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Quantum Techniques in Machine Learning (QTML conference 2023)

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Kristina Gunne/CERN
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500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium

CERN

400
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Description

Some pictures taken during the event can be found at https://cds.cern.ch/record/2881086?ln=de

The registration has reached its maximum capacity. If you wish to be added to the waiting list, please contact QTML-logistics@cern.ch. Please note that the conference will be webcast.

Programme

The programme will start on Sunday 19th of November at 10.30 AM with a tutorial which will go on until 6 PM in two parallel sessions.

On Monday the 20th of November we kick off the QTML conference at 8:45.

The daily programme starts at 9 am (except on Monday!). At 1.15 PM  we break for lunch, and resume the programme from 2.45 PM to 6.30 PM (Depending on the days!). During the week we will organise two poster sessions, one on Tuesday 21 November and the second one on Thursday 23 November. The conference will end on Friday 24 November in the afternoon.

A conference dinner is organised on Wednesday evening for those who signed up for it.

The full online programme is available online!

 

THANK YOU to our sponsors!

 

 

Bus 68_Direction CERN.pdf
Bus line 68 direction France.pdf
Dinner.pdf
Dinner.pptx
Map_QTML_CERN.pdf
QTML local organising team
Registration
Registration fondue Wed 22 Nov
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Registration form_invited speakers
Registration form QTML delegates
Webcast
There is a live webcast for this event
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  • Sunday 19 November
    • 1
      ADVANCE TUTORIAL: Learning theory for quantum machines 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      In this tutorial, I will cover recent advances in developing learning theory for quantum machines. The tutorial will focus on the basic techniques for establishing prediction guarantees in quantum machine learning models and the fundamental ideas for proving the advantages of quantum machines over classical machines in learning from experiments.

      Speakers: Hsin-Yuan Huang (Google Quantum AI, MIT), Zoe Holmes (EPFL)
      QTML-Tutorial.pdf
    • BASIC TUTORIAL for Beginner (Part I): TUTORIAL for Beginner (Part I) 61/1-009 - Room C

      61/1-009 - Room C

      CERN

      22
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      Speaker: Elisa Bahumer

      Conveners: Elisa Bäumer, Dr Michele Grossi (CERN)
    • 13:15
      LUNCH Restaurant 1
    • ADVANCE TUTORIAL: Quantum algorithms – what’s quantum complexity theory got to do with it? 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      This tutorial gives a gentle introduction to the crucial interplay between quantum algorithms and quantum complexity theory, with an eye on developments in the Quantum Machine Learning sphere. We begin with basic complexity classes such as BQP, followed by the HHL algorithm for its complete problem, Matrix Inversion. We then discuss how the Quantum Singular Value Transform (QSVT) significantly generalizes HHL to general quantum algorithms for Linear Algebra. Finally, as time permits, we discuss the other side of the coin – what does it mean to “dequantize” algorithms like the QSVT, and when is it possible?

      Conveners: Prof. Alessandra Di Pierro (University of Verona), Prof. Sevag Gharibian
    • BASIC TUTORIAL for Beginner (Part I): TUTORIAL for beginners (Part II) 61/1-009 - Room C

      61/1-009 - Room C

      CERN

      22
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      Speaker: Elisa Bahumer

      Convener: Elisa Bäumer
  • Monday 20 November
    • 3
      OPENING 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      OPENING
      Speaker: Michele Grossi & Alberto Di Meglio

      Speakers: Alberto Di Meglio (CERN), Dr Michele Grossi (CERN)
      CERN QTI QTML 2023.pdf
      CERN QTI QTML 2023.pptx
      D0D8F897-D386-450E-9A1E-01BD73426EE4.jpeg
      Grossi_QTML_intro.pdf
    • 4
      KEYNOTE: A General Message Belief Propagation Framework for Quantum Computations 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      The core computational tasks in quantum systems are the computation of expectations of operators, including reduced density matrices, and the computation of the ground state energy of a quantum system. Many tools have been developed in the literature to achieve this, including Density Functional Theory (DFT), Density Matrix Renormalization Group (DMRG) and other Tensor Network methods, Variational Monte Carlo (VMC) and so on. Recently, some methods based on Machine Learning have also been pioneered such as FermiNet and PauliNet and other Neural Variational methods. In this work we will build a bridge between the rich Machine Learning literature on Loopy Belief Propagation and its generalizations for posterior inference and the above mentioned quantum computational tasks. It was shown recently that LBP can be used to contract Tensor Networks and compute Reduced Density Matrices. Here we generalize this concept to a new class of generalized LBP methods, known as Region Graph BP and as a particular example we implemented TreeEP. We show that a very general framework exists that encompasses both classical LBP and quantum LBP, which can be used to compute expectations as well as ground state energies and states. We hope that this work will encourage cross fertilization between these two fields.

