Condensed Matter

Displaying 49 - 60 of 1110

Machine Learning for Quantum Many-Body Systems

21 talks

June 12, 2023 - June 16, 2023

Collection Number C23002

PIRSA
Talk

Welcome and Opening Remarks

Roger Melko University of Waterloo

PIRSA:23060027

June 12, 2023

Condensed Matter

[Virtual] Exploring Quantum Science with Machine Learning

Di Luo Massachusetts Institute of Technology (MIT)

PIRSA:23060028

June 12, 2023

Condensed Matter

Near Term Distributed Quantum Computation using Optimal Auxiliary Encoding

Abigail McClain Gomez

PIRSA:23060030

June 12, 2023

Condensed Matter

Dimension reduction of the Functional Renormalization Group

Jiawei Zang

PIRSA:23060031

June 12, 2023

Condensed Matter

Neural quantum states for simulating strongly interacting fermions in continuous space

Jannes Nys

PIRSA:23060032

June 12, 2023

Condensed Matter

Automated Characterization of Engineered Quantum Materials

Eliska Greplova Delft University of Technology

PIRSA:23060033

June 13, 2023

Condensed Matter

Learning Feynman Diagrams with Tensor Trains

Xavier Waintal CEA Grenoble

PIRSA:23060034

June 13, 2023

Condensed Matter

Data-centric learning of Quantum Many-body States with Classical Machines

Eun-Ah Kim Cornell University

PIRSA:23060049

June 13, 2023

Condensed Matter
See all from this collection
QuEra - quantum computing with neutral atoms:

Anna Knörr ETH Zurich
PIRSA:22110095

November 23, 2022
Condensed Matter
Gibbs Sampling of Periodic Potentials on a Quantum Computer

Arsalan Motamedi University of Waterloo
PIRSA:22110094

November 23, 2022
Condensed Matter
Adaptive Quantum State Tomography with Active Learning

Hannah Lange
PIRSA:22110092

November 23, 2022
Condensed Matter
Representing quantum states with spiking neural networks
- Stefanie Czischek University of Ottawa
- Stefanie Czischek
PIRSA:22110090

November 23, 2022
Condensed Matter
Quantum hypernetworks

Juan Carrasquilla Vector Institute for Artificial Intelligence
PIRSA:22110089

November 22, 2022
Condensed Matter
A Study of Neural Network Field Theories

Anindita Maiti Perimeter Institute for Theoretical Physics
PIRSA:22110088

November 22, 2022
Condensed Matter
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Friederike Metz IBM (United States)
PIRSA:22110087

November 22, 2022
Condensed Matter
Matchgate Shadows for Fermionic Quantum Simulation
- Kianna Wan
- Kianna Wan
PIRSA:22110086

November 22, 2022
Condensed Matter
Towards an artificial Muse for new ideas in Quantum Physics

Mario Krenn Max Planck Institute for the Science of Light
PIRSA:22110085

November 22, 2022
Condensed Matter
Quantum adiabatic speedup on a class of combinatorial optimization problems
- Madelyn Cain Harvard University
- Madelyn Cain
PIRSA:22110083

November 22, 2022
Condensed Matter
Unlocking the Universe with quantum materials

Jess McIver University of British Columbia
PIRSA:22110077

November 16, 2022
Condensed Matter

Machine Learning for Quantum Many-Body Systems

21 talks

June 12, 2023 - June 16, 2023

Collection Number C23002

PIRSA

Machine learning techniques are rapidly being adopted into the field of quantum many-body physics, including condensed matter theory, experiment, and quantum information science. The steady increase in data being produced by highly-controlled quantum experiments brings the potential of machine learning algorithms to the forefront of scientific advancement. Particularly exciting is the prospect of using machine learning for the discovery and design of molecules, quantum materials, synthetic matter, and computers. In order to make progress, the field must address a number of fundamental questions related to the challenges of studying many-body quantum mechanics using classical computing algorithms and hardware.

The goal of this conference is to bring together experts in computational physics, machine learning, and quantum information, to make headway on a number of related topics, including:
- Data-drive quantum state reconstruction
- Machine learning strategies for quantum error correction and quantum control
- Neural-network inspired wavefunctions
- Near-term prospects for data from quantum devices
- Machine learning for quantum algorithm discovery

pirsa.org/C23002

Territorial Land Acknowledgement

Perimeter Institute acknowledges that it is situated on the traditional territory of the Anishinaabe, Haudenosaunee, and Neutral peoples.

