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Quantum Techniques in Machine Learning (QTML conference 2023)
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500/1-001 - Main Auditorium (CERN)
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!
QTML local organising team
Registration
Registration fondue Wed 22 Nov
Registration form_industry session
Registration form_invited speakers
Registration form QTML delegates
Webcast
There is a live webcast for this event
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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) -
10:30
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13:15
Speaker: Elisa Bahumer
Conveners: Elisa Bäumer, Dr Michele Grossi (CERN) -
13:15
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14:45
LUNCH Restaurant 1 1h 30m
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14:45
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18:00
ADVANCE TUTORIAL: Quantum algorithms – what’s quantum complexity theory got to do with it?¶ 500/1-001 - Main Auditorium
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- 14:45
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14:45
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18:00
Speaker: Elisa Bahumer
Convener: Elisa Bäumer
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10:30
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OPENING
Speaker: Michele Grossi & Alberto Di MeglioSpeakers: Alberto Di Meglio (CERN), Dr Michele Grossi (CERN) -
09:00
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10:00
KEYNOTE: A General Message Belief Propagation Framework for Quantum Computations¶ 1h 500/1-001 - Main Auditorium
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 BondesanSpeaker: Prof. Max Welling (University of Amsterdam) -
10:00
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10:45
INVITED TALK: Better than classical? The subtle art of benchmarking quantum models¶ 45m 500/1-001 - Main Auditorium
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) -
10:45
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Coffe break 30m 61/1-201 - Pas perdus - Not a meeting room -
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Convener: Prof. Minh Ha Quang (RIKEN Center for Advanced Intelligence Project)
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Speaker: Marcel Hinsche (Freie Universität Berlin)
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Speaker: Evan Peters (University of Waterloo/Perimeter)
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Speaker: Enrico Fontana (University of Strathclyde)
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LUNCH 1h 30m
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16:00
Convener: Diego Garcia-Martin (Los Alamos National Laboratory)
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14:45
Speaker: haimeng Zhao (Tsinghua University)
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Speaker: Jarrod Mclean (Google Quantum AI)
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Speaker: Xinbiaong Wha (Wuhan University)
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17:30
Convener: Dr Diego Garcia-Martin (Los Alamos National Laboratory)
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Speaker: Xinbiaong Wha (Wuhan University)
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Speaker: Sofiene Jerbi (Freie Universität Berlin)
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Speaker: Tobias Haug (TII)
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Speaker: Elies Gil-Fuster (reie Universität Berlin, Fraunhofer HHI Berlin)
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16:00
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09:00
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Convener: Oriel Orphee Moira Kiss (Universite de Geneve (CH))
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09:00
Speaker: Pere Mujal (ICFO)
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Speaker: Ben Jadeberg (PASQAL)
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Speaker: Slimane Thabet (PASQAL - Sorbonne University)
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Speaker: Matt Lourens (Stellenbosch University)
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Speaker: Giulio Crognaletti (University of Trieste)
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Speaker: Francesco Scala (Università degli Studi di Pavia)
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Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits¶ 15mSpeaker: Darya Martyniuk (Freie Universitaet Berlin, Fraunhofer Gesellschaft)
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Coffe break 30m 61/1-201 - Pas perdus - Not a meeting room -
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INVITED TALK: A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits¶ 45m 500/1-001 - Main Auditorium
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) -
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Convener: Dr Supanut Thanasilp (EPFL)
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Speaker: Martina Larocca (Los Alamos National Laboratory)
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Speaker: Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))
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Speaker: Isabel Nha Minh LE (IBM Research Europe - Zurich and Technical University of Munich)
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13:00
Speaker: Rahul Arvind (Institute for High Performance Computing, A*STAR)
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LUNCH 1h 30m
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Convener: Dr Martina Larocca (Los Alamos National Laboratory)
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Speaker: Manuel Rudolph (EPFL)
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Speaker: Luuk Coopmans (Quantinuum)
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Speaker: Dr Christa Zoufal (IBM Quantum Europe)
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Speaker: Roeland WIERSEMA (University of Waterloo & Vector Institute)
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Convener: Tobias Haug (TII)
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Speaker: Sofiene Jerbi (Freie Universität Berlin)
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Speaker: Po-wei Huang (Centre for Quantum Technologies)
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Speaker: Pooya Ronagh (University of Waterloo & 1QBit)
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Speaker: Yifei Chen (Institute for Quantum Computing, Baidu)
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Speaker: Aliosha Hamma (Università di Napoli Federico II)
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Speaker: Prof. Natalia Ares (University of Oxford)
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Convener: Prof. Antonio Mandarino (ICTQT - University of Gdansk)
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Speaker: Matias Bilkis (Computer Vision Center)
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Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems¶ 15mSpeaker: Enrico Rinaldi (Quantinuum K. K.)
