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Quantum Techniques in Machine Learning (QTML conference 2023)

19–24 Nov 2023
CERN
Europe/Zurich timezone
There is a live webcast for this event.

QTML local organising team

Contribution List

94 out of 94 displayed
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  1. 95. ADVANCE TUTORIAL: Learning theory for quantum machines
    Hsin-Yuan Huang (Google Quantum AI, MIT), Zoe Holmes (EPFL)
    19/11/2023, 10:30

    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.

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  2. 96. Contribution
    Sevag Gharibian
    19/11/2023, 14:45
  3. 3. OPENING
    Alberto Di Meglio (CERN), Dr Michele Grossi (CERN)
    20/11/2023, 08:45

    OPENING
    Speaker: Michele Grossi & Alberto Di Meglio

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  4. 4. KEYNOTE: A General Message Belief Propagation Framework for Quantum Computations
    Prof. Max Welling (University of Amsterdam)
    20/11/2023, 09:00

    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,...

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  5. 5. INVITED TALK: Better than classical? The subtle art of benchmarking quantum models
    Dr Nathan Killoran (Xanadu)
    20/11/2023, 10:00

    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...

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  6. 20. On quantum backpropagation, information reuse, and cheating measurement collapse
    Amira Abbas (University of Amsterdam/QuSoft)
    20/11/2023, 11:15
  7. 78. Classical Verification of Quantum Learning
    Marcel Hinsche (Freie Universität Berlin)
    20/11/2023, 12:00
  8. 79. Learning bounds and guarantees for testing (quantum) hypotheses
    Evan Peters (University of Waterloo/Perimeter)
    20/11/2023, 12:30
  9. 80. Exponential separations between classical and quantum learners
    Casper GYURIK (Leiden)
    20/11/2023, 12:45
  10. 81. Classical simulations of noisy variational quantum circuits
    Enrico Fontana (University of Strathclyde)
    20/11/2023, 13:00
  11. 10. Non-IID Quantum Federated Learning with One-shot Communication Complexity
    haimeng Zhao (Tsinghua University)
    20/11/2023, 14:45
  12. 11. Quantum models and data through a precomputation lens
    Jarrod Mclean (Google Quantum AI)
    20/11/2023, 15:00
  13. 12. Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
    Xinbiaong Wha (Wuhan University)
    20/11/2023, 15:30
  14. 13. Transition role of entangled data in quantum machine learning
    Xinbiaong Wha (Wuhan University)
    20/11/2023, 16:00
  15. 14. The power and limitations of learning quantum dynamics incoherently
    Sofiene Jerbi (Freie Universität Berlin)
    20/11/2023, 16:30
  16. 15. Understanding generalization with quantum geometry
    Tobias Haug (TII)
    20/11/2023, 17:00
  17. 16. Understanding quantum machine learning also requires rethinking generalization
    Elies Gil-Fuster (reie Universität Berlin, Fraunhofer HHI Berlin)
    20/11/2023, 17:15
  18. 26. Time-series quantum reservoir computing with weak and projective measurements
    Pere Mujal (ICFO)
    21/11/2023, 09:00
  19. 32. Let Quantum Neural Networks Choose Their Own Frequencies
    Ben Jadeberg (PASQAL)
    21/11/2023, 09:15
  20. 62. Extending Graph Transformers with Quantum Computed Aggregation
    Slimane Thabet (PASQAL - Sorbonne University)
    21/11/2023, 09:30
  21. 30. Hierarchical quantum circuit representations for neural architecture search
    Matt Lourens (Stellenbosch University)
    21/11/2023, 09:45
  22. 82. QResNet: a variational entanglement skipping algorithm
    Giulio Crognaletti (University of Trieste)
    21/11/2023, 10:00
  23. 44. A General Approach for Dropout in Quantum Neural Networks
    Francesco Scala (Università degli Studi di Pavia)
    21/11/2023, 10:15
  24. 77. Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits
    Darya Martyniuk (Freie Universitaet Berlin, Fraunhofer Gesellschaft)
    21/11/2023, 10:30
  25. 35. INVITED TALK: A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits
    Dr Marco Cerezo (Los Alamos National Laboratory)
    21/11/2023, 11:15

    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...