Perimeter Institute is located on the Haldimand Tract. After the American Revolution, the tract was granted by the British to the Six Nations of the Grand River and the Mississaugas of the Credit First Nation as compensation for their role in the war and for the loss of their traditional lands in upstate New York. Of the 950,000 acres granted to the Haudenosaunee, less than 5 percent remains Six Nations land. Only 6,100 acres remain Mississaugas of the Credit land.

We thank the Anishinaabe, Haudenosaunee, and Neutral peoples for hosting us on their land.
Talk

Welcome and Opening Remarks

Roger Melko University of Waterloo

PIRSA:23060027

June 12, 2023

Condensed Matter

[Virtual] Exploring Quantum Science with Machine Learning

Di Luo Massachusetts Institute of Technology (MIT)

PIRSA:23060028

June 12, 2023

Condensed Matter

Near Term Distributed Quantum Computation using Optimal Auxiliary Encoding

Abigail McClain Gomez

PIRSA:23060030

June 12, 2023

Condensed Matter

Dimension reduction of the Functional Renormalization Group

Jiawei Zang

PIRSA:23060031

June 12, 2023

Condensed Matter

Neural quantum states for simulating strongly interacting fermions in continuous space

Jannes Nys

PIRSA:23060032

June 12, 2023

Condensed Matter

Automated Characterization of Engineered Quantum Materials

Eliska Greplova Delft University of Technology

PIRSA:23060033

June 13, 2023

Condensed Matter

Learning Feynman Diagrams with Tensor Trains

Xavier Waintal CEA Grenoble

PIRSA:23060034

June 13, 2023

Condensed Matter

Data-centric learning of Quantum Many-body States with Classical Machines

Eun-Ah Kim Cornell University

PIRSA:23060049

June 13, 2023

Condensed Matter
See all from this collection
QuEra - quantum computing with neutral atoms:

Anna Knörr ETH Zurich
PIRSA:22110095

November 23, 2022
Condensed Matter
QuEra is a quantum computing start up located in Boston, spinning off from the groups in the physics and engineering departments of Harvard and MIT. I have spent this fall working at QuEra and will introduce you to the company and its neutral-atom quantum computing technology.
Gibbs Sampling of Periodic Potentials on a Quantum Computer

Arsalan Motamedi University of Waterloo
PIRSA:22110094

November 23, 2022
Condensed Matter
"Motivated by applications in machine learning, we present a quantum algorithm for Gibbs sampling from continuous real-valued functions defined on high dimensional tori. We show that these families of functions satisfy a Poincare inequality. We then use the techniques for solving linear systems and partial differential equations to design an algorithm that performs zeroeth order queries to a quantum oracle computing the energy function to return samples from its Gibbs distribution. We further analyze the query and gate complexity of our algorithm and prove that the algorithm has a polylogarithmic dependence on approximation error (in total variation distance) and a polynomial dependence on the number of variables, although it suffers from an exponentially poor dependence on temperature."
Adaptive Quantum State Tomography with Active Learning

Hannah Lange
PIRSA:22110092

November 23, 2022
Condensed Matter
Representing quantum states with spiking neural networks
- Stefanie Czischek University of Ottawa
- Stefanie Czischek
PIRSA:22110090

November 23, 2022
Condensed Matter
Quantum hypernetworks

Juan Carrasquilla Vector Institute for Artificial Intelligence
PIRSA:22110089

November 22, 2022
Condensed Matter
A Study of Neural Network Field Theories

Anindita Maiti Perimeter Institute for Theoretical Physics
PIRSA:22110088

November 22, 2022
Condensed Matter
The backbones of modern-day Deep Learning, Neural Networks (NN), define field theories on Euclidean background through their architectures, where field interaction strengths depend on the choice of NN architecture width and stochastic parameters. Infinite width limit of NN architectures, combined with independently distributed stochastic parameters, lead to generalized free field theories by the Central Limit Theorem (CLT). Small and large deviations from the CLT, due to finite architecture width and/or correlated stochastic parameters, respectively give rise to weakly coupled field theories and non-perturbative non-Lagrangian field theories in Neural Networks. I will present a systematic exploration of Neural Network field theories via a dual framework of NN parameters: non-Gaussianity, locality by cluster decomposition, and symmetries are studied without necessitating the knowledge of an action. Such a dual description to statistical or quantum field theories in Neural Networks can have potential implications for physics.
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Friederike Metz IBM (United States)
PIRSA:22110087

