The use of properties that are intrinsic to quantum mechanics has made it possible to build quantum algorithms with greater efficiency than classical algorithms to solve problems whose classically efficient solution either does not exist or is not known. There are quantum neurons that can carry an exponential amount of information to a linear number of quantum information units (qubits) using the quantum property of superposition. In this paper, we compare the performance of three of these quantum neuron models applied to the diabetes classification problem. We also propose the use of different data preprocessing strategies. Quantum neurons were simulated using the IBM Qiskit tool. We compare the preprocessing approaches applied to two toy problems (1) simulating the XOR operator and (2) solving a generic nonlinear problem. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future.
The use of properties that are intrinsic to quantum mechanics has made it possible to build quantum algorithms with greater efficiency than classical algorithms to solve problems whose classically efficient solution either does not exist or is not known. There are quantum neurons that can carry an exponential amount of information to a linear number of quantum information units (qubits) using the quantum property of superposition. In this paper, we compare the performance of three of these quantum neuron models applied to the diabetes classification problem. We also propose the use of different data preprocessing strategies. Quantum neurons were simulated using the IBM Qiskit tool. We compare the preprocessing approaches applied to two toy problems (1) simulating the XOR operator and (2) solving a generic nonlinear problem. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future.
Parametric models with real numbers valued parameters have greater performance than its counterparts with binary valued weights, due to the gain in representing informa- tion with real values, and therefore having a larger space for memory association. In this work, is proposed a quantum neuron capable of store real weights and preserve the gain of the superposition property, encoding the information in the probability amplitudes of the quantum system, the Real Weights Quantum Neuron. Its performance is compared with other quantum neurons to analyze the application of the quantum neurons on real-world problems, i.e diabetes classification. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future. This work is also a contribution to the field of quantum neural networks, which can be further advanced from the quantum neuron proposed.
Abstract The paper focused on the implementation of Quantum machine learning and artificial intelligence techniques in diagnosing diseases, specifically focusing on diabetes. The paper proposed an ensemble approach that combined classical algorithms with quantum processing unit (QPU)--based algorithms to improve the performance of a model. The diabetes dataset used in the study is obtained from the (Centre for Disease Control and Prevention (CDC) repository, and the goal is to classify patients as either diabetic or non-diabetic. The ensemble algorithms examined in the study include Voting classifier, Adaboost, Xgboost, Catboost, and QPU-based Qboost. While Qboost demonstrates some quantum speedup, its performance is not satisfactory. Therefore, the proposed hybrid model is developed to enhance the performance metrics. The hybrid model achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.89, 0.85, 0.95, 0.90, and 0.96, respectively, on the diabetes dataset. In comparison, the top-performing Adaboost algorithm achieves an average accuracy, precision, recall, f1 score, and AUC score of 0.94, 0.91, 0.98, 0.94, and 0.98, respectively. The paper concludes that while quantum computing (QC) significantly improves computation speed, it comes at a slight cost of a 5 \% decrease in classification metrics and 0.186 in the AUC score. Additionally, the study suggests that further development of Quantum computing hardware will enhance overall performance metrics.
Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.
Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).
In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.
Most proposals for quantum neural networks (QNN) have skipped over the problem of how to learn the networks. This paper describes a novel model for quantum neuron and proposes its learning algorithm. It can be shown that this algorithm works on quantum systems and the convergence result demonstrates efficient performance of the proposed quantum neuron. Numerical and graphical results show that this single quantum neuron can perform the XOR function unrealizable with a classical neuron and can eliminate the necessity of building a network of neurons to obtain nonlinear mapping.