      Joint work with:
      Evgenii Egorov
      Antonio Rotundo
      Ido Niesen
      Roberto Bondesan

      Speaker: Prof. Max Welling (University of Amsterdam)
      QTML23-Geneve_final.pdf
      QTML23-Geneve_final.pptx
      Recording
      Video preview
    • 5
      INVITED TALK: Better than classical? The subtle art of benchmarking quantum models 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Abstract: There is no shortage of quantum machine learning papers observing that a particular quantum model "beats its classical counterparts on real-world datasets". However, the subtlety of choices made in benchmark experiments, the small scale of the models and data, as well as narratives influenced by the commercialisation of quantum technologies carry the danger of a strong positivity bias. To judge the true potential of prominent ideas in quantum machine learning we are conducting one of the first large-scale meta-studies that systematically tests 12 popular supervised quantum models at scale using the PennyLane software framework. This talk gives a sneak peek of some surprising preliminary results, and reveals the technical and conceptual difficulty of robust benchmarking, a skill which deserves more attention in the quantum applications literature.

      Speaker: Dr Nathan Killoran (Xanadu)
      QTML_2023.pdf
      Recording
      Video preview
    • 10:45
      Coffe break 61/1-201 - Pas perdus - Not a meeting room -

      61/1-201 - Pas perdus - Not a meeting room -

      CERN

      10
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    • Quantum Learning and Quantum Advantage 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Prof. Minh Ha Quang (RIKEN Center for Advanced Intelligence Project)
    • 13:15
      LUNCH
    • Quantum Models and Data 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Diego Garcia-Martin (Los Alamos National Laboratory)
    • Generalisation 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Dr Diego Garcia-Martin (Los Alamos National Laboratory)
    • WELCOME: CERN SCIENCE GATEWAY
  • Tuesday 21 November
    • Architectures for QML 1 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Oriel Orphee Moira Kiss (Universite de Geneve (CH))
    • 10:45
      Coffe break 61/1-201 - Pas perdus - Not a meeting room -

      61/1-201 - Pas perdus - Not a meeting room -

      CERN

      10
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    • 25
      INVITED TALK: A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Abstract: Variational quantum computing schemes have received considerable attention due to their high versatility and potential to make practical use of near-term quantum devices. Despite their promise, the trainability of these algorithms can be hindered by barren plateaus (BPs) induced by the expressiveness of the parametrized quantum circuit, the entanglement of the input data, the locality of the observable or the presence of hardware noise. Up to this point, these sources of BPs have been regarded as independent and have been studied only for specific circuit architectures. In this work, we present a general Lie algebraic theory that provides an exact expression for the variance of the loss function of sufficiently deep parametrized quantum circuits, even in the presence of certain noise models. Our results unify under one single framework all aforementioned sources of BPs by leveraging generalized (and subsystem independent) notions of entanglement and operator locality. Finally, our results lead to a critical question: Does the inherent structure that precludes the presence of BPs in a variational model (a requisite for trainability) simultaneously render it classically simulable?

