Xiaodong Ding

Quantum neural networks (QNNs) have made significant progress in theoretical research, yet their application to real‐world tasks remains highly challenging. To handle complex signal classification tasks and reduce the impact of decoherence effects on real quantum devices, it is necessary to study a QNN that both supports multi‐dimensional input features and has small‐scale quantum circuits. Based on the theoretical derivation of the unitary invariance of matrix operations, this paper proposes a novel multi‐block quantum neural network(MQNN), which supports direct rotational encoding of multi‐dimensional features. MQNN combines multiple small‐scale quantum circuits to replace the quantum operations of large quantum circuits, and collaboratively processes multi‐dimensional features to complete the classification task. MQNN is applied to the real collected dataset of radar echo signals from unmanned aerial vehicles(UAVs). The 64‐dimensional Doppler spectrum features extracted from the dataset can be simultaneously rotationally encoded into 64 qubits of the MQNN. Compared with five quantum machine learning algorithms (QSVM, QKNN, QCNN, QNNC, and QNNS), MQNN has the highest accuracy. Moreover, MQNN also has a smaller quantum circuit scale than the amplitude encoded quantum methods, thus it has a stronger anti‐decoherence ability. MQNN provides a useful reference for other quantum algorithms in processing multi‐dimensional data.

The electrocardiogram (ECG), as a key tool for medical diagnosis, accurately captures the dynamic changes in electrical potential of the heart during repolarization, which is crucial for assessing cardiac function abnormalities. In the face of the surge in ECG data volume, the development of efficient, accurate, and fast algorithms for abnormality monitoring has become the key to reducing the burden on physicians and improving the efficiency of diagnosis and treatment. Traditionally, although deep learning models such as LSTM and convolutional neural networks show potential in this area, their large parameter sizes and computational complexity limit further applications. To break through this bottleneck, we innovatively incorporate the unique advantages of quantum computing and design a framework based on embedded quantum-enhanced neural network (QENN) feature mapping for ECG feature extraction. Accurate monitoring of ECG is achieved by seamlessly migrating the output of QENN to the classical, fully connected layer. This attempt, to the best of our knowledge, is the first time that quantum computing technology has been successfully and effectively combined with traditional methods in the field of ECG monitoring. The experimental results show that, compared to popular traditional methods such as LSTM and convolutional neural networks, our proposed method maintains or even surpasses the detection performance of these mainstream methods while significantly reducing the parameter requirements (up to one-twentieth of them). This result not only demonstrates the great potential of quantum computing in biomedical signal processing but also opens up new paths for future ECG arrhythmia detection.

The decoherence effect of the real quantum computer poses a significant challenge for designing quantum neural networks (QNN), which requires simultaneous consideration of model performances and parameterized quantum circuit (PQC) scales. In this paper, a method for the intelligent generation of lightweight QNN is proposed, which utilizes the parameters of quantum chips such as topology and relaxation time to assist the automatic design of QNNs. Combining the mixture expert mechanism with the Hyperband algorithm, a Mixture of Hyperband Experts for QNN is proposed to search for combinations of hyperparameters that meet multi‐objective requirements from the lightweight QNN. To the best of the authors’ knowledge, this is the first work that combines quantum chip capabilities to intelligently generate lightweight QNNs. Experiments are carried out on datasets in the fields of medicine and information security, and compared with classical machine learning algorithms and quantum machine learning algorithms (QSVM, QKNN, Intelligent Generation for QNN), the QNN automatically generated by this method has good performance and scalability, and the PQC has a smaller scale. The anti‐coherence capability of the QNN intelligently generated by this method is verified using experiments on a real quantum computer.

Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications.

The potential of quantum neural networks (QNN) for applications in several fields has been widely demonstrated, but hyperparameter selection remains a technical challenge. The complexity of combinatorial optimization rises dramatically as the dimensionality of the hyperparameter space increases, making hyperparameter selection exceptionally complex. Traditional manual trial‐and‐error methods are both time‐consuming and expensive. To break through this bottleneck, the Ansatz structure set is proposed and the Hyperband algorithm is utilized to combine the structure set with the stacking number and algorithmic hyperparameters to propose the Hyperband‐QNN model. The model can intelligently generate QNN according to the actual data features, and in practical applications in many fields such as medicine and agriculture, it performs well compared with the traditional machine learning algorithms as well as the quantum algorithms QSVM and QKNN, and when it is used to deal with the large‐scale data, the efficiency advantage becomes more obvious with the increase of the number of samples. This not only proves the advantages of Hyperband‐QNN model in practical applications but also provides useful exploration and reference for the integration of traditional algorithms and quantum algorithms in practical problems and the play of their respective advantages.

In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processing information in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processing information, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain’s ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of classical classifiers with a considerable number of trainable parameters on some subsets of the MNIST dataset.

Quantum computing as a new computing model with parallel computing capability and high information carrying capacity, has attracted a lot of attention from researchers. Ensemble learning is an effective strategy often used in machine learning to improve the performance of weak classifiers. Currently, the classification performance of quantum classifiers is not satisfactory enough due to factors such as the depth of quantum circuit, quantum noise, and quantum coding method, etc. For this reason, this paper combined the ensemble learning idea and quantum classifiers to design a novel hybrid quantum machine learning model. Firstly, we run the Stacking method in classical machine learning to realize the dimensionality reduction of high-latitude data while ensuring the validity of data features. Secondly, we used the Bagging method and Bayesian hyperparameter optimization method applied to quantum support vector machine (QSVM), quantum K nearest neighbors (QKNN), variational quantum classifier (VQC). Thirdly, the voting method is used to ensemble the predict results of QSVM, QKNN, VQC as the final result. We applied the hybrid quantum ensemble machine learning model to malicious code detection. The experimental results show that the classification precision (accuracy, F1-score) of this model has been improved to 98.9% (94.5%, 94.24%). Combined with the acceleration of quantum computing and the higher precision rate, it can effectively deal with the growing trend of malicious codes, which is of great significance to cyberspace security.