QML 2019: Quantum Machine Learning |
Website | http://teamsb.net/qml/ |
Submission link | https://easychair.org/conferences/?conf=qml2019 |
Abstract registration deadline | August 31, 2018 |
Submission deadline | November 15, 2018 |
Quantum machine learning is an emerging interdisciplinary research area which resorts to the principles of quantum physics applied to machine learning. Quantum machine learning algorithms helps to improve classical methods of machine learning by taking the advantages offered by quantum computation. Given the inherent parallelism offered due to the features of quantum computing, researchers have evolved different intelligent tools and techniques which are more robust and efficient in performance. Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. For example, the outcome of the measurement of a qubit could reveal the result of a binary classification task. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. Imparting intelligence to the machines has always been a challenging thoroughfare. Over the years, several intelligent tools have been invented or proposed to deal with the uncertainties encountered by human beings with the advent of the soft computing paradigm. However, it has been observed that even the soft computing tools often fall short in offering a reliable and reasonable solution in real time. Hence, scientists employed hybrid intelligent techniques using the combination of several soft computing tools to overcome the shortcomings. Quantum computing has evolved from the studies of Feynman and Deutsche who evolved efficient searching techniques in the quantum domain. These searching techniques outperform the classical techniques both in terms of time and space. Inspired by this, researchers are on the spree for conjoining the existing soft computing tools with the quantum computing paradigm to evolve more robust and time efficient intelligent algorithms. The resultant algorithms are immensely useful for solving several scientific and engineering problems, which includes data processing and analysis, machine vision, social networks, big data analytics, flow shop scheduling problems to name a few.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Prospective authors are invited to submit a 3- 4 pages Abstract of the paper along with title of the paper and author details. Abstract should highlight the novelty and contribution of the proposed article.
- Authors need to submit this abstract using the Easy Chair submission link given in http://qml.teamsb.net/
List of Topics
- Quantum Computing
- Quantum Machine Learning
- Quantum Machine Learning
Publication
QML 2019 proceedings will be published by Cambridge Scholar Publishing
Contact
All questions about submissions should be emailed to dr.siddhartha.bhattacharyya@gmail.com