Quantum Machine Learning
Format results
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Comparing Classical and Quantum Methods for Supervised Machine Learning
Ashish Kapoor - Microsoft Corporation
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Classification on a quantum computer: Linear regression and ensemble methods
Maria Schuld - University of KwaZulu-Natal
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Rejection and Particle Filtering for Hamiltonian Learning
Cassandra Granade - Dual Space Solutions, LLC
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Physical approaches to the extraction of relevant information
David Schwab - Northwestern University
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Learning with Quantum-Inspired Tensor Networks
Miles Stoudenmire - Flatiron Institute
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Quantum Boltzmann Machine using a Quantum Annealer
Mohammad Amin - D-Wave Systems Inc.
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Comparing Classical and Quantum Methods for Supervised Machine Learning
Ashish Kapoor - Microsoft Corporation
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… -
Classification on a quantum computer: Linear regression and ensemble methods
Maria Schuld - University of KwaZulu-Natal
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… -
Rejection and Particle Filtering for Hamiltonian Learning
Cassandra Granade - Dual Space Solutions, LLC
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… -
Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
Barry Sanders - University of Calgary
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… -
Physics-inspired techniques for association rule mining
Cyril Stark - ETH Zurich
Imagine you run a supermarket, and assume that for each customer “u” you record what “u” 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 buy cheese… -
Physical approaches to the extraction of relevant information
David Schwab - Northwestern University
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… -
Learning with Quantum-Inspired Tensor Networks
Miles Stoudenmire - Flatiron Institute
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… -
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Towards Quantum Supremacy with Near-Term Devices
Sergei Isakov - ETH Zurich
Can quantum computers outperform classical computers on any computational problem in the near future? We study the problem of sampling from the output distribution of random quantum circuits. Sampling from this distribution requires an exponential amount of classical computational resources. We… -
Quantum Boltzmann Machine using a Quantum Annealer
Mohammad Amin - D-Wave Systems Inc.
Machine learning is a rapidly growing field in computer science with applications in computer vision, voice recognition, medical diagnosis, spam filtering, search engines, etc. In this presentation, I will introduce a new machine learning approach based on quantum Boltzmann distribution of a…