Theoretical Investigations at

Slides:

Advertisements

Similar presentations

Conservation of energy -Energy cannot be created or destroyed-

Advertisements

Research Projects Dr Martin Paul Vaughan available from available from

Nathan Wiebe, Ashish Kapoor and Krysta Svore Microsoft Research ASCR Workshop Washington DC Quantum Deep Learning.

CS 678 –Boltzmann Machines1 Boltzmann Machine Relaxation net with visible and hidden units Learning algorithm Avoids local minima (and speeds up learning)

Adiabatic Evolution Algorithm Chukman So ( ) Steven Lee ( ) December 3, 2009 for CS C191 In this presentation we will talk about the quantum.

Solvation Models. Many reactions take place in solution Short-range effects Typically concentrated in the first solvation sphere Examples: H-bonds,

An evolutionary Monte Carlo algorithm for predicting DNA hybridization Joon Shik Kim et al. (2008) (Fri) Computational Modeling of Intelligence.

CHAPTER 8 A NNEALING- T YPE A LGORITHMS Organization of chapter in ISSO –Introduction to simulated annealing –Simulated annealing algorithm Basic algorithm.

Adiabatic Quantum Computation with Noisy Qubits Mohammad Amin D-Wave Systems Inc., Vancouver, Canada.

Identical Particles In quantum mechanics two electrons cannot be distinguished from each other students have names and can be ‘tagged’ and hence are distinguishable.

Monte Carlo Simulation Methods - ideal gas. Calculating properties by integration.

Lecture # 8 Quantum Mechanical Solution for Harmonic Oscillators

Thermo & Stat Mech - Spring 2006 Class 18 1 Thermodynamics and Statistical Mechanics Statistical Distributions.

Lecture 2110/24/05. Light Emission vs. Absorption Black body.

Schrödinger’s Elephants & Quantum Slide Rules A.M. Zagoskin (FRS RIKEN & UBC) S. Savel’ev (FRS RIKEN & Loughborough U.) F. Nori (FRS RIKEN & U. of Michigan)

Universal Spin Transport in Strongly Interacting Fermi Gases Ariel Sommer Mark Ku, Giacomo Roati, Martin Zwierlein MIT INT Experimental Symposium May 19,

Quantum Communication, Quantum Entanglement and All That Jazz Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical Engineering,

1 IE 607 Heuristic Optimization Simulated Annealing.

Quantum Monte-Carlo for Non-Markovian Dynamics Collaborator : Denis Lacroix Guillaume Hupin GANIL, Caen FRANCE  Exact  TCL2 (perturbation)  TCL4  NZ2.

Amplitude Analysis in GlueX Curtis A. Meyer Carnegie Mellon University.

Aim  to compare our model predictions with the measured (Dubna and GSI) evaporation cross sections for the 48 Ca Pb reactions. Calculations.

DPG, Fragment formation in low energy proton-induced reactions within a hybrid BUU+SMM approach Th. Gaitanos, H. Lenske, U. Mosel Introduction.

The Road to Quantum Computing: Boson Sampling Nate Kinsey ECE 695 Quantum Photonics Spring 2014.

Simulated Annealing.

Advanced methods of molecular dynamics 1.Monte Carlo methods 2.Free energy calculations 3.Ab initio molecular dynamics 4.Quantum molecular dynamics 5.Trajectory.

Adiabatic quantum computation and stoquastic Hamiltonians B.M. Terhal IQI, RWTH Aachen A review of joint work with S. Bravyi, D.P.DiVincenzo and R. Oliveira.

Quantum Convolutional Coding for Distillation and Error Correction Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical.

1 Dorit Aharonov Hebrew Univ. & UC Berkeley Adiabatic Quantum Computation.

Development of Atomic Models

A Study of Error-Correcting Codes for Quantum Adiabatic Computing Omid Etesami Daniel Preda CS252 – Spring 2007.

Collaborations: L. Santos (Hannover) Former members: R. Chicireanu, Q. Beaufils, B. Pasquiou, G. Bismut A.de Paz (PhD), A. Sharma (post-doc), A. Chotia.

