Condensed matter physics is the branch of physics that studies systems of very large numbers of particles in a condensed state, like solids or liquids. Condensed matter physics wants to answer questions like: why is a material magnetic? Or why is it insulating or conducting? Or new, exciting questions like: what materials are good to make a reliable quantum computer? Can we describe gravity as the behavior of a material? The behavior of a system with many particles is very different from that of its individual particles. We say that the laws of many body physics are emergent or collective. Emergence explains the beauty of physics laws.
Format results
-
12 talks-Collection Number C17002
Talk
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 1
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 2
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 3
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 4
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 5
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 6
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 7
Guifre Vidal Alphabet (United States)
-
PSI 2016/2017 - Condensed Matter (Review) - Lecture 8
Guifre Vidal Alphabet (United States)
-
-
Low Energy Challenges for High Energy Physicists II
21 talks-Collection Number C16019Talk
-
-
Solitons and Spin-Charge Correlations in Strongly Interacting Fermi Gases
Martin Zwierlein Massachusetts Institute of Technology (MIT)
-
Hierarchical growth of entangled states
John McGreevy University of California, San Diego
-
Scaling geometries and DC conductivities
Sera Cremonini LeHigh University
-
Viscous Electron Fluids: Higher-Than-Ballistic Conduction Negative Nonlocal Resistance and Vortices
Leonid Levitov Massachusetts Institute of Technology (MIT) - Department of Physics
-
Universal Diffusion and the Butterfly Effect
Michael Blake Massachusetts Institute of Technology (MIT)
-
Particle-Vortex duality and Topological Quantum Matter
Jeff Murugan Institute for Advanced Study (IAS) - School of Natural Sciences (SNS)
-
TBA
Andrew Mackenzie Max Planck Institute
-
-
Quantum Machine Learning
21 talks-Collection Number C16017Talk
-
-
Comparing Classical and Quantum Methods for Supervised Machine Learning
Ashish Kapoor Microsoft Corporation
-
Classification on a quantum computer: Linear regression and ensemble methods
Maria Schuld University of KwaZulu-Natal
-
Rejection and Particle Filtering for Hamiltonian Learning
Christopher Granade Dual Space Solutions, LLC
-
-
-
Physical approaches to the extraction of relevant information
David Schwab Northwestern University
-
Learning with Quantum-Inspired Tensor Networks
Miles Stoudenmire Flatiron Institute
-
-
4 Corners Southwest Ontario Condensed Matter Symposium
9 talks-Collection Number C16007Talk
-
-
Superconductivity and Charge Density Waves in the Clean 2D Limit
Adam Tsen Institute for Quantum Computing (IQC)
-
Honeycomb lattice quantum magnets with strong spin-orbit coupling
Young-June Kim University of Toronto
-
-
-
Stochastic Resonance Magnetic Force Microscopy: A Technique for Nanoscale Imaging of Vortex Dynamics
Raffi Budakian Institute for Quantum Computing (IQC)
-
Spin Slush in an Extended Spin Ice Model
Jeff Rau University of Waterloo
-
Universal Dynamic Magnetism in the Ytterbium Pyrochlores
Alannah Hallas McMaster University
-
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
The Stability of Gapped Quantum Matter and Error-Correction with Adiabatic Noise - VIRTUAL
Ali Lavasani University of California, Santa Barbara
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
The Ising Model on $S^2$ - The Affine Conjecture
Richard Brower Boston University
-
Efficient Simulation of Quantum Transport in 1D
Frank Pollmann Technical University of Munich (TUM)
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
PSI 2016/2017 - Condensed Matter Review (Vidal)
12 talks-Collection Number C17002PSI 2016/2017 - Condensed Matter Review (Vidal) -
Low Energy Challenges for High Energy Physicists II
21 talks-Collection Number C16019Low Energy Challenges for High Energy Physicists II
-
-
4 Corners Southwest Ontario Condensed Matter Symposium
9 talks-Collection Number C160074 Corners Southwest Ontario Condensed Matter Symposium -
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
The Stability of Gapped Quantum Matter and Error-Correction with Adiabatic Noise - VIRTUAL
Ali Lavasani University of California, Santa Barbara
The code space of a quantum error-correcting code can often be identified with the degenerate ground-space within a gapped phase of quantum matter. We argue that the stability of such a phase is directly related to a set of coherent error processes against which this quantum error-correcting code (QECC) is robust: such a quantum code can recover from adiabatic noise channels, corresponding to random adiabatic drift of code states through the phase, with asymptotically perfect fidelity in the thermodynamic limit, as long as this adiabatic evolution keeps states sufficiently "close" to the initial ground-space. We further argue that when specific decoders -- such as minimum-weight perfect matching -- are applied to recover this information, an error-correcting threshold is generically encountered within the gapped phase. In cases where the adiabatic evolution is known, we explicitly show examples in which quantum information can be recovered by using stabilizer measurements and Pauli feedback, even up to a phase boundary, though the resulting decoding transitions are in different universality classes from the optimal decoding transitions in the presence of incoherent Pauli noise. This provides examples where non-local, coherent noise effectively decoheres in the presence of syndrome measurements in a stabilizer QECC.
---
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
The Ising Model on $S^2$ - The Affine Conjecture
Richard Brower Boston University
A formulation of the 2-dimensional Ising model on a triangulated Riemann sphere is proposed that converges to the exact conformal field theory (CFT) in the continuum limit. The solution is based on reconciling Regge's simplicial geometry for the Einstein Hilbert action with an Affine map to quantum correlators on the tangent plane. Numerical tests of the 2d Ising sphere and radial quantized phi4 theory on $R x S^2$ are presented. Extending the method to more general fields theories on curved manifolds is discussed.
---
-
Efficient Simulation of Quantum Transport in 1D
Frank Pollmann Technical University of Munich (TUM)
Tensor product states are powerful tools for simulating area-law entangled states of many-body systems. The applicability of such methods to the non-equilibrium dynamics of many-body systems is less clear due to the presence of large amounts of entanglement. New methods seek to reduce the numerical cost by selectively discarding those parts of the many-body wavefunction, which are thought to have relatively litte effect on dynamical quantities of interest. We present a theory for the sizes of “backflow corrections”, i.e., systematic errors due to these truncation effects and introduce the dissipation-assisted operator evolution (DAOE) method for calculating transport properties of strongly interacting lattice systems in the high temperature regime. In the DAOE method, we represent the observable as a matrix product operator, and show that the dissipation leads to a decay of operator entanglement, allowing us to capture the dynamics to long times. We benchmark this scheme by calculating spin and energy diffusion constants in a variety of physical models and compare to other existing methods.
---
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
-
Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics