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An Introduction to Quantum Computing, Probably An Introduction to Quantum Computing, Probably Washington Seminars Bo Ewald October 17, 2017 Bo Ewald April 4-5, 2018 Company Confidential TOPICS •Introduction to Quantum Computing •D-Wave Quantum Systems •Early Applications •2018 & Beyond •Questions Copyright © D-Wave Systems Inc. 2 RicHard Feynman 1960 1970 1980 1990 2000 2010 2020 Copyright © D-Wave Systems Inc. 3 April 1983 – Richard Feynman’s talk Title: Los Alamos Experience Author: Phyllis K Fisher Page 247 Copyright © D-Wave Systems Inc. 4 WHat is a Quantum Computer? • Exploits quantum mechanical effects • Built with “qubits” rather than “bits” • Operates in an extreme environment • Enables quantum algorithms to solve very hard problems Quantum Processor Copyright © D-Wave Systems Inc. 5 CHaracteristics of Classical Digital Systems Binary Separable Barriers Copyright © D-Wave Systems Inc. 6 Quantum Effects on D-Wave Systems Superposition Entanglement Quantum Tunneling Copyright © D-Wave Systems Inc. 7 Quantum Information Science Quantum key distribution Quantum information processing Quantum Quantum Annealing Topological Quantum Sensor Computing Quantum Gate Model Emerging Communication Quantum Cryptography Topological Ion Trap Copyright © D-Wave Systems Inc. 8 Qubits Being Investigated Copyright © D-Wave Systems Inc. 9 Simulation on IBM Quantum Experience (IBM QX) Preparation Rotation by Readout of singlet state �1 and �2 measurement IBM QX, Yorktown Heights, USA X X-gate: U1 phase-gate: Xȁ0ۧ = ȁ1ۧ U1ȁ0ۧ = ȁ0ۧ, U1ȁ1ۧ = ���ȁ1ۧ Xȁ1ۧ = ȁ0ۧ CNOT gate: C01ȁ0100ۧ = ȁ0100ۧ Hadamard gate: H C01ȁ0110ۧ = ȁ1110ۧ Hȁ0ۧ = ȁ0ۧ + ȁ1ۧ / 2 C01ȁ1100ۧ = ȁ1100ۧ + Hȁ1ۧ = ȁ0ۧ − ȁ1ۧ / 2 C01ȁ1110ۧ = ȁ0110ۧ 10 “Computing with Quantum Knots”* *Graham P. Collins Scientific American 294 2006 pp. 56-63 Copyright © D-Wave Systems Inc. 11 © Kristel Michielsen, Thomas Lippert – Forschungszentrum Jülich 12 (http://www.fz-juelich.de/ias/jsc/EN/Research/ModellingSimulation/QIP/QTRL/_node.html) TOPICS •Introduction to Quantum Computing •D-Wave Quantum Systems •Early Applications •2018 and Beyond •Questions Copyright © D-Wave Systems Inc. 13 Original Simulated Annealing Paper 1950 1960 1970 1980 1990 2000 2010 Copyright © D-Wave Systems Inc. 14 Quantum Annealing Outlined by Tokyo TecH PHYSICAL REVIEW E VOLUME 58, NUMBER 5 NOVEMBER 1998 Quantum annealing in the transverse Ising model Tadashi Kadowaki and Hidetoshi Nishimori Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro-ku, Tokyo 152- 8551, Japan (Received 30 April 1998) We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach. The idea is tested by the transverse Ising model, in which the transverse field is a function of time similar to the temperature in the conventional method. The goal is to find the ground state of the diagonal part of the Hamiltonian with high accuracy as quickly as possible. We have solved the time-dependent Schrödinger equation numerically for small size systems with various exchange interactions. Comparison with the results of the corresponding classical (thermal) method reveals that the quantum annealing leads to the ground state with much larger probability in almost all cases if we use the same annealing schedule. [S1063-651X~98!02910-9] 1960 1970 1980 1990 2000 2010 2020 Copyright © D-Wave Systems Inc. 15 MIT Group Proposes Adiabatic QC 1960 1970 1980 1990 2000 2010 2020 Copyright © D-Wave Systems Inc. 16 Company Background • Founded in 1999 • World’s first quantum computing company • Public customers: – Lockheed Martin/USC – Google/NASA Ames/USRA – Los Alamos National Laboratory – Cybersecurity - 1 – Oak Ridge National Laboratory • Other customer projects done via cloud access • ~150 U.S. patents Copyright © D-Wave Systems Inc. 17 How it Works Copyright © D-Wave Systems Inc. 18 But, It Is Fundamentally Different Than Anything You’ve Ever Done Before! Intel 64 D-Wave Performance (GFLOPS) ~20 (12 cores) 0 Precision (bits) 64 4-5 MIPS ~12,000 (12 cores) 0.01 Instructions 245+ (A-M) 251+ (N-Z) 1 Operating Temp. 67.9° C -273° C Power Cons. 100 w +/- ~0 Devices 4B+ transistors 2000 qubits Maturity 1945-2016 ~1950’s Copyright © D-Wave Systems Inc. 19 D-Wave Container –Faraday Cage - No RF Interference Copyright © D-Wave Systems Inc. 20 System. SHielding • 16 Layers between the quantum chip and the outside world • Shielding preserves the quantum calculation Copyright © D-Wave Systems Inc. 21 . Processor Environment • Cooled to 0.015 Kelvin, 175x colder than interstellar space • Shielded to 50,000× less than Earth’s magnetic field • In a high vacuum: pressure is 10 billion times lower than atmospheric pressure • On low vibration floor 15mK • <25 kW total power consumption – for the next few generations Copyright © D-Wave Systems Inc. 22 D-Wave 2000Q Quantum Processor Copyright © D-Wave Systems Inc. 23 D-Wave Product Generations 10,000 1,000 Number of 100 Qubits 10 1 Copyright © D-Wave Systems Inc. 24 TOPICS •Introduction to Quantum Computing •D-Wave Quantum Systems •Early Applications •2018 and Beyond •Questions Copyright © D-Wave Systems Inc. 25 Mission To help solve the most challenging problems in the multiverse: • Optimization • Machine Learning • Monte Carlo/Sampling • Material Science Copyright © D-Wave Systems Inc. 26 Customer Application Areas • Lockheed/USC ISI • Los Alamos National Laboratory – Software Verification and – Optimization Validation – Machine Learning, Sampling – Optimization – Aeronautics – Software Stack – Performance Characterization & – Simulating Quantum Systems Physics – Other (good) Ideas • Google/NASA Ames/USRA • CS - 1 – Machine Learning – Cybersecurity – Optimization • Oak Ridge National Laboratory – Performance Characterization & Physics – Similar to Los Alamos – Research – Material Science & Chemistry Copyright © D-Wave Systems Inc. 27 D-Wave “Rapid Response” Projects (Stephan Eidenbenz, ISTI) Round 1 (June 2016) Round 2 (December 2016) 1. Preprocessing Methods for Scalable Quantum Annealing 1. Accelerating Deep Learning with Quantum Annealing 2. QA Approaches to Graph Partitioning for Electronic Structure Problems 2. Constrained Shortest Path Estimation 3. Combinatorial Blind Source Separation Using “Ising” 3. D-Wave Quantum Computer as an 4. Rigorous Comparison of “Ising” to Established B-QP Efficient Classical Sampler Solution Methods 4. Efficient Combinatorial Optimization using Quantum Computing Round 3 (January 2017) 5. Functional Topological Particle Padding 1. The Cost of Embedding 6. gms2q—Translation of B-QCQP to 2. Beyond Pairwise Ising Models in D-Wave: Searching for D-Wave Hidden Multi-Body Interactions 7. Graph Partitioning using the D-Wave for 3. Leveraging “Ising” for Random Number Generation Electronic Structure Problems 4. Quantum Interaction of Few Particle Systems Mediated 8. Ising Simulations on the D-Wave QPU by Photons 9. Inferring Sparse Representations for 5. Simulations of Non-local-Spin Interaction in Atomic Object Classification using the Magnetometers on “Ising” Quantum D-Wave 2X machine 6. Connecting “Ising” to Bayesian Inference Image Analysis 10. Quantum Uncertainty Quantification for 7. Characterizing Structural Uncertainty in Models of Physical Models using ToQ.jl Complex Systems 11. Phylogenetics calculations 8. Using “Ising” to Explore the Formation of Global Terrorist Networks Los Alamos National Laboratory Use Case 2016 2017 Total % Combinatorial Optimization 5 5 10 45% Machine Learning, Sampling 2 2 4 18% Understanding Device Physics 2 1 3 14% Software Stack/Embeddings 1 1 2 9% Simulating Quantum Systems 2 2 9% Other (good) Ideas 1 1 5% Total 11 11 22 100% The LANL Rapid Response Project results for 2016 and 2017 are available as PDF’s at: http://www.lanl.gov/projects/national-security-education-center/information-science- technology/dwave/index.php Los Alamos National Laboratory 6/27/2017 ISTI Rapid Response DWave Project (Dan O’Malley): Nonnegative/Binary Matrix Factorization ▪ Low-rank matrix factorization • � ≈ �� where ��,� ≥ 0 and ��,� ∈ {0,1} (1999) • � ≈ � � Nature , ▪ Unsupervised machine-learning application • Learn to represent a face as a linear combination of basis images Image credit: Lee & Seung • Goal is for basis images to correspond to intuitive notions of parts of faces ▪ “Alternating least squares” 1. Randomly generate a binary � 2. Solve � = arg min� ∥ � − �� ∥� classically 3. Solve � = arg min� ∥ � − �� ∥� on the D-Wave 4. Repeat from step 2 ▪ Results • The D-Wave NMF approach results in a sparser � (85% vs. 13%) and denser but more lossy compression than the classical NMF approach • The D-Wave outperforms two state-of-the-art classical codes in a cumulative time-to-target benchmark when a low-to-moderate number of samples are used UNCLASSIFIED Nov. 13, 2017 | 30 ISTI Rapid Response DWave Project (Hristo Djidev): Efficient Combinatorial Optimization ▪ Objectives • Running on larger (Chimera-like) graphs • Develop D-Wave (DW) algorithms for NP-hard o Chimera graph is problems modified by merging a set of randomly Focus: the max clique (MC) problem: selected edges into a vertex o Resulting graphs of sizes upto 1000 are used as inputs probSedges:S3068,SEnergySprobSupperSbound:S3930.5 orig.graph cliqueofmaxsize to MC problem -2 2 Vertices -1 1 Couplers SolutionS: -1 +1 • Study scalability/accuracy issues and ways to o DW beats simulated mitigate them annealing, its classical analogue, by
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