Towards quantum machine learning

Arshdeep, Konika Rani, Karanpreet Singh +3 more

Machine learning, a subset of AI is moving algorithms in the system which can be learned by data and not programmed explicitly. Various applications of AI across professionals like finance, health-care and automation. With the advancements in Machine Learning, Quantum Machine Learning (QML) or more concretely integrating quantum mechanics into classical machine learning tasks is emerging since QM provides some of the principles to increase efficiency from computational benchmarks. From the most used types of machine learning and algorithms to quantum its usage and applications in Quantum Machine Learning (QML) this paper covers everything. We then articulate some benefits and difficulties of merging quantum computing with machine learning, as well the real-life applications across industry backbones.

Cristian Romero García

Dhanunjai Bandi, Yong Shi, Hossain Shahriar +4 more

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.

Jacob Biamonte, Péter Wittek, Nicola Pancotti +3 more

Vandana Milind Rohokale, Ramjee Prasad

Quantum machine learning (QML) represents the fusion of quantum computing algorithms with machine learning techniques. A prominent application of QML involves employing machine learning algorithms to analyze classical data using a quantum computer, known as quantum-enhanced machine learning. This interdisciplinary field merges concepts from quantum physics and machine learning to pioneer new algorithms and methodologies for data processing and analysis. QML seeks to harness unique quantum properties like superposition and entanglement to enhance the performance of machine learning models. It explores the synergy between quantum computing and machine learning, aiming to leverage quantum systems’ inherent characteristics for advanced data processing capabilities. This chapter provides an in-depth exploration of QML’s comprehensive approach.