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Speaker: Simone Roncallo (Università degli studi di Pavia)
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Speaker: Felix Frohnert (Leiden University)
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LUNCH 1h 30m
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Convener: Dr Francesco Tacchino (IBM Quantum)
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Speaker: Slimane Thabet (PASQAL - Sorbonne University)
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Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform¶ 30mSpeaker: Alexia salavrakos (Quandela)
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Convener: Dr Francesco Tacchino (IBM Quantum)
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Speaker: Julien Gacon (IBM EPFL)
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Speaker: Yan Zhu (The University of Hong Kong)
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Speaker: Chengran Yang (Centre for Quantum Technologies, National University of Singapore)
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Speakers: Jonas Landman (University of Edinburgh / QC Ware), Natansh Mathur
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Convener: Dr Michele Grossi (CERN)
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Speaker: Bertrand Le Saux (European Space Agency)
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Speaker: Jarrod McClean (Google AI Quantum)
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Speaker: Masako Yamada
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Speaker: Dr Panagiotis Barkoutsos (PASQAL)
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Convener: Mr Massimiliano Incudini (University of Verona)
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Speaker: Francisca Vasconcelos
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Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits¶ 30mSpeaker: Alessandro Luongo (Centre for Quantum Technologies)
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Speaker: Naomi Mona CHMIELEWSKI (EDF Lab)
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Speaker: Pooya Ronagh (University of Waterloo & 1QBit)
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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) -
12:00
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Convener: Dr Christa Zoufal (IBM Quantum Europe)
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12:00
Speaker: Mr Massimiliano Incudini (University of Verona)
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Speaker: Beng Yee Gan (Centre for Quantum Technologies)
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Speaker: Shivani Pillay (University of KwaZulu Natal)
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LUNCH 1h 30m
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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) -
15:30
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Convener: Dr Sofia Vallecorsa (CERN)
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15:30
Speaker: Vasilis Belis (ETH Zurich (CH))
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Speaker: Dr Lento Nagano (University of Tokyo (JP))
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16:00
Speaker: Jinzhao Sun (Imperial college)
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Speaker: Antonio Mandarino (ICTQT - University of Gdansk)
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Speaker: Gian Gentinetta (EPFL)
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Speaker: Egor Tiunov (Technology Innovation Institute, Abu Dhabi)
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INVITED TALK: The signal and the noise: learning with random quantum circuits and other agents of chaos¶ 30m 500/1-001 - Main Auditorium
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) -
09:30
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10:45
Convener: Amira Abbas (University of Amsterdam/QuSoft)
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09:30
Speaker: Adrian Perez Salinas (Lorentz Institute - Leiden University)
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Speaker: Martin Larocca (Los Alamos National Lab)
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10:00
Speaker: Julian Berberich (University of Stuttgart)
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10:30
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data¶ 15mSpeaker: Koki Chinzei (Fujitsu Limited)
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INVITED TALK: Approximate Autonomous Quantum Error Correction with Reinforcement Learning¶ 45m 500/1-001 - Main Auditorium
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) -
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13:02
Convener: Sofiene Jerbi (Freie Universität Berlin)
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12:00
Speaker: Thomas Elliott (University of Manchester)
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12:30
Talk cancelled: Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks¶ 1mSpeaker: Friederike Metz (EPFL)
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Speaker: Debbie Huey Chih LIM (University of Latvia)
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Convener: Prof. David Windridge (Middlesex University, London, UK)
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14:30
Speaker: Jinzhao Sun (Imperial college)
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15:00
Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration¶ 15mSpeaker: Koichi Miyamoto (Osaka University)
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Convener: Prof. David Windridge (Middlesex University, London, UK)
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Speaker: Pietro Torta (SISSA)
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Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants¶ 15mSpeaker: Kostas Blekos (University of Patras)
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Convener: Prof. David Windridge (Middlesex University, London, UK)
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Speaker: Filip Wudraski (USRA)
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Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics¶ 15mSpeaker: Chotibut Thiparat (Chulalongkorn University)
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