EPJ ST Special Issue: Machine Learning for quantum many-body systems
- Published on 22 August 2022
Editors: Roberta Citro and Morten Hjorth-Jensen
This special topic will cover different aspects of machine learning for the description of quantum many-body physics systems from both solid state, statistical mechanics and computer science. Machine learning will be discussed as a complementary method to current computational techniques for many-body systems, including Monte Carlo and tensor networks, as well as methods to analyze "big data" generated in experiments.
Foundational questions in machine learning for many-body systems will be addressed, as well as the theoretical connections between deep learning, renormalization group theory and neural-networks. We expect to build up a volume in which readers can find new directions in turning the early machine learning methods in performing multifaceted tasks, e.g. to design new experiments. Moreover, the reader will be exposed to a new multidisciplinary field and will enrich his/her vision of computational techniques.
The proposed issue will cover areas including:
- Fast machine learning for scientific discoveries
- Artificial Intelligence
- Novel quantum Monte Carlo Methods
- Applications of Machine Learning and Artificial Intelligence to sub-atomic physics
- Applications of Bayesian optimization and Bayesian statistics and Bayesian Machine Learning to many-body problems
The Guest Editors invite authors to submit their original research and short reviews on the theme of the Special Issue of the European Physical Journal - Special Topics. Articles may be one of four types: (i) minireviews (10-15 pages), (ii) tutorial reviews (15+ pages), (iii) original paper v1 (5-10 pages), or (iv) original paper v2 (3-5 pages). More detailed descriptions of each paper type can be found in the Submission Guidelines. Manuscripts should be prepared using the latex template (preferably 2-column layout), which can be downloaded here.
Articles should be submitted to the Editorial Office of EPJ ST via the submission system, and should be clearly identified as intended for the topical issue “Machine Learning for quantum many-body systems”.
Open Access: EPJST is a hybrid journal offering Open Access publication via the Open Choice programme and a growing number of Springer Compact “Publish and Read” arrangements which enable authors to publish OA at no direct cost (all costs are paid centrally).