Quantum Classification

Davide Pastorello

Durgadevi Palani, K. Akila

Quantum classification is a rapidly developing area in the field of quantum computing that seeks to leverage the unique properties of quantum mechanics to improve the accuracy and efficiency of classification tasks. This chapter provides an in-depth exploration of the key concepts, algorithms, and applications of quantum classification. It begins with an introduction to the basic principles of quantum mechanics underlying quantum classification, including qubits, quantum gates, and quantum circuits. It then moves on to various quantum classification algorithms, such as quantum support vector machines, quantum decision trees, and quantum K-nearest neighbors, and compares them to their classical counterparts. Next, it delves into the practical aspects of quantum classification, such as hardware and software considerations, error correction, and optimization techniques. The use of quantum machine learning libraries, such as PennyLane and Qiskit, to implement quantum classification algorithms are also explored. The chapter then showcases the wide range of applications of quantum classification, including image and speech recognition, natural language processing, finance, and drug discovery. It highlights the potential advantages and limitations of quantum classification, compared to classical approaches, and presents several use cases where quantum classification has demonstrated superior performance. Finally, the current state of the art in quantum classification is discussed and future directions for research in this exciting and rapidly growing field are outlined. The challenges and opportunities for the adoption of quantum classification in industry and academia, and the potential impact of quantum classification on society at large are also covered. Overall, this chapter serves as a comprehensive guide to the emerging field of quantum classification, offering readers a clear understanding of the theory, algorithms, and applications of this exciting new area of research.

Hector Spencer-Wood, John Jeffers, Sarah Croke

We consider measurement disturbance tradeoffs in quantum machine learning protocols which seek to learn about quantum data. We study the simplest example of a binary classification task in the unsupervised regime. Specifically, we investigate how a classification of two qubits, that can each be in one of two unknown states, affects our ability to perform a subsequent classification on three qubits when a third is added. Surprisingly, we find a range of strategies in which a nontrivial first classification does not affect the success rate of the second classification. There is, however, a nontrivial measurement disturbance tradeoff between the success rate of the first and second classifications, and we fully characterize this tradeoff analytically.

Davide Pastorello

Enrica Santucci, Giuseppe Sergioli

Guoqing Zhou, Mengxuan Zhang, Jianrong Xiao +3 more

The existing models based on quantum pixels can mine spectral information and entanglement features, but are time-consuming in practice due to its characteristics on pixel-level entanglement. For this reason, an innovative image classification model, named ‘Quantum Superpixel Entanglement Classification (QSEC) model’, is proposed in this study. By treating superpixels in remote sensing images (RSIs) as quantum particles, we extend the paradigm of image classification from the pixel level to the superpixel level. This design fully leverages the spectral information, textural features, and inter-superpixel quantum entanglement characteristics inherent in RSIs, enabling the effective capture and processing of complex image information via quantum entanglement-driven computational frameworks. Furthermore, the framework quantizes RSI superpixels to map their greyscale and texture features onto corresponding quantum states, constructs a quantum superpixel entanglement pattern, extracts the entanglement relationships between superpixels using entanglement coefficients, and ultimately develops a classification method applicable to multispectral images. The results from the four groups of the experiments demonstrated that: (1) The QSEC model achieves an average classification accuracy of and Kappa coefficient (KC) of 92.02% and 0.86, respectively, with an average running time of 29.25 seconds; (2) the running time of the QSEC model was decreased by up to 400 seconds, the classification speed was improved by 75.29% when compared with the neural network classification (NNC), support vector machine (SVM) and deep learning (DL) model; and (3) the QSEC model improves classification accuracy by 8.76% and increases KC by 0.12 when compared with unsupervised classification models such as ISODATA and K-means.