      Speaker: Dr Marco Cerezo (Los Alamos National Laboratory)
      QTML_BPs.pdf
      QTML_BPs.pptx
    • Symmetry and Geometric QML 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Dr Supanut Thanasilp (EPFL)
      • 26
        Equivariant Quantum Models
        Speaker: Martina Larocca (Los Alamos National Laboratory)
        EQM@QTML.pdf
        EQM@QTML.pptx
      • 27
        Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images
        Speaker: Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))
        EQNN_images.pdf
        EQNN_images.pptx
      • 28
        Symmetry-invariant quantum machine learning force fields
        Speaker: Isabel Nha Minh LE (IBM Research Europe - Zurich and Technical University of Munich)
        EQNNFF_QTML_talk_INMLe_splitted.pdf
      • 29
        Homogenous space expressibility of parametrized quantum circuits
        Speaker: Rahul Arvind (Institute for High Performance Computing, A*STAR)
        HomogeneousSpace.pdf
        HomogeneousSpace.pptx
    • 13:15
      LUNCH
    • Trainability of Quantum Architectures 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Dr Martina Larocca (Los Alamos National Laboratory)
      • 30
        Trainability barriers and opportunities in quantum generative modeling
        Speaker: Manuel Rudolph (EPFL)
        Trainability_QCBM_QTML_2023.pdf
      • 31
        On the Sample Complexity of Quantum Boltzmann Machine Learning
        Speaker: Luuk Coopmans (Quantinuum)
        qtml_2023_luuk.pdf
      • 32
        On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks
        Speaker: Dr Christa Zoufal (IBM Quantum Europe)
        TightGradientBounds_BPAbsence_Zoufal23.pdf
      • 33
        Deep quantum neural networks form Gaussian processes
        Speaker: Diego Garcia-Martin (Los Alamos National Laboratory)
        gps.pdf
      • 34
        Here comes the SU(N) multivariate quantum gates and gradients
        Speaker: Roeland WIERSEMA (University of Waterloo & Vector Institute)
        here_comes_the_sun.pdf
    • 35
      Poster Session
  • Wednesday 22 November
  • Thursday 23 November
    • Quantum Algorithms 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Mr Massimiliano Incudini (University of Verona)
    • 10:45
      Coffee Break 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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    • 64
      INVITED TALK: Topics in quantum topological data analysis 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Abstract:
      Although still a relatively niche field in classical machine learning, topological data analysis has raised substantial interest from the perspective of quantum algorithms in the last few years.
      In this talk we will introduce the topic of topological data analysis, and discuss the state-of-art of quantum algorithms for this problem, together with their promises and limitations, possible generalisations and connections to many-body physics.

      Speaker: Vedran Dunjko (Leiden University)
      Recording
      Topics in QTDA.pdf
      Video preview
    • Quantum Kernels 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Dr Christa Zoufal (IBM Quantum Europe)
      • 65
        A Topological Features Based Quantum Kernel
        Speaker: Mr Massimiliano Incudini (University of Verona)
        qtml_2023_topological_kernel.pdf
      • 66
        Expressivity and Generalization Ability of Trace-induced Quantum Kernels
        Speaker: Beng Yee Gan (Centre for Quantum Technologies)
        QTML2023-GanBengYee.pdf
        QTML2023-GanBengYee.pptx
      • 67
        A Multi-Class Quantum Kernel-Based Classifier
        Speaker: Shivani Pillay (University of KwaZulu Natal)
        S Pillay QTML 2023 Talk.pdf
      • 68
        Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
        Speaker: Tak Hur (Yonsei University)
        QTML.pdf
    • 13:15
      LUNCH
    • 69
      INVITED TALK: Accelerating Discovery in Particle Physics with AI 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      The quest to understand the fundamental constituents of the universe is at the heart of particle physics. However, the complexity of particle interactions, the volume of data produced by experiments, and the intricacy of theoretical models present significant challenges to advancements in this field. In recent years, artificial intelligence has emerged as a transformative tool for overcoming these challenges, offering new pathways to accelerate the pace of discovery and fostering a deeper understanding of the fundamental forces of nature. This talk aims to elucidate the pivotal role AI plays in particle physics, from optimizing detector design and operation to analyzing vast datasets and validating theoretical models.

      Speaker: Jennifer Ngadiuba (FNAL)
      MELBOURNE Proposal QTML 2024_REVISED copy.pdf
      MELBOURNE Proposal QTML 2024_REVISED copy.pptx
      QTIworkshop_Ngadiuba.pdf
      Recording
      Video preview
    • QML for Physics 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Dr Sofia Vallecorsa (CERN)
    • 76
      Poster Session
  • Friday 24 November
    • 77
      INVITED TALK: The signal and the noise: learning with random quantum circuits and other agents of chaos 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      What can we quantum-learn in the age of noisy quantum computation? Both more and less than you think. Noise limits our ability to error-mitigate, a term that refers to near-term schemes where errors that arise in a quantum computation are dealt with in classical pre-processing. I present a unifying framework for error mitigation and an analysis that strongly limits the degree to which quantum noise can be effectively `undone' for larger system sizes, and shows that current error mitigation schemes are more or less as good as they can be. After presenting this negative result, I'll switch to discussing how noise can be a friendly foe: non-unital noise, unlike its unital counterparts, surprisingly results in absence of barren plateaus in quantum machine learning.

