QUANTUM MACHINE LEARNING
@
ECML PKDD

Theory, Algorithms and Applications

September 19th, 2022 Grenoble

Topic

Quantum Machine Learning (QML) is an emerging field of research, with fast growth. This research field is largely driven by the desire to develop artificial intelligence that uses quantum technologies to improve the speed and performance of learning algorithms. Strong interdisciplinary collaborations are needed to face the challenges of integrating quantum computation and machine learning and to gain better knowledge in this area. The goal of this workshop is to gather researchers in quantum computing and machine learning together, to discuss achievements, challenges and visions in this field.

The workshop program will be a combination of "hot topic" presentations by world-renowned experts in the field, original and recently published papers and a panel discussion with participants. The program will also include an introductory tutorial to QML for a machine learning audience. This will allow workshop participants to obtain a global perspective of the scope of this area and of the technical challenges associated with it, in a participative and interactive manner.

Schedule

* Times below are UTC+2

9:00 - 9:10 am Opening remarks
9:10 - 9:50 am Keynote 1 by Francesco Petruccione
9:50 - 10:30 am Keynote 2 by Ronald de Wolf
10:30 - 11:30 am Poster session* and coffe break
11:30 - 12:10 am Keynote 3 by Bob Coecke
12:10 - 12:50 am Keynote 4 by Elham Kashefi Armando Angrisani
12:50 - 13:00 am Closing remarks

Canceled: The talk by Iordanis Kerenidis (QC Ware and CNRS) has been canceled.

* Accepted posters:

Multiclass SVM with Quantum Annealing. Amer Delilbasic, Gabriele Cavallaro, Bertrand Le Saux, Morris Riedel and Kristel Michielsen
Quantum Circuit Evolution on NISQ Devices. Lukas Franken, Bogdan Georgiev, Sascha Mücke, Moritz Wolter, Raoul Heese, Christian Bauckhage and Nico Piatkowski
Monte Carlo Qubit Routing. Alexis Hummel and Tristan Cazenave
Simulating Quantum Circuits using the Multi-scale Entanglement Eenormalization Ansatz. Ilia Luchnikov, Aleksandr Berezutskii and Alexey Fedorov
Classical and Quantum Algorithms for Orthogonal Neural Networks. Jonas Landman, Natansh Mathur and Iordanis Kerenidis
Combining Variational Quantum Classifiers with Incremental Learning. Corrado Loglisci and Donato Malerba
Characterization of Variational Quantum Algorithms using Free Fermions. Gabriel Matos, Chris N. Self, Zlatko Papic, Konstantinos Meichanetzidis and Henrik Dreyer
k-Arrangement Quantum Data Loader and Quantum Orthogonal Neural Network. Léo Monbroussou, Alex B. Grilo, Elham Kashefi and Romain Kukla
Unsupervised Strategies for Identifying Optimal Parameters in Quantum Approximate Optimization Algorithm. Charles Moussa, Hao Wang, Thomas Baeck and Vedran Dunjko
Quantum Perceptron Revisited: Computational-Statistical Tradeoffs. Mathieu Roget, Giuseppe Di Molfetta and Hachem Kadri
Rapid Training of Quantum Recurrent Neural Network. Michał Siemaszko, Thomas McDermott, Adam Buraczewski, Bertrand Le Saux and Magdalena Stobińska
Equivariant Quantum Circuits for Learning on Weighted Graphs. Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck and Vedran Dunjko
DisCoPy for the Quantum Computer Scientist. Alexis Toumi, Giovanni de Felice and Richie Yeung

Keynote Speakers

* Listed alphabetically

  • Bob Coecke (Cambridge Quantum): From Quantum Picturalism to Quantum AI

  • Elham Kashefi (CNRS and University of Edinburgh): Noisy Intermediate Scale QML

  • Iordanis Kerenidis (QC Ware and CNRS): Towards Quantum Machine Learning Applications

  • Francesco Petruccione (University of KwaZulu-Natal): Introduction to QML

  • Ronald de Wolf (QuSoft, CWI and University of Amsterdam): Quantum Learning Theory

Call for Contributions

The workshop will welcome submissions in the following formats:

  1. Extended abstracts that report on novel and preliminary ideas. Extended abstracts can be at most 4 pages in LNCS format.
  2. Presentations of relevant work that has recently been published. The submission should in this case only consist of a copy of the paper.

We invite submissions on topics including but not limited to the following:

  • Quantum learning theory
  • Quantum algorithms for machine learning tasks
  • Machine learning for quantum information processing
  • Quantum tensor networks
  • Applications of quantum machine learning

Submission Website: Submissions is via EasyChair.

Dates

  • Submission deadline: June 20, 2022 June 30, 2022
  • Acceptance notification: July 13, 2022
  • Camera-ready deadline: September 11, 2022

Program Committee

  • Alessandra Di Pierro (University of Verona)
  • Cécilia Lancien (CNRS, Institut Fourier Grenoble)
  • Anupam Prakash (QC Ware)
  • Minh Ha Quang (Riken)
  • Anna Scaife (University of Manchester)

Workshop Chairs

  • Giuseppe Di Molfetta

    Aix-Marseille University

    Vedran Dunjko

    Leiden University

    Hachem Kadri

    Aix-Marseille University

    Francesco Petruccione

    University of KwaZulu-Natal