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PERIMETER INSTITUTE RECORDED SEMINAR ARCHIVE

PIRSA:C16017 - Quantum Machine LearningPODCAST Subscribe to podcast

Quantum Machine Learning

Organizer(s):

Collection URL: http://pirsa.org/C16017


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PIRSA:16080000  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Welcome and Opening Remarks
Speaker(s):
Abstract:
Date: 08/08/2016 - 9:30 am

PIRSA:16080001  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Comparing Classical and Quantum Methods for Supervised Machine Learning
Speaker(s): Ashish Kapoor
Abstract: Supervised Machine Learning is one of the key problems that arises in modern big data tasks. In this talk, I will first describe several different classical algorithmic paradigms for classification and then contrast them with quantum algorithmic constructs. In particular, we will look at classical m... read more
Date: 08/08/2016 - 9:35 am

PIRSA:16080002  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Classification on a quantum computer: Linear regression and ensemble methods
Speaker(s): Maria Schuld
Abstract: Quantum machine learning algorithms usually translate a machine learning methods into an algorithm that can exploit the advantages of quantum information processing. One approach is to tackle methods that rely on matrix inversion with the quantum linear system of equations routine. We give such a qu... read more
Date: 08/08/2016 - 11:00 am

PIRSA:16080003  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Rejection and Particle Filtering for Hamiltonian Learning
Abstract: Many tasks in quantum information rely on accurate knowledge of a system's Hamiltonian, including calibrating control, characterizing devices, and verifying quantum simulators. In this talk, we pose the problem of learning Hamiltonians as an instance of parameter estimation. We then solve this probl... read more
Date: 08/08/2016 - 11:45 am

PIRSA:16080004  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
Speaker(s): Barry Sanders
Abstract: Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control para... read more
Date: 08/08/2016 - 2:30 pm

PIRSA:16080005  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Physics-inspired techniques for association rule mining
Speaker(s): Cyril Stark
Abstract: Imagine you run a supermarket, and assume that for each customer ā€œuā€ you record what ā€œuā€ is buying. For instance, you may observe that u=1 typically buys bread and cheese and u=2 typically buys bread and salami. Studying your dataset you suspect that generally, customers who are likely to bu... read more
Date: 09/08/2016 - 9:30 am

PIRSA:16080006  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Physical approaches to the extraction of relevant information
Speaker(s): David Schwab
Abstract: In the first part of this talk, I will focus on the physics of deep learning, a popular subfield of machine learning where recent performance on tasks such as visual object recognition rivals human performance. I present work relating greedy training of deep belief networks to a form of variational ... read more
Date: 09/08/2016 - 11:00 am

PIRSA:16080007  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Learning with Quantum-Inspired Tensor Networks
Speaker(s): Miles Stoudenmire
Abstract: We propose a family of models with an exponential number of parameters, but which are approximated by a tensor network. Tensor networks are used to represent quantum wavefunctions, and powerful methods for optimizing them can be extended to machine learning applications as well. We use a matrix pro... read more
Date: 09/08/2016 - 11:45 am

PIRSA:16080010  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Learning quantum annealing
Speaker(s): James Steck
Abstract:
Date: 09/08/2016 - 2:30 pm

PIRSA:16080011  ( MP4 Medium Res , MP3 , PDF ) Which Format?
Deep Learning: An Overview
Speaker(s):
Abstract:
Date: 09/08/2016 - 3:00 pm

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