Choose timezone
Your profile timezone:
Quantum Techniques in Machine Learning (QTML conference 2023)
→
Europe/Zurich
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
-
-
10:30
→
13:15
ADVANCE TUTORIAL: Learning theory for quantum machines 2h 45m 500/1-001 - Main Auditorium
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
→
13:15
BASIC TUTORIAL for Beginner (Part I): TUTORIAL for Beginner (Part I) 61/1-009 - Room C
Speaker: Elisa Bahumer
Conveners: Elisa Bäumer, Dr Michele Grossi (CERN) -
13:15
→
14:45
LUNCH Restaurant 1 1h 30m
-
14:45
→
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
-
14:45
→
18:00
BASIC TUTORIAL for Beginner (Part I): TUTORIAL for beginners (Part II) 61/1-009 - Room C
Speaker: Elisa Bahumer
Convener: Elisa Bäumer
-
10:30
→
13:15
-
-
08:45
→
09:00
OPENING
Speaker: Michele Grossi & Alberto Di MeglioSpeakers: Alberto Di Meglio (CERN), Dr Michele Grossi (CERN) -
09:00
→
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
→
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
→
11:15
Coffe break 30m 61/1-201 - Pas perdus - Not a meeting room -
-
11:15
→
13:15
Quantum Learning and Quantum Advantage 500/1-001 - Main AuditoriumConvener: Prof. Minh Ha Quang (RIKEN Center for Advanced Intelligence Project)
-
11:15
On quantum backpropagation, information reuse, and cheating measurement collapse 45mSpeaker: Amira Abbas (University of Amsterdam/QuSoft)
-
12:00
Classical Verification of Quantum Learning 30mSpeaker: Marcel Hinsche (Freie Universität Berlin)
-
12:30
Learning bounds and guarantees for testing (quantum) hypotheses 15mSpeaker: Evan Peters (University of Waterloo/Perimeter)
-
12:45
Exponential separations between classical and quantum learners 15mSpeaker: Casper GYURIK (Leiden)
-
13:00
Classical simulations of noisy variational quantum circuits 15mSpeaker: Enrico Fontana (University of Strathclyde)
-
11:15
-
13:15
→
14:45
LUNCH 1h 30m
-
14:45
→
16:00
Quantum Models and Data 500/1-001 - Main AuditoriumConvener: Diego Garcia-Martin (Los Alamos National Laboratory)
-
14:45
Non-IID Quantum Federated Learning with One-shot Communication Complexity 15mSpeaker: haimeng Zhao (Tsinghua University)
-
15:00
Quantum models and data through a precomputation lens 30mSpeaker: Jarrod Mclean (Google Quantum AI)
-
15:30
Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification 30mSpeaker: Xinbiaong Wha (Wuhan University)
-
14:45
-
16:00
→
17:30
Convener: Dr Diego Garcia-Martin (Los Alamos National Laboratory)
-
16:00
Transition role of entangled data in quantum machine learning 30mSpeaker: Xinbiaong Wha (Wuhan University)
-
16:30
The power and limitations of learning quantum dynamics incoherently 30mSpeaker: Sofiene Jerbi (Freie Universität Berlin)
-
17:00
Understanding generalization with quantum geometry 15mSpeaker: Tobias Haug (TII)
-
17:15
Understanding quantum machine learning also requires rethinking generalization 15mSpeaker: Elies Gil-Fuster (reie Universität Berlin, Fraunhofer HHI Berlin)
-
16:00
-
17:30
→
19:00
WELCOME: CERN SCIENCE GATEWAY
-
08:45
→
09:00
-
-
09:00
→
10:45
Architectures for QML 1 500/1-001 - Main AuditoriumConvener: Oriel Orphee Moira Kiss (Universite de Geneve (CH))
-
09:00
Time-series quantum reservoir computing with weak and projective measurements 15mSpeaker: Pere Mujal (ICFO)
-
09:15
Let Quantum Neural Networks Choose Their Own Frequencies 15mSpeaker: Ben Jadeberg (PASQAL)
-
09:30
Extending Graph Transformers with Quantum Computed Aggregation 15mSpeaker: Slimane Thabet (PASQAL - Sorbonne University)
