A Classical Machine Learning Algorithm Goes Quantum

Charles R. Giardina

Prabhat Kumar Santi, Kamakhya Mishra, Sibabrata Mohanty

Although it will be a while before a practical quantum computer is available, there is no need to hold off. Methods and algorithms are being developed to demonstrate the feasibility of running machine learning (ML) pipelines in QC (Quantum Computing). There is a lot of ongoing work on general QML (Quantum Machine Learning) algorithms and applications. However, a working model or pipeline for a text classifier using quantum algorithms isn't available. This paper introduces quantum machine learning w.r.t text classification to readers of classical machine learning. It begins with a brief description of quantum computing and basic quantum algorithms, with an emphasis on building text classification pipelines. A new approach is introduced to implement an end-to-end text classification framework (Quantum Text Classifier - QTC), where pre- and post-processing of data is performed on a classical computer, and text classification is performed using the QML algorithm. This paper also presents an implementation of the QTC framework and available quantum ML algorithms for text classification using the IBM Qiskit library and IBM backends.

Binju Saju, Madhwaraj Kango Gopal, B. Nithya +2 more

Quantum machine learning is a process by which quantum computers are used to learn from data. It is still in its begining stages of development, but has the potential to be much more efficient than classical machine learning algorithms. The main advantages of machine learning is that it can exploit the massive parallelism of quantum computers. This means that quantum machine learning algorithms can potentially learn from data much faster than classical algorithms. Another advantage is that quantum machine learning algorithms can deal with data that is too large or too complex for classical algorithms. For example, a quantum algorithm could be used to learn from a dataset that is too large to fit into a classical computer's memory. There are still many challenges to overcome before quantum machine learning can be used in practice, but the potential benefits are huge. If successful, Quantum machine learning could revolutionize the field of machine learning and have a profound impact on many other areas of science and technology.

Hiep L. Thi

Esma Aı̈meur, Gilles Brassard A, Sébastien Gambs

P Kuppusamy, Nnvs Yaswanth Kumar, Jyotsna Dontireddy +1 more

The rapid development of machine learning technology leads to make the devices in the industries working autonomously. However, the growth of sensors in the industries leads to produce vast data that is utilized by machine learning algorithms to improve the autonomous devices’ performances. However, classical ML algorithms and hardware systems cannot process large data to meet real-time problems. Hence, the researchers have developed Quantum Computing hardware systems and Quantum Machine learning algorithms to speed up the process. This research work presented the review of quantum computing mechanisms and QML algorithms that are applied to classify the images. This work demonstrated the performance comparison of various QML algorithms. It showed that the images are classified using various QML algorithms faster than classical ML algorithms in terms of time.