Overview: Machine Learning for Quantum Matter Research | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
22,300
Published on ● Video Link: https://www.youtube.com/watch?v=IsHVcKRA9Yc


Duration: 59:54
267 views
13

Speaker(s): Juan Felipe Carrasquilla Alvarez
Host: Amir Feizpour

Find the recording, slides, and more info at https://ai.science/e/overview-machine-learning-for-quantum-matter-research--XfeXp85nIFa0ithqWiOW

Motivation / Abstract
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, and large-scale numerical simulations. Recently, researchers interested in condensed matter physics have turned their attention to the algorithms underlying modern machine learning, with an eye on making progress in their fields. In this talk I will informally discuss a series of recent developments related to the adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize phases of matter in synthetic an experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. I will also discuss the outlook for future developments in areas at the intersection between machine learning and quantum physics.


------
#AISC hosts 3-5 live sessions like this on various AI research, engineering, and product topics every week! Visit https://ai.science for more details



Other Videos By LLMs Explained - Aggregate Intellect - AI.SCIENCE


2020-10-14An eye on AI in Healthcare | AISC
2020-10-13Some Salient Issues with Saliency Models | AISC
2020-10-13The Messy Side of AI Products | AISC
2020-10-09Human Aware AI: Reducing Transportation Energy of a City by Influencing Individual Behaviour
2020-10-08Policy Priority Inference: Simulations for Government Strategy | AISC
2020-10-07Inferring the 3D Standing Spine Posture from 2D Radiographs | AISC
2020-10-06Knowledge Extraction from Multimodal & Multilingual sources | AISC
2020-10-01Camera Depth of Field Manipulation for Pre- and Post-Image Capture | AISC
2020-09-30[MOREL] Unsupervised Video Object Segmentation for Deep Reinforcement Learning
2020-09-29Dealing with Bias and Fairness in Data Science Systems: A Practical Hands-on Tutorial | AISC
2020-09-29Overview: Machine Learning for Quantum Matter Research | AISC
2020-09-25Overview of Machine Learning for Knowledge Graphs | AISC
2020-09-24Integrating Physics into Machine Learning Models for Scientific Discovery | AISC
2020-09-24Applications of Blockchain to IoT Security | AISC
2020-09-24Human-Technology Systems for Intelligent Civil Infrastructure Operation and Maintenance | AISC
2020-09-23The AI Design Sprint -- setting your AI Initiative up for delivery success! | AISC
2020-09-23explainX - Explainable AI for model developers | AISC
2020-09-22Statistical Issues in Agent-Based Models | AISC
2020-09-22Layerwise Learning for Quantum Neural Networks | AISC
2020-09-17Survival regression with AFT model in XGBoost | AISC
2020-09-17Detecting Off-Topic Spoken Response with NLP | AISC