Introduction to quantum computing and quantum annealing

Introduction to quantum computing and quantum annealing

Introduction to quantum computing and quantum annealing

May 9 - May 14, 2019

TRIUMF Auditorium

Olivia Di Matteo
April 27, 2019

Lectures will take place from 12:00 - 1:30pm in the TRIUMF auditorium on May 9, 10, 13, and 14.

Basic quantum mechanics and linear algebra (superposition of states, measurement, spin 1/2 systems, Hamiltonians,eigenvalues/eigenvectors). Experience with Python is helpful for following the demonstrations and playing with the software on your own. No quantum computing experience is necessary.

May 9th/10th: gate model quantum computing
Day 1: Theory

  • Motivation behind quantum computing
  • Mathematical representation of qubits
  • Unitary operations; common quantum gates and universal gate sets; quantum circuits
  • Measurements; measurement bases; projectors
  • Visualizing qubits on the Bloch sphere
  • Multi-qubit systems; tensor products and entanglement
  • Applications: superdense coding, quantum teleportation, Deutsch's algorithm

Day 2: Hardware and applications

  • Overview of major classes of algorithms
  • Visual walk-through of Grover's search algorithm
  • Computational speedups and quantum advantage
  • How do I build a qubit? How do I manipulate it?
  • Curernt state of noisy intermediate-scale quantum devices (NISQ devices): qubit hardware graphs, error
  • rates, technical challenges
  • Full-stack quantum computing'; see the work ow of solving a problem on a quantum computer
  • Overview of Hamiltonian simulation
  • HEP application: Calculating neutrino oscillation probabilities on a quantum processor
  • HEP application: Finding ground states of molecules with the variational quantum eigensolver

May 13th/14th: quantum annealing
Day 1: Theory

  • The adiabatic theorem
  • Equivalence of adiabatic and gate-model quantum computing
  • Simulated annealing and the Ising model; transverse-eld Ising model
  • Quadratic unconstrained binary optimization problems (QUBOs)
  • Equivalence of QUBOs and Ising Hamiltonians
  • D-Wave quantum annealers: what are they? How do they work?
  • Formulation of problems as QUBOs; what kinds of problems have people solved using D-Wave?

Day 2: Hardware and applications

  • Brief overview of superconducting qubit hardware
  • D-Wave topology; the chimera and Pegasus qubit hardware graphs
  • Technological limitations: noise, precision restrictions, problem size and graph minor embeddings
  • Application Solving a simple graph theory problem (hopefully using the actual quantum processor!)
  • Graph embedding; how do I couple two qubits that aren't directly connected?
  • HEP application: classifying Higgs decay signals
  • HEP application: particle tracking with LHC data
  • Brief survey of ideas in quantum machine learning

Software demonstrations
I will be doing demonstrations live, but will also distribute the code and Jupyter notebooks so you can follow along
or play with it afterwards. You will need to install:

  • Python 3 (I recommend the Anaconda distribution since it comes pre-installed with Jupyter and the scientic
  • packages you will need),
  • Qiskit, IBM's software platform, installable from their Github page or via pip,
  • D-Wave's Ocean SDK, installable via pip with instructions here

I recommend doing the installation before the lectures start. If at any point you're stuck, feel free to message me ( or even better, drop by my oce (MOB 284) with your laptop and I will help.

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