Adiabatic Quantum Computation with Noisy Qubits M.H.S. Amin D-Wave Systems Inc., Vancouver, Canada.

Quantum Convolutional Coding Techniques Mark M. Wilde Communication Sciences Institute, Ming Hsieh Department of Electrical Engineering, University of.

Discrete optimisation problems on an adiabatic quantum computer

Copyright© 2012, D-Wave Systems Inc. 1 Quantum Boltzmann Machine Mohammad Amin D-Wave Systems Inc.

Determining the Complexity of the Quantum Adiabatic Algorithm using Monte Carlo Simulations A.P. Young, University of California Santa Cruz

Adiabatic Quantum Computing Josh Ball with advisor Professor Harsh Mathur Problems which are classically difficult to solve may be solved much more quickly.

CSC321: Introduction to Neural Networks and Machine Learning Lecture 17: Boltzmann Machines as Probabilistic Models Geoffrey Hinton.

Self-generated electron glasses in frustrated organic crystals Louk Rademaker (Kavli Institute for Theoretical Physics, Santa Barbara) Leiden University,

1 B3-B1 phase transition in GaAs: A Quantum Monte Carlo Study C N M Ouma 1, 2, M Z Mapelu 1, G. O. Amolo 1, N W Makau 1, and R Maezono 3, 1 Computational.

Quantum Boltzmann Machine

Introduction to Quantum Monte Carlo Methods 2! Claudio Attaccalite.

Modern Atomic Model Sometimes called:

Prabhas Chongstitvatana Chulalongkorn University

QUANTUM COMPUTING: Quantum computing is an attempt to unite Quantum mechanics and information science together to achieve next generation computation.

Statistical-Mechanical Approach to Probabilistic Image Processing -- Loopy Belief Propagation and Advanced Mean-Field Method -- Kazuyuki Tanaka and Noriko.

Example: Fragmentation of argon tetramer after sudden ionisation

Joint Institute for Nuclear Research, Dubna, Russia

T. Gaitanos, H. Lenske, U. Mosel

Programming Quantum Computers

the illusion of the Heisenberg scaling

CSC321: Neural Networks Lecture 19: Boltzmann Machines as Probabilistic Models Geoffrey Hinton.

GEANT4-DNA New physics models …from cell to DNA Christophe Champion

Maria Okuniewski Nuclear Engineering Dept.

Building Quantum Computers

Superconducting qubit for quantum thermodynamics experiments

III. Quantum Model of the Atom (p )

Quantum Model of the Atom

Total Angular Momentum

Quantum Computing: the Majorana Fermion Solution

Expectation-Maximization & Belief Propagation

Chiral Spin States in the Pyrochlore Heisenberg Magnet

Quantum Computing Prabhas Chongstitvatana Faculty of Engineering

Quantum-Classical Hybrid Machine Learning for Gene Regulatory Pathways

Quantum Computing Hakem Alazmi Jhilakshi Sharma Linda Vu.

Neural Network Training

III. Quantum Model of the Atom (p )

Computational approaches for quantum many-body systems

Trotterized time evolution and resulting error on local observables

Presentation transcript:

Theoretical Investigations at D-Wave Mohammad Amin 1

Study spin polaron effect on MRT 1/f Noise & MRT Study spin polaron effect on MRT

Generalize the hybrid open quantum modeling to multi-qubit systems

Many-Body Localization and QA Slow equilibration Paramagnetic Fast many-body localization Ground state Study quantum dynamics near the many-body localization point Excited states

Equilibration and Quantum Speedup Amin, PRA 92, 052323 (2015) Equilibrated probability!!!

Equilibration and Quantum Speedup Bimodal (J=-1, +1 , h=0) Mean residual energy Annealing time (ms) Lowest residual energy Benchmark time to equilibration instead of time to the best solution

QA vs Quantum Monte Carlo Show by a counterexample that this is not true

Quantum Boltzmann Machine Train a Boltzmann machine using quantum Boltzmann distribution (Amin, Andriyash, et al., arXiv:1601.02036) Classical BM Bound gradient D=2 Exact gradient (D is trained) D final = 2.5 Implement different machine learning techniques using QBM