PIRSA:C19025 - Machine Learning for Quantum DesignPODCAST Subscribe to podcast

Machine Learning for Quantum Design

Organizer(s): Roger Melko   Juan Carrasquilla   Estelle Inack   Sandro Sorella  

Collection URL: http://pirsa.org/C19025

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PIRSA:19070003  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Deep learning for quantum many-body physics or: Toolmaking beyond the papyrus complexity
Speaker(s): Giuseppe Carleo
Abstract: In this talk I will discuss some of the long-term challenges emerging with the effort of making deep learning a relevant tool for controlled scientific discovery in many-body quantum physics. The current state of the art of deep neural quantum states and learning tools will be discussed in connect... read more
Date: 08/07/2019 - 9:30 am

PIRSA:19070004  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Simulating Thermal and Quantum Fluctuations in Materials and Molecules
Speaker(s): Michele Ceriotti
Abstract: Both electrons and nuclei follow the laws of quantum mechanics, and even though classical approximations and/or empirical models can be quite successful in many cases, a full quantum description is needed to achieve predictive simulations of matter. Traditionally, simulations that treat both ele... read more
Date: 08/07/2019 - 10:45 am

PIRSA:19070005  ( MP4 Medium Res , MP3 , PDF ) Which Format?
How to use a Gaussian Boson Sampler to learn from graph-structured data
Speaker(s): Maria Schuld
Abstract: A device called a ‘Gaussian Boson Sampler’ has initially been proposed as a near-term demonstration of classically intractable quantum computation. But these devices can also be used to decide whether two graphs are similar to each other. In this talk, I will show how to construct a feature map ... read more
Date: 08/07/2019 - 11:30 am

PIRSA:19070020  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Machine learning meets quantum physics
Speaker(s): Dong-Ling Deng
Abstract: Recently, machine learning has attracted tremendous interest across different communities. In this talk, I will briefly introduce some new progresses in the emergent field of quantum machine learning ---an interdisciplinary field that explores the interactions between quantum physics and machine lea... read more
Date: 08/07/2019 - 2:00 pm

PIRSA:19070021  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Designing a Quantum Transducer With Genetic Algorithms and Electron Transport Calculations
Speaker(s): Kevin Ryczko
Abstract: The fields of quantum information and quantum computation are reliant on creating and maintaining low-dimensional quantum states. In two-dimensional hexagonal materials, one can describe a two-dimensional quantum state with electron quasi-momentum. This description, often referred to as valleytronic... read more
Date: 08/07/2019 - 2:30 pm

PIRSA:19070018  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Engineering Programmable Spin Interactions in a Near-Concentric Cavity
Speaker(s): Emily Davis
Date: 08/07/2019 - 3:30 pm

PIRSA:19070022  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Alleviating the sign structure of quantum states
Speaker(s): Giacomo Torlai
Abstract: The sign structure of quantum states - the appearance of “probability” amplitudes with negative sign - is one of the most striking contrasts between the classical and the quantum world, with far-reaching implications in condensed matter physics and quantum information science. Because it is a ba... read more
Date: 08/07/2019 - 4:00 pm

PIRSA:19070023  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Navigating the quantum computing field as a high school student
Speaker(s): Tanisha Bassan
Date: 08/07/2019 - 7:00 pm

PIRSA:19070006  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Machine Learning Quantum Emergence from Complex Data
Speaker(s): Eun-Ah Kim
Date: 09/07/2019 - 9:30 am

PIRSA:19070024  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
Abstract: In this talk I will discuss how (unsupervised) machine learning methods can be useful for quantum experiments. Specifically, we will consider the use of a generative model to perform quantum many-body (pure) state reconstruction directly from experimental data. The power of this machine learning app... read more
Date: 09/07/2019 - 10:45 am

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