November 22, 2022
Condensed Matter
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
Matchgate Shadows for Fermionic Quantum Simulation
- Kianna Wan
- Kianna Wan
PIRSA:22110086

November 22, 2022
Condensed Matter
In this talk, I'll describe new tomographic protocols for efficiently estimating various fermionic quantities, including both local observables (i.e., expectation values of local fermionic operators) and certain global properties (e.g., inner products between an unknown quantum state and arbitrary fermionic Gaussian states). Our protocols are based on classical shadows arising from random matchgate circuits. As a concrete application, they enable us to implement the recently introduced quantum-classical hybrid quantum Monte Carlo algorithm, without the exponential post-processing cost incurred by the original approach.
Towards an artificial Muse for new ideas in Quantum Physics

Mario Krenn Max Planck Institute for the Science of Light
PIRSA:22110085

November 22, 2022
Condensed Matter
Quantum adiabatic speedup on a class of combinatorial optimization problems
- Madelyn Cain Harvard University
- Madelyn Cain
PIRSA:22110083

November 22, 2022
Condensed Matter
"One of the central challenges in quantum information science is to design quantum algorithms that outperform their classical counterparts in combinatorial optimization. In this talk, I will describe a modification of the quantum adiabatic algorithm (QAA) [1] that achieves a Grover-type speedup in solving a wide class of combinatorial optimization problem instances. The speedup is obtained over classical Markov chain algorithms including simulated annealing, parallel tempering, and quantum Monte Carlo. I will then introduce a framework to predict the relative performance of the standard QAA and classical Markov chain algorithms, and show problem instances with quantum speedup and slowdown. Finally, I will apply this framework to interpret results from a recent Rydberg atom array experiment [2], which suggest a superlinear speedup in solving the Maximum Independent Set problem on unit-disk graphs. [1] Farhi et al. (2001) Science 292, 5516 [2] Ebadi et al. (2022) Science 376, 6598"
Unlocking the Universe with quantum materials

Jess McIver University of British Columbia
PIRSA:22110077

November 16, 2022
Condensed Matter
Just seven years after their first detection, gravitational waves (GWs) have revealed the first glimpses of a previously hidden dark Universe. Using the GW signature of distant compact-object collisions, we have discovered a new population of stellar remnants and unlocked new tests of general relativity, cosmology, and ultra-dense matter. Materials with low mechanical loss (and strong constraints on other properties, e.g. reflectivity) are integral to the design and success of the GW detectors making these groundbreaking measurements. I'll summarize recent results from LIGO-Virgo and their wide-reaching implications, and discuss quantum materials advances required to enable future ground-based gravitational wave detectors, including Cosmic Explorer, to sense black hole collisions all the way back to the dawn of cosmic time.

Title	Speaker(s)	Date	Talk Type	Info link
Machine Learning for Quantum Many-Body Systems				View details
QuEra - quantum computing with neutral atoms:	Anna Knörr	2022-11-23	Conference	View details
Gibbs Sampling of Periodic Potentials on a Quantum Computer	Arsalan Motamedi	2022-11-23	Conference	View details
Adaptive Quantum State Tomography with Active Learning	Hannah Lange	2022-11-23	Conference	View details
Representing quantum states with spiking neural networks	Stefanie Czischek Stefanie Czischek	2022-11-23	Conference	View details
Quantum hypernetworks	Juan Carrasquilla	2022-11-22	Conference	View details
A Study of Neural Network Field Theories	Anindita Maiti	2022-11-22	Conference	View details
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks	Friederike Metz	2022-11-22	Conference	View details
Matchgate Shadows for Fermionic Quantum Simulation	Kianna Wan Kianna Wan	2022-11-22	Conference	View details
Towards an artificial Muse for new ideas in Quantum Physics	Mario Krenn	2022-11-22	Conference	View details
Quantum adiabatic speedup on a class of combinatorial optimization problems	Madelyn Cain Madelyn Cain	2022-11-22	Conference	View details
Unlocking the Universe with quantum materials	Jess McIver	2022-11-16	Conference	View details

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