Advances in Quantum Machine Learning and Quantum Information

Dear Colleagues,

Quantum machine learning (QML) is an emerging interdisciplinary research area at the intersection of mathematics, physics and machine learning. AI and quantum information (QI), currently trending topics, involve work and the scientific community’s attention. This Special Issue aims to analyze quantum algorithms to solve machine learning tasks, improve classical machine learning techniques and apply machine learning techniques to enhance the capabilities of quantum computers. Quantum computing requires STEM experts and specialists to ensure scientific rigor, continuing to provide technological advances.

The main goal of the Special Issue “Advances in Quantum Machine Learning and Quantum Information” is to share knowledge about the most recent advancements in the field of QML, as well as to build a discussion forum on this topic for researchers in the field and machine learning researchers interested in applying AI to enhance quantum computing algorithms.

We observed many published research papers in the quantum machine learning domain in recent years. Machine learning researchers are increasingly interested in applying AI to the field of quantum computing (and vice versa). Therefore, we expect the proposed Special Issue “Advances in Quantum Machine Learning, and Quantum Information” to have strong interest.

Objectives

• Present the most recent cutting-edge research results in the field of QML and QI;
• Discuss the most promising future research directions in QML and QI;
• Integrate the community of researchers working in the field of QML, machine learning researchers and quantum computing researchers.

Scope

We welcome the submission of papers including, but not limited to, the following topics:

AI for Quantum

• Machine learning for improved quantum algorithm performance;
• Machine learning for quantum control;
• Machine learning for building better quantum hardware.

QML technologies and applications

• Quantum computing: models and paradigms;
• Fairness/ethics with quantum machine learning;
• Quantum algorithms for hyperparameter tuning (quantum computing for auto-ML);
• Theory of quantum-enhanced machine learning;
• Quantum machine learning algorithms based on the Grover search;
• Quantum-enhanced reinforcement learning;
• Quantum computing, models and paradigms such as quantum annealing and quantum sampling.

QML foundations

• Quantum computing: models and paradigms;
• Applications of quantum machine learning;
• Quantum tensor networks and their applications in QML;
• Quantum algorithms for linear systems of equations and other algorithms: quantum neural networks, quantum hidden Markov models, quantum PCA, quantum SVM, quantum autoencoders, quantum transfer learning, quantum Boltzmann machines, Grover, Shor and others in their mathematical concepts.

Quantum information and Cybersecurity

• Quantum cryptography;
• Postquantum cryptography;
• Quantum algorithms and foundations.

Dr. Javier Orduz
Dr. Pablo Rivas
Guest Editors

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 machine learning
• quantum AI
• quantum cryptography
• quantum information
• quantum machine learning applications