Quantum Machine Learning Cooperative Study Group Meeting at PP-RAI 2023

Quantum Machine Learning is a paradigm of computing that employs quantum computers to perform a variety of learning tasks. It is also an ideal buzz-word. We propose to the willing participants of PP-RAI 2023 to take part in a cooperative study session organized by experts in quantum computing and quantum machine learning. The goal of the session is to familiarize the participants with the basics of quantum computing and quantum machine learning in an interactive and lively way.

There will be no competition, no trophies, no prizes. The tasks will be suggested by the organizers, but the participants are encouraged to determine tasks for themselves. The tasks will be small and trainable on laptops. Bring your own toy datasets and laptops!

We expect the participants to be familiar with python, basic concepts of machine learning and neural networks, and one of deep learning frameworks e.g. pytorch or jax.

You will have a unique opportunity to collaborate with experts in quantum computing and quantum machine learning.


● Piotr Gawron (coordinator),

● Zbigniew Puchała (co-coordinator),

● Łukasz Pawela,

● Bartłomiej Gardas,

● Joanna Jaworek-Korjakowska,

● Mariusz Sterzel


QUBO and quantum annealing

Bartłomiej Gardas and Tomasz Śmierzchalski will introduce computing with D-Wave quantum annealers. Including:

● Setting-up account at D-Wave Leap.

● Using ocean-sdk for solving QUBO problems.

● Encoding real-life-like problems as QUBOs.

● Executing annealing and interpreting the results.


The participants willing to play with quantum annealing are invited to read: 

● Introduction to QUBO: Quadratic unconstrained binary optimization – Wikipedia

If someone is allergic to math, they can instead read: Optimization and QUBOs: A Primer Part 1

● The “What is Quantum Annealing?” article from D-Wave, especially the “Applicable Problems” and “How Quantum Annealing Works in D-Wave QPUs” sections. The quantum physics sections are nice to read but won’t be necessary. What is Quantum Annealing?


Quantum perceptron

Łukasz Pawela will introduce quantum perceptron and its implementation in IBM qiskit:

● Setting up qiskit with local simulators

● Introduction to basic quantum operations

● Implementing circuits in qiskit

● Running on local simulators, remote simulators and quantum hardware

● Result interpretation and visualization

● Implementing a quantum perceptron model

● *Comment on measurement error mitigation

Recommended reading before the session:

Quantum logic gate – Wikipedia

Introduction to Qiskit


Quantum neural networks — variational classifiers

Piotr Gawron and Tomasz Rybotycki will introduce quantum neural networks, and their implementations in the Xanadu’s’ pennylane library

● Quantum devices

● Idea of quantum neural networks

● Implementation of QNN in pennylane

● Training and inference of QNNs


Strongly recommended reading before the session:

Basic tutorial: qubit rotation — PennyLane documentation

Quantum embedding — PennyLane documentation

Variational classifier — PennyLane documentation