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  26. 37. Equivariant Quantum Models
    Martina Larocca (Los Alamos National Laboratory)
    21/11/2023, 12:00
  27. 38. Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images
    Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))
    21/11/2023, 12:15
  28. 36. Symmetry-invariant quantum machine learning force fields
    Isabel Nha Minh LE (IBM Research Europe - Zurich and Technical University of Munich)
    21/11/2023, 12:30
  29. 34. Homogenous space expressibility of parametrized quantum circuits
    Rahul Arvind (Institute for High Performance Computing, A*STAR)
    21/11/2023, 13:00
  30. 40. Trainability barriers and opportunities in quantum generative modeling
    Manuel Rudolph (EPFL)
    21/11/2023, 14:45
  31. 42. On the Sample Complexity of Quantum Boltzmann Machine Learning
    Luuk Coopmans (Quantinuum)
    21/11/2023, 15:15
  32. 41. On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks
    Dr Christa Zoufal (IBM Quantum Europe)
    21/11/2023, 15:45
  33. 43. Deep quantum neural networks form Gaussian processes
    Diego Garcia-Martin (Los Alamos National Laboratory)
    21/11/2023, 16:15
  34. 21. Here comes the SU(N) multivariate quantum gates and gradients
    Roeland WIERSEMA (University of Waterloo & Vector Institute)
    21/11/2023, 16:45
  35. 83. Poster Session
    21/11/2023, 17:00
  36. 45. Shadows of quantum machine learning
    Sofiene Jerbi (Freie Universität Berlin)
    22/11/2023, 09:00
  37. 48. Post-Variational Quantum Neural Networks
    Po-wei Huang (Centre for Quantum Technologies)
    22/11/2023, 09:30
  38. 46. Neural–Shadow Quantum State Tomography
    Pooya Ronagh (University of Waterloo & 1QBit)
    22/11/2023, 09:45
  39. 47. Efficient information recovery from Pauli noise via classical shadow
    Yifei Chen (Institute for Quantum Computing, Baidu)
    22/11/2023, 10:15
  40. 84. Learning t-doped stabiliser states
    Aliosha Hamma (Università di Napoli Federico II)
    22/11/2023, 10:30
  41. 49. INVITED TALK:
    Prof. Natalia Ares (University of Oxford)
    22/11/2023, 11:15
  42. 51. Machine learning continuously monitored systems: estimating parameters
    Matias Bilkis (Computer Vision Center)
    22/11/2023, 12:00
  43. 50. Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems
    Enrico Rinaldi (Quantinuum K. K.)
    22/11/2023, 12:15
  44. 52. Channel tomography for quantum noise characterization and mitigation
    Simone Roncallo (Università degli studi di Pavia)
    22/11/2023, 12:30
  45. 53. Explainable Representation Learning of Small Quantum States
    Felix Frohnert (Leiden University)
    22/11/2023, 12:45
  46. 65. Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
    Slimane Thabet (PASQAL - Sorbonne University)
    22/11/2023, 14:45
  47. 66. Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform
    Alexia salavrakos (Quandela)
    22/11/2023, 15:15
  48. 19. Variational Quantum Time Evolution without the Quantum Geometric Tensor
    Julien Gacon (IBM EPFL)
    22/11/2023, 15:45
  49. 64. Quantum Similarity Testing with Convolutional Neural Networks
    Yan Zhu (The University of Hong Kong)
    22/11/2023, 16:00
  50. 63. Dimension reduction in quantum stochastic modelling
    Chengran Yang (Centre for Quantum Technologies, National University of Singapore)
    22/11/2023, 16:15
  51. 31. Quantum Fourier Networks for Solving Parametric PDEs
    Jonas Landman (University of Edinburgh / QC Ware), Natansh Mathur
    22/11/2023, 16:30
  52. 89. ESA
    Bertrand Le Saux (European Space Agency)
    22/11/2023, 17:00
  53. 90. Google
    Jarrod McClean (Google AI Quantum)
    22/11/2023, 17:15
  54. 91. IBM
    Christa Zoufal (IBM Quantum Europe)
    22/11/2023, 17:30
  55. 92. INTEL
    Gian Giacomo Guerreschi (Intel)
    22/11/2023, 17:45
  56. 97. IONQ
    Masako Yamada
    22/11/2023, 18:00
  57. 93. NASA
    Davide Venturelli (NASA)
    22/11/2023, 18:15
  58. 94. PASQAL
    Dr Panagiotis Barkoutsos (PASQAL)
    22/11/2023, 18:30
  59. 98. Walk to Restaurant 2
    22/11/2023, 18:45
  60. 85. Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods
    Francisca Vasconcelos
    23/11/2023, 09:00
  61. 86. Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits
    Alessandro Luongo (Centre for Quantum Technologies)
    23/11/2023, 09:30
  62. 87. Quantum Distance Calculation for ε-Graph Construction
    Naomi Mona CHMIELEWSKI (EDF Lab)
    23/11/2023, 10:00
  63. 88. Gibbs Sampling of Periodic Potentials on a Quantum Computer
    Pooya Ronagh (University of Waterloo & 1QBit)
    23/11/2023, 10:15
  64. 8. INVITED TALK: Topics in quantum topological data analysis
    Vedran Dunjko (Leiden University)
    23/11/2023, 11:15

    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,...