      Speaker: Dr Yihui QUEK (Harvard University)
      QTML 2023_Quek.pdf
      Recording
      Video preview
    • Gradients and Landscape Theory 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Amira Abbas (University of Amsterdam/QuSoft)
      • 78
        Analyzing variational quantum landscapes with information content
        Speaker: Adrian Perez Salinas (Lorentz Institute - Leiden University)
        analyzing_landscapes_IC_QTML.pdf
      • 79
        The landscape of QAOA Max-Cut Lie algebras
        Speaker: Martin Larocca (Los Alamos National Lab)
        MAXCUT QTML(1).pdf
        MAXCUT QTML(1).pptx
      • 80
        Training robust quantum classifiers based on Lipschitz bounds
        Speaker: Julian Berberich (University of Stuttgart)
        QTML_2023_talk.pdf
        QTML_2023_talk.pptx
      • 81
        Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
        Speaker: Koki Chinzei (Fujitsu Limited)
        QTML2023_kc.pdf
    • 82
      Logistics updates 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Some logistics for Friday 24 November.pdf
      Some logistics for Friday 24 November.pptx
    • 10:50
      Coffee Break 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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    • 83
      INVITED TALK: Approximate Autonomous Quantum Error Correction with Reinforcement Learning 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Quantum error correction will ultimately empower quantum computers to
      leverage their full potential. However, substantial device overhead and
      the need for frequent syndrome measurements, which are themselves
      error-prone, render the demonstration of logical qubits that
      significantly surpass break-even still challenging. Autonomous quantum
      error correction represents a promising alternative, where an engineered
      environment allows to bypass the syndrome measurements. In this talk, I
      show how we use reinforcement learning to search for, and find, bosonic
      code spaces that can surpass break-even under experimentally feasible
      conditions. Bosonic codes are, for instance, available and utilized in
      some of the currently most promising and widespread quantum processors
      based on superconducting qubits. Surprisingly, when we increase the
      search space by relaxing the constraints on ideal quantum error
      correction, we find simple and robust code words that significantly
      surpass break-even while minimizing device overhead. This RL code not
      only reduces device complexity compared to other proposed encodings, but
      also outperforms its competitors in terms of its capability to correct
      errors.

      Speaker: Dr Clemens Gneiting (Riken)
      QTML2023_Gneiting.pdf
      Recording
      Video preview
    • Reinforcement Learning and Robust Learning 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Sofiene Jerbi (Freie Universität Berlin)
      • 84
        Quantum adaptive agents with efficient long-term memories
        Speaker: Thomas Elliott (University of Manchester)
        slides.pdf
      • 85
        Talk cancelled: Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
        Speaker: Friederike Metz (EPFL)
        qtml23.pdf
      • 86
        Quantum machine learning with enhanced adversarial robustness
        Speaker: Maxwell West (The University of Melbourne)
        qtml.pdf
      • 87
        Quantum algorithm for robust optimization via stochastic-gradient online learning
        Speaker: Debbie Huey Chih LIM (University of Latvia)
        QORO (QTML).pdf
    • 13:05
      Lunch 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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    • Quantum Monte Carlo 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Prof. David Windridge (Middlesex University, London, UK)
      • 88
        Quantum Computing Quantum Monte Carlo
        Speaker: Jinzhao Sun (Imperial college)
        Qc-QMC_v2.0.pdf
        Qc-QMC_v2.0.pptx
      • 89
        Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
        Speaker: Koichi Miyamoto (Osaka University)
        QTML_MIYAMOTO.pdf
    • Quantum Optimization 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Prof. David Windridge (Middlesex University, London, UK)
      • 90
        Quantum optimization of Binary Neural Networks
        Speaker: Pietro Torta (SISSA)
        Torta_Pietro_QTML_PDF.pdf
      • 91
        Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
        Speaker: Kostas Blekos (University of Patras)
        QAOA-QTML23.pdf
        QAOA-QTML23.pptx
    • Quantum Reservoir Computing 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      Convener: Prof. David Windridge (Middlesex University, London, UK)
      • 92
        Hybrid quantum-classical reservoir computing for solving chaotic systems
        Speaker: Filip Wudraski (USRA)
        QTML_FW 2.pdf
      • 93
        Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
        Speaker: Chotibut Thiparat (Chulalongkorn University)
        Chotibut_QNGRC_QTML2023.pdf
    • 94
      Closing words 500/1-001 - Main Auditorium

      500/1-001 - Main Auditorium

      CERN

      400
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      QTML 2023 Closing.pdf
      QTML 2023 Closing.pptx
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