decision making. There has been a lot of effort involved in developing efficient quantum neural networks due to the success over the traditional neural network. This research proposes a new deep residual learning based quantum–classical neural network (Res-QCNN). The residual structural block is connected to the quantum neural network to examine and provide a training algorithm for analysing the IoT platform. Simultaneously, the benefits and drawbacks of translating quantum idea form deep residual learning are discussed. As a result, training algorithm is performing in the proposed model from beginning to end, similar working of backpropagation in traditional neural networks. When compared to existing, the Res-QCNN completes better when learning a Unitary function and has robustness for noisy data.
Abstract The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.
Artificial neural networks, usually just called neural networks, computing systems indefinitely inspired by the biological neural networks and they are extensive in both research as well as industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project, we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed-forward neural network proficient in universal quantum computation. This structure takes input from one layer of qubits and drives that input onto another layer of qubits. This layer of qubits evaluates this information and drives on the output to the next layer. Eventually, the path leads to the final layer of qubits. The layers do not have to be of the same breadth, meaning they need not have the same number of qubits as the layer before and/or after it. This assembly is trained on which path to take identical to classical ANN. The intended project can be compiled by the subsequent points provided here: 1. The expert training of the quantum neural network utilizing the fidelity as a cost function, providing both conventional and efficient quantum implementations. 2. Use of methods that enable quick optimization with reduced memory requirements. 3. Benchmarking our proposal for the quantum task of learning an unknown unitary and find extraordinary generality and a remarkable sturdiness to noisy training data.