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  65. 22. A Topological Features Based Quantum Kernel
    Mr Massimiliano Incudini (University of Verona)
    23/11/2023, 12:00
  66. 25. Expressivity and Generalization Ability of Trace-induced Quantum Kernels
    Beng Yee Gan (Centre for Quantum Technologies)
    23/11/2023, 12:15
  67. 24. A Multi-Class Quantum Kernel-Based Classifier
    Shivani Pillay (University of KwaZulu Natal)
    23/11/2023, 12:45
  68. 23. Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
    Tak Hur (Yonsei University)
    23/11/2023, 13:00
  69. 54. INVITED TALK: Accelerating Discovery in Particle Physics with AI
    Jennifer Ngadiuba (FNAL)
    23/11/2023, 14:45

    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...

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  70. 55. Quantum anomaly detection in the latent space of proton collision events at the LHC
    Vasilis Belis (ETH Zurich (CH))
    23/11/2023, 15:30
  71. 56. Quantum data learning for quantum simulations in high-energy physics
    Dr Lento Nagano (University of Tokyo (JP))
    23/11/2023, 15:45
  72. 57. Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding
    Jinzhao Sun (Imperial college)
    23/11/2023, 16:00
  73. 58. Detection of quantum phase transitions with quantum machine learning techniques
    Antonio Mandarino (ICTQT - University of Gdansk)
    23/11/2023, 16:15
  74. 59. Simulating dynamics of large quantum systems on small quantum devices using circuit knitting
    Gian Gentinetta (EPFL)
    23/11/2023, 16:30
  75. 60. Complete quantum-inspired framework for simulations of flows past immersed bodies
    Egor Tiunov (Technology Innovation Institute, Abu Dhabi)
    23/11/2023, 16:45
  76. 61. Poster Session
    23/11/2023, 17:00
  77. 6. INVITED TALK: The signal and the noise: learning with random quantum circuits and other agents of chaos
    Dr Yihui QUEK (Harvard University)
    24/11/2023, 09:00

    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...

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  78. 17. Analyzing variational quantum landscapes with information content
    Adrian Perez Salinas (Lorentz Institute - Leiden University)
    24/11/2023, 09:30
  79. 33. The landscape of QAOA Max-Cut Lie algebras
    Martin Larocca (Los Alamos National Lab)
    24/11/2023, 09:45
  80. 18. Training robust quantum classifiers based on Lipschitz bounds
    Julian Berberich (University of Stuttgart)
    24/11/2023, 10:00
  81. 39. Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
    Koki Chinzei (Fujitsu Limited)
    24/11/2023, 10:30
  82. 100. Logistics updates
    24/11/2023, 10:45
  83. 67. INVITED TALK: Approximate Autonomous Quantum Error Correction with Reinforcement Learning
    Dr Clemens Gneiting (Riken)
    24/11/2023, 11:15

    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...

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  84. 68. Quantum adaptive agents with efficient long-term memories
    Thomas Elliott (University of Manchester)
    24/11/2023, 12:00
  85. 69. Talk cancelled: Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
    Friederike Metz (EPFL)
    24/11/2023, 12:30
  86. 70. Quantum machine learning with enhanced adversarial robustness
    Maxwell West (The University of Melbourne)
    24/11/2023, 12:31
  87. 71. Quantum algorithm for robust optimization via stochastic-gradient online learning
    Debbie Huey Chih LIM (University of Latvia)
    24/11/2023, 12:47
  88. 73. Quantum Computing Quantum Monte Carlo
    Jinzhao Sun (Imperial college)
    24/11/2023, 14:30
  89. 72. Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration
    Koichi Miyamoto (Osaka University)
    24/11/2023, 15:00
  90. 76. Quantum optimization of Binary Neural Networks
    Pietro Torta (SISSA)
    24/11/2023, 15:15
  91. 74. Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants
    Kostas Blekos (University of Patras)
    24/11/2023, 15:30
  92. 28. Hybrid quantum-classical reservoir computing for solving chaotic systems
    Filip Wudraski (USRA)
    24/11/2023, 15:45
  93. 27. Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
    Chotibut Thiparat (Chulalongkorn University)
    24/11/2023, 16:00
  94. 99. Closing words
    24/11/2023, 16:15