Advances in Quantum Computing and Quantum Machine Learning

Dear Colleagues,

Quantum computing has emerged as a new field of scientific research and development that exploits the synergy between quantum mechanisms and computer science. Quantum computing has stirred a burgeoning revolution in the fields of computing and machine learning. Although quantum computers have some of their components named similar to those of classical computers, such as registers, gates, and memory elements, their underlying physical structures are fundamentally distinct and unique. Quantum computers operate on qubits (the quantum counterpart of classical bits) that are capable of existing in states of zero, one, or any intermediate value and exhibit superposition and entanglement properties. These unique attributes empower quantum computers to simultaneously follow multiple computational paths within a single calculation, which is not possible by classical computers without repeated iterations. Quantum computing is capable of enhancing the machine learning design process due to its ability to speed up linear algebraic operations exponentially as state space grows. With the advent of noisy intermediate-scale quantum (NISQ) processors, quantum machine learning based on heuristic methods has gained momentum due to the increased computational capabilities of quantum hardware, particularly in the field of deep learning. Since quantum processors are still fairly small and noisy, to improve machine learning performance effectively, NISQ processors often work with classical co-processors in hybrid mode, giving rise to hybrid quantum–classical machine learning.

This Special Issue targets advances in quantum computing and quantum machine learning. This Special Issue invites original research articles and reviews that relate to the circuits, algorithms, implementation, and applications of quantum computing and quantum machine learning. All fields of quantum computing and machine learning, including hybrid quantum–classical computing and machine learning, are of interest to this Special Issue. Topics of interest include, but are not limited to, the following:

• Quantum computing;
• Quantum machine learning;
• Hybrid quantum–classical computing;
• Hybrid quantum–classical machine learning;
• Noisy intermediate-scale quantum (NISQ) processing;
• Circuits for quantum computing and machine learning;
• Performance analysis of classical versus quantum computing;
• Hybrid quantum–classical deep learning;
• Hybrid quantum–classical neural networks;
• Applications of (hybrid) quantum computing;
• Applications of (hybrid) quantum machine learning.

Dr. Arslan Munir
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• quantum computing
• quantum machine learning
• hybrid quantum-classical computing
• hybrid quantum-classical machine learning
• noisy intermediate-scale quantum processing
• quantum algorithms
• quantum circuits