We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.
We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.
Abstract Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the minimum-error QSD, called the variational quantum state discriminator (VQSD). The VQSD uses a parameterized quantum circuit that is trained by minimizing a cost function derived from the QSD, and finds the optimal positive-operator valued measure (POVM) for distinguishing target quantum states. The VQSD is capable of discriminating even unknown states, eliminating the need for expensive quantum state tomography. Our numerical simulations and comparisons with semidefinite programming demonstrate the effectiveness of the VQSD in finding optimal POVMs for minimum-error QSD of both pure and mixed states. In addition, the VQSD can be utilized as a supervised machine learning algorithm for multi-class classification. The area under the receiver operating characteristic curve obtained in numerical simulations with the Iris flower dataset ranges from 0.97 to 1 with an average of 0.985, demonstrating excellent performance of the VQSD classifier.
Quantum computing has shown considerable promise for compute-intensive tasks in recent years. For instance, classification tasks based on quantum neural networks (QNN) have garnered significant interest from researchers and have been evaluated in various scenarios. However, the majority of quantum classifiers are currently limited to binary classification tasks due to either constrained quantum computing resources or the need for intensive classical post-processing. In this paper, we propose an efficient quantum multi-classifier called MORE, which stands for measurement and correlation based variational quantum multi-classifier. MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit. To extract the complete information from the readout qubit, we select three observables that form the basis of a two-dimensional Hilbert space. We then use the quantum state tomography technique to reconstruct the readout state from the measurement results. Afterward, we explore the correlation between classes to determine the quantum labels for classes using the variational quantum clustering approach. Next, quantum label-based supervised learning is performed to identify the mapping between the input data and their corresponding quantum labels. Finally, the predicted label is determined by its closest quantum label when using the classifier. We implement this approach using the Qiskit Python library and evaluate it through extensive experiments on both noise-free and noisy quantum systems. Our evaluation results demonstrate that MORE, despite using a simple ansatz and limited quantum resources, achieves advanced performance.
Machine learning models achieve strong predictive performance by uncovering hidden patterns and structures within data. Quantum Machine Learning (QML) extends this capability by leveraging principles of quantum mechanics to address complex classification tasks. However, designing effective feature extraction strategies remains challenging. The choice of ansatz plays a central role in determining the expressive power and learning capability of quantum models. In this work, we propose a parameterized quantum circuit model incorporating adaptive transformations between neighboring qubits to enhance feature extraction and classification performance. The proposed approach is evaluated on seven benchmark datasets from the UCI repository: Iris, Banknote Authentication (BNA), Raisin, Wireless Indoor Localization (WIL), Wine, Penguin, and HTRU2. Its performance is compared against existing quantum classification models, including the Amplitude Embedding Model (AEM), Variational Quantum Circuit (VQC), and Quantum Multi-Class Classifier (QMCC), using identical numbers of layers and training epochs. Additionally, comparisons are made with well-established standard classical classifiers. Experimental results implemented on the PennyLane platform demonstrate that the proposed model achieves high test accuracies within only 10–15 training epochs, reaching 100% (Iris), 98.3% (BNA), 91.1% (Raisin), 97.0% (WIL), 99.2% (Penguin), 94.0% (Wine), and 95.4% (HTRU2). These findings indicate that the proposed ansatz effectively extracts meaningful features and performs competitively in both binary and multi-class classification tasks, highlighting its potential for practical quantum machine learning applications.
Abstract We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according to the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parameters into gate-based operations and optimize the parameters via gradient descent. The high cost of training and low fidelity of current quantum devices, however, restricts much of quantum machine learning to classical simulation. For few-qubit problems with large datasets, training the matrix elements directly, as is done with weight matrices in classical neural networks, can be faster than decomposing data and parameters into gates. We propose a method that trains matrices directly while maintaining unitarity through a single regularization term added to the loss function. A second training step, circuit alignment, then recovers a gate-based architecture from the resulting soft-unitary. On a five-qubit supervised classification task with 1000 datapoints, this two-step process produces a trained variational circuit in under four minutes, compared to over two hours for direct circuit training, while achieving lower binary cross-entropy loss. In a second experiment, soft-unitaries are embedded in a hybrid quantum-classical network for a reinforcement learning cartpole task, where the hybrid agent outperforms a purely classical baseline of comparable size.
With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.