-
09:45
Hierarchical quantum circuit representations for neural architecture search 15mSpeaker: Matt Lourens (Stellenbosch University)
-
10:00
QResNet: a variational entanglement skipping algorithm 15mSpeaker: Giulio Crognaletti (University of Trieste)
-
10:15
A General Approach for Dropout in Quantum Neural Networks 15mSpeaker: Francesco Scala (Università degli Studi di Pavia)
-
10:30
Applying Genetic Algorithms to Optimize the Generalization Ability of Variational Quantum Circuits 15mSpeaker: Darya Martyniuk (Freie Universitaet Berlin, Fraunhofer Gesellschaft)
-
09:00
-
10:45
→
11:15
Coffe break 30m 61/1-201 - Pas perdus - Not a meeting room -
-
11:15
→
12:00
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) -
12:00
→
13:15
Symmetry and Geometric QML 500/1-001 - Main AuditoriumConvener: Dr Supanut Thanasilp (EPFL)
-
12:00
Equivariant Quantum Models 15mSpeaker: Martina Larocca (Los Alamos National Laboratory)
-
12:15
Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images 15mSpeaker: Su Yeon Chang (EPFL - Ecole Polytechnique Federale Lausanne (CH))
-
12:30
Symmetry-invariant quantum machine learning force fields 30mSpeaker: Isabel Nha Minh LE (IBM Research Europe - Zurich and Technical University of Munich)
-
13:00
Homogenous space expressibility of parametrized quantum circuits 15mSpeaker: Rahul Arvind (Institute for High Performance Computing, A*STAR)
-
12:00
-
13:15
→
14:45
LUNCH 1h 30m
-
14:45
→
17:00
Trainability of Quantum Architectures 500/1-001 - Main AuditoriumConvener: Dr Martina Larocca (Los Alamos National Laboratory)
-
14:45
Trainability barriers and opportunities in quantum generative modeling 30mSpeaker: Manuel Rudolph (EPFL)
-
15:15
On the Sample Complexity of Quantum Boltzmann Machine Learning 30mSpeaker: Luuk Coopmans (Quantinuum)
-
15:45
On the Absence of Barren Plateaus in Quantum Generative Adversarial Networks 30mSpeaker: Dr Christa Zoufal (IBM Quantum Europe)
-
16:15
Deep quantum neural networks form Gaussian processes 30mSpeaker: Diego Garcia-Martin (Los Alamos National Laboratory)
-
16:45
Here comes the SU(N) multivariate quantum gates and gradients 15mSpeaker: Roeland WIERSEMA (University of Waterloo & Vector Institute)
-
14:45
-
17:00
→
18:30
Poster Session 1h 30m
-
09:00
→
10:45
-
-
09:00
→
10:45
Quantum Learning and Shadows 500/1-001 - Main AuditoriumConvener: Tobias Haug (TII)
-
09:00
Shadows of quantum machine learning 30mSpeaker: Sofiene Jerbi (Freie Universität Berlin)
-
09:30
Post-Variational Quantum Neural Networks 15mSpeaker: Po-wei Huang (Centre for Quantum Technologies)
-
09:45
Neural–Shadow Quantum State Tomography 30mSpeaker: Pooya Ronagh (University of Waterloo & 1QBit)
-
10:15
Efficient information recovery from Pauli noise via classical shadow 15mSpeaker: Yifei Chen (Institute for Quantum Computing, Baidu)
-
10:30
Learning t-doped stabiliser states 15mSpeaker: Aliosha Hamma (Università di Napoli Federico II)
-
09:00
-
10:45
→
11:15
-
11:15
→
12:00
Speaker: Prof. Natalia Ares (University of Oxford)
-
12:00
→
13:15
Machine Learning for Quantum Science 500/1-001 - Main AuditoriumConvener: Prof. Antonio Mandarino (ICTQT - University of Gdansk)
-
12:00
Machine learning continuously monitored systems: estimating parameters 15mSpeaker: Matias Bilkis (Computer Vision Center)
-
12:15
Deep Learning of Quantum Correlations for Quantum Parameter Estimation of Continuously Monitored Systems 15mSpeaker: Enrico Rinaldi (Quantinuum K. K.)
