Quantum Computing in Machine Learning

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

Leopoldo Angrisani, Egidio De Benedetto, Alessandro Di Bernardo +2 more

In recent years, quantum technology has been developing in several fields, including quantum computing. Quantum Computing (QC) is a type of computing that uses quantum bits (qubits) instead of classical bits to perform calculations. Unlike classical computers, which use binary logic (0s and 1s), quantum computers exploit quantum phenomena (such as superposition and entanglement), which are expected to bring advantages over traditional Machine Learning (ML). However, QC is still an emerging field, and further research is needed to fully understand its potential in ML. In this regard, for example, it is still unclear how QC compares with traditional ML in terms of stability and generalization. Starting from this consideration, this work intends to experimentally compare quantum and traditional ML. In particular, the classical Support Vector Machine (SVM), which belongs to the traditional ML approach, and a Quantum Support Vector Machine (QSVM), as Quantum Machine Learning (QML) approach (which combines quantum computing and machine learning through quantum properties), are applied to a specific case study related to electroencephalographic (EEG) data. The comparison was conducted on similar development environments to ensure comparable results from a measurement standpoint. The results highlight that there are some critical points involving the generalization ability of QML algorithms, possibly related to an appropriate and specific hyperparameter search.

T. Shreekumar, K. M. Rashmi, Ravinarayana +1 more

Zainab Abohashima, Mohamed Elhoseny, Essam Halim Houssein +1 more

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented.

Kaushal Kishor

Quantum machine learning, which combines quantum computers with regular machine learning, is a much contested field of research. The goal of quantum ML is to provide light on the possible applications of quantum mechanical findings to ML challenges. The exponential growth of data that must be taught to a typical computer paradigm implies that the present cutting-edge computing equipment cannot keep up with the pace. Quantum computing might be useful in this context for continuous training with enormous datasets. Scientists have resorted to quantum machine learning to reduce the development time for new learning algorithms. Traditional machine learning seeks patterns in data and uses those patterns to forecast the future. This is machine learning’s ultimate objective. However, the new patterns created by quantum techniques imply that quantum computers may be more successful in machine learning than traditional computers. One of the most significant uses of cryptography is assuring user anonymity and data security over unsecure networks. In this section, we examine the history of quantum machine learning and summarize the most current results in the field.

Maria Schuld, Francesco Petruccione