A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Zeng, Yi; Wang, Hao; He, Jin; Huang, Qijun; Chang, Sheng

doi:10.3390/e24030394

Cited by 23 publications

(11 citation statements)

References 32 publications

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“…Different VQC Structures: We conducted experiments on the real amplitude circuit (RAC) [ 48 ], the Bellman circuit (BC) [ 48 ], the ladder-like circuit (LC) [ 49 ], and the dressed quantum circuit (DC) [ 35 ]. The experimental results are displayed in Figure 5 a.…”

Section: Methodsmentioning

confidence: 99%

A Quantum-Classical Hybrid Solution for Deep Anomaly Detection

Wang

Huang

Liu

et al. 2023

Entropy

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Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notably, image AD is one of the most representative tasks in current deep AD research. ML’s interaction with quantum computing is giving rise to a heated topic named quantum machine learning (QML), which enjoys great prospects according to recent academic research. This paper attempts to address the image AD problem in a deep manner with a novel QML solution. Specifically, we design a quantum-classical hybrid DNN (QHDNN) that aims to learn directly from normal raw images to train a normality model and then exclude images that do not conform to this model as anomalies during its inference. To enable the QHDNN to perform satisfactorily in deep image AD, we explore multiple quantum layer architectures and design a VQC-based QHDNN solution. Extensive experiments were conducted on commonly used benchmarks to test the proposed QML solution, whose results demonstrate the feasibility of addressing deep image AD with QML. Importantly, the experimental results show that our quantum-classical hybrid solution can even yield superior performance to that of its classical counterpart when they share the same number of learnable parameters.

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Section: Methodsmentioning

confidence: 99%

A Quantum-Classical Hybrid Solution for Deep Anomaly Detection

Wang

Huang

Liu

et al. 2023

Entropy

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“…on hybrid quantum-classical algorithms, [19][20][21][22][23][24][25] which can be realized on classical computers by constructing a suitable software framework. [26] To date, many hybrid quantum-classical machine learning algorithms have been proposed to tackle problems in physics, [27] chemistry, and engineering, [28,29] but their application on prediction of protein-ligand binding affinity has been reported only rarely.…”

Section: Introductionmentioning

confidence: 99%

“…[ 14–16 ] Due to the exponential speedups [ 17,18 ] achieved by quantum computers, quantum‐based machine learning or deep learning may resolve challenging tasks that are beyond the ability of classical computation. In the short term, however, with the inevitable technical problems, many studies have concentrated on hybrid quantum‐classical algorithms, [ 19–25 ] which can be realized on classical computers by constructing a suitable software framework. [ 26 ]…”

Section: Introductionmentioning

confidence: 99%

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

Dong

Li²,

Liu

et al. 2023

Adv Quantum Tech

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Rapid and accurate prediction of protein‐ligand binding affinity plays a vital role in high‐throughput drug screening. With the development of deep learning, increasingly accurate prediction models have been established. Deep learning may have ushered in an era of quantization, but the practical use of this theory for protein‐ligand binding affinity is still infrequent. Here, the introduction of the quantum algorithm into classical deep learning is described, which enables it to reliably predict protein‐ligand binding affinity using simple sequence information. Based on different deep learning models, including graph neural networks (GNN) and convolutional neural networks (CNN), corresponding quantum hybrid deep learning models have been constructed and compared to the classical models. This study has shown that the quantum algorithm can achieve considerable accuracy and good generalization, and show potential to balance between accuracy and generalization, although the parameters used in the model have been remarkably reduced. These models based on quantum hybrid deep learning (QDL) show robust predictions on four benchmark datasets, and exhibit the practical application power in drug screening for targets related to human liver cirrhosis. This work highlights the potential of the hybrid quantum deep learning algorithm in solving complex problems in bioinformatics.

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“…Some of the recent work in quantum machine learning has seen the development of multi-class quantum and quantuminspired classifiers that avoid these heuristic strategies. [9][10][11][12][13][14][15][16] Most recently, the quantum-inspired methods [15,16] use techniques from quantum state discrimination for multi-class classification. Other work has seen the development of quantum convolutional neural networks (QCNNs) for multi-class classification.…”

Section: Introductionmentioning

confidence: 99%

A Multi‐Class Quantum Kernel‐Based Classifier

Pillay,

Sinayskiy,

Jembere

et al. 2023

Adv Quantum Tech

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Multi‐class classification problems are fundamental in many varied domains in research and industry. A popular strategy for solving multi‐class classification problems involves first transforming the problem into many binary classification problems. However, this requires the number of binary classification models that need to be developed to grow with the number of classes. Recent work in quantum machine learning has seen the development of multi‐class quantum classifiers that circumvent this growth by learning a mapping between the data and a set of label states. This work presents the first multi‐class SWAP‐Test classifier inspired by its binary predecessor and the use of label states in recent work. With this classifier, the cost of developing multiple models is avoided. In contrast to previous work, the number of qubits required, the measurement strategy, and the topology of the circuits used is invariant to the number of classes. In addition, unlike other architectures for multi‐class quantum classifiers, the state reconstruction of a single qubit yields sufficient information for multi‐class classification tasks. Both analytical results and numerical simulations show that this classifier is not only effective when applied to diverse classification problems but also robust to certain conditions of noise.

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A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Cited by 23 publications

References 32 publications

A Quantum-Classical Hybrid Solution for Deep Anomaly Detection

A Quantum-Classical Hybrid Solution for Deep Anomaly Detection

Prediction of Protein‐Ligand Binding Affinity by a Hybrid Quantum‐Classical Deep Learning Algorithm

A Multi‐Class Quantum Kernel‐Based Classifier

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