-
12:30
Channel tomography for quantum noise characterization and mitigation 15mSpeaker: Simone Roncallo (Università degli studi di Pavia)
-
12:45
Explainable Representation Learning of Small Quantum States 30mSpeaker: Felix Frohnert (Leiden University)
-
12:00
-
13:15
→
14:45
LUNCH 1h 30m
-
14:45
→
15:45
Experimental Implementations 500/1-001 - Main AuditoriumConvener: Dr Francesco Tacchino (IBM Quantum)
-
14:45
Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor 30mSpeaker: Slimane Thabet (PASQAL - Sorbonne University)
-
15:15
Variational quantum algorithms implemented on a general-purpose single-photon-based quantum computing platform 30mSpeaker: Alexia salavrakos (Quandela)
-
14:45
-
15:45
→
17:00
Architecture for QML 3 500/1-001 - Main AuditoriumConvener: Dr Francesco Tacchino (IBM Quantum)
-
15:45
Variational Quantum Time Evolution without the Quantum Geometric Tensor 15mSpeaker: Julien Gacon (IBM EPFL)
-
16:00
Quantum Similarity Testing with Convolutional Neural Networks 15mSpeaker: Yan Zhu (The University of Hong Kong)
-
16:15
Dimension reduction in quantum stochastic modelling 15mSpeaker: Chengran Yang (Centre for Quantum Technologies, National University of Singapore)
-
16:30
Quantum Fourier Networks for Solving Parametric PDEs 30mSpeakers: Jonas Landman (University of Edinburgh / QC Ware), Natansh Mathur
-
15:45
-
17:00
→
18:45
Convener: Dr Michele Grossi (CERN)
-
17:00
ESA 15mSpeaker: Bertrand Le Saux (European Space Agency)
-
17:15
Google 15mSpeaker: Jarrod McClean (Google AI Quantum)
- 17:30
- 17:45
-
18:00
IONQ 15mSpeaker: Masako Yamada
- 18:15
-
18:30
PASQAL 15mSpeaker: Dr Panagiotis Barkoutsos (PASQAL)
-
17:00
-
18:45
→
19:00
Walk to Restaurant 2 15m 500/1-001 - Main Auditorium
-
19:00
→
22:30
Social Dinner 504
504
CERN
Restaurant 2 is located in building 504
-
09:00
→
10:45
-
-
09:00
→
10:45
Quantum Algorithms 500/1-001 - Main AuditoriumConvener: Mr Massimiliano Incudini (University of Verona)
-
09:00
Quadratic Speedup in Quantum Zero-Sum Games via Single-Call Mirror-Prox Matrix Methods 30mSpeaker: Francisca Vasconcelos
-
09:30
Constant-depth circuits for Uniformly Controlled Gates and Boolean functions with application to quantum memory circuits 30mSpeaker: Alessandro Luongo (Centre for Quantum Technologies)
-
10:00
Quantum Distance Calculation for ε-Graph Construction 15mSpeaker: Naomi Mona CHMIELEWSKI (EDF Lab)
-
10:15
Gibbs Sampling of Periodic Potentials on a Quantum Computer 30mSpeaker: Pooya Ronagh (University of Waterloo & 1QBit)
-
09:00
-
10:45
→
11:15
-
11:15
→
12:00
INVITED TALK: Topics in quantum topological data analysis 45m 500/1-001 - Main Auditorium
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
→
13:15
Convener: Dr Christa Zoufal (IBM Quantum Europe)
-
12:00
A Topological Features Based Quantum Kernel 15mSpeaker: Mr Massimiliano Incudini (University of Verona)
-
12:15
Expressivity and Generalization Ability of Trace-induced Quantum Kernels 30mSpeaker: Beng Yee Gan (Centre for Quantum Technologies)
-
12:45
A Multi-Class Quantum Kernel-Based Classifier 15mSpeaker: Shivani Pillay (University of KwaZulu Natal)
-
13:00
Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning 15mSpeaker: Tak Hur (Yonsei University)
-
12:00
-
13:15
→
14:45
LUNCH 1h 30m
-
14:45
→
15:30
INVITED TALK: Accelerating Discovery in Particle Physics with AI 45m 500/1-001 - Main Auditorium
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
→
17:00
Convener: Dr Sofia Vallecorsa (CERN)
-
15:30
Quantum anomaly detection in the latent space of proton collision events at the LHC 15mSpeaker: Vasilis Belis (ETH Zurich (CH))
-
15:45
Quantum data learning for quantum simulations in high-energy physics 15mSpeaker: Dr Lento Nagano (University of Tokyo (JP))
-
16:00
Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding 15mSpeaker: Jinzhao Sun (Imperial college)
-
16:15
Detection of quantum phase transitions with quantum machine learning techniques 15mSpeaker: Antonio Mandarino (ICTQT - University of Gdansk)
-
16:30
Simulating dynamics of large quantum systems on small quantum devices using circuit knitting 15mSpeaker: Gian Gentinetta (EPFL)
-
16:45
Complete quantum-inspired framework for simulations of flows past immersed bodies 15mSpeaker: Egor Tiunov (Technology Innovation Institute, Abu Dhabi)
-
15:30
-
17:00
→
18:30
Poster Session 1h 30m
-
09:00
→
10:45
-
-
09:00
→
09:30
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
→
10:45
Gradients and Landscape Theory 500/1-001 - Main AuditoriumConvener: Amira Abbas (University of Amsterdam/QuSoft)
-
09:30
Analyzing variational quantum landscapes with information content 15mSpeaker: Adrian Perez Salinas (Lorentz Institute - Leiden University)
-
09:45
The landscape of QAOA Max-Cut Lie algebras 15mSpeaker: Martin Larocca (Los Alamos National Lab)
-
10:00
Training robust quantum classifiers based on Lipschitz bounds 30mSpeaker: Julian Berberich (University of Stuttgart)
-
10:30
Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data 15mSpeaker: Koki Chinzei (Fujitsu Limited)
-
09:30
-
10:45
→
10:50
Logistics updates 5m 