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 talksCollection 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 talksCollection Number C16019Talk


Solitons and SpinCharge 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: HigherThanBallistic 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)

ParticleVortex 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 talksCollection 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 KwaZuluNatal

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 QuantumInspired Tensor Networks
Miles Stoudenmire Flatiron Institute


4 Corners Southwest Ontario Condensed Matter Symposium
9 talksCollection 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 spinorbit coupling
YoungJune 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


Replica topological order in quantum mixed states and quantum error correction
Roger Mong University of Pittsburgh

Quantum Spin Liquid Oasis in Desert States of Unfrustrated Spin Models: Mirage ?
Baskaran Ganapathy Institute of Mathematical Sciences

Machine Learning Lecture
Damian Pope Perimeter Institute for Theoretical Physics


Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics

The Stability of Gapped Quantum Matter and ErrorCorrection 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

PSI 2016/2017  Condensed Matter Review (Vidal)
12 talksCollection Number C17002PSI 2016/2017  Condensed Matter Review (Vidal) 
Low Energy Challenges for High Energy Physicists II
21 talksCollection Number C16019Low Energy Challenges for High Energy Physicists II


4 Corners Southwest Ontario Condensed Matter Symposium
9 talksCollection Number C160074 Corners Southwest Ontario Condensed Matter Symposium 
Replica topological order in quantum mixed states and quantum error correction
Roger Mong University of Pittsburgh
Topological phases of matter offer a promising platform for quantum computation and quantum error correction. Nevertheless, unlike its counterpart in pure states, descriptions of topological order in mixed states remain relatively underexplored. We will give various definitions for replica topological order in mixed states. Similar to the replica trick, our definitions also involve n copies of density matrix of the mixed state. Within this framework, we categorize topological orders in mixed states as either quantum, classical, or trivial, depending on the type of information they encode.
For the case of the toric code model in the presence of decoherence, we associate for each phase a quantum channel and describes the structure of the code space. We show that in the quantumtopological phase, there exists a postselectionbased error correction protocol that recovers the quantum information, while in the classicaltopological phase, the quantum information has decohere and cannot be fully recovered. We accomplish this by describing the mixed state as a projected entangled pairs state (PEPS) and identifying the symmetryprotected topological order of its boundary state to the bulk topology.


Quantum Spin Liquid Oasis in Desert States of Unfrustrated Spin Models: Mirage ?
Baskaran Ganapathy Institute of Mathematical Sciences
Hilbert spaces are incomprehensibly vast and rich. Model Hamiltonians are space ships. They could take us to new worlds, such as cold \textit{spin liquid oasis} in hot regions in Hilbert space deserts. Exact decomposition of isotropic Heisenberg Hamiltonian on a Honeycomb lattice into a sum of 3 noncommuting (permuted) Kitaev Hamiltonians, helps us build a degenerate \textit{manifold of metastable flux free Kitaev spin liquid vacua} and vector Fermionic (Goldstone like) collective modes. Our method, \textit{symmetric decomposition of Hamiltonians}, will help design exotic metastable quantum scars and exotic quasi particles, in nonexotic real systems.
G. Baskaran, arXiv:2309.07119

Machine Learning Lecture
Damian Pope Perimeter Institute for Theoretical Physics


Machine Learning Lecture
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics

The Stability of Gapped Quantum Matter and ErrorCorrection with Adiabatic Noise  VIRTUAL
Ali Lavasani University of California, Santa Barbara
The code space of a quantum errorcorrecting code can often be identified with the degenerate groundspace 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 errorcorrecting 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 groundspace. We further argue that when specific decoders  such as minimumweight perfect matching  are applied to recover this information, an errorcorrecting 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 nonlocal, 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