Quantum Programming Mini Course @ MLPrague 2019

Quantum Programming @ Machine Learning Prague 2019

, 2019

Register: www.MLPrague.com

Join us at MLPrague on the 22nd of February for an information packed day of quantum programming. Find out

  1. What a tech professional needs to know about quantum technology
  2. How and when quantum enhanced computation will change the software industry
  3. What possibilities exist to use quantum enhanced processors today via cloud services

     Learn to write simple quantum enhanced optimization and quantum enhanced machine learning programs!

The course will take a tech professional, such as a software engineer, through all that is needed to get started with quantum programing with IBM’s quantum programming platform, QISKit.  The course will focus on IBMQ and D-Wave Quantum Enhanced Annealers.

The course is lead by Prof Jacob Biamonte, who has taught similar short courses around the world.  The course will be based on Biamonte’s forthcoming book, Quantum Programming, which will be distributed in draft PDF form to those attending.

Schedule. Room 106 at CEVRO Institut, Jungmannova 28/17, Prague 1

9:00 a.m. introduction to embedding problem instances into physical spin systems

  • What is a spin?
  • What are the known techniques for efficient problem reduction into spin systems?
  • Programs and examples of (i) ground state logic gates; (ii) 3 Satisfiability; MIS-Maximum Independent Set will be distributed and analyzed.

10:30 – 11:00 Coffee Break and Discussion 

11:00 – 12:30 Quantum Enhanced Optimization: Annealing versus Gate-Based Minimization (QAOA)

  • Comparing physical annealing processes vs simulated annealing processes
  • Quantum enhanced annealing devices (D-Wave)
  • Mimicking annealing using gate-based approaches (IBMQ)

12:30 – 2:00 p.m. Lunch and Discussion 

2:00 – 15:30 Introduction to quantum algorithms on IBMQ

  • Introduction to quantum circuit (network) notation
  • Finding eigenvalues of operators
  • Phase estimation vs variational quantum eigensolvers

15:30 – 16:00 Coffee Break and Discussion 

16:00 –  17:50 Programming Examples

  • Quantum classifiers (circuits)
  • Putting it all together by example

Synopsis. This course introduces contemporary methods to utilize quantum processors to accelerate computational subtasks. No background in quantum mechanics is assumed. As part of a unified quantum programming framework, we will introduce the core building blocks needed to program two types of quantum devices. (Type I) Quantum enhanced annealers; (Type II) gate-model based devices. We will compare and contrast the use of quantum annealers and gate-model based devices to (i) solve optimization problem instances; (ii) train binary classifiers. Course notes problem sets with solutions as well as compete programming examples will be provided.  Our focus is on IBMQ and D-Wave Annealers.  The examples will be programmed using IBM QISKit.

 

Questions and Answers

Q. Will he work with models of real-world quantum gate devices or theoretical models with no decoherence and no restriction on number of qbits? 
 
A.  “We will only consider quantum programs that can be executed on existing or very near term quantum processors.  There are two classes of such devices.  The first is the quantum enhanced annealer.  This device functions without quantum effects but is enhanced with them.  D-Wave makes prototypes of them.  The next class we consider is the gate-based devices of IBM.  These processors are far from perfect.  They can only execute very short quantum circuits.  The longer the circuit gets, the worse it becomes (it quickly becomes useless).  Quantum circuits describe ‘quantum programs’ so we will only consider what are called, low-depth quantum circuits or in other words, very short quantum subroutines.  As you are aware, current processors are subject to noise errors and imperfections: the point of our approach is to consider what can practically be done with such devices.”

Leave a Reply

Your email address will not be published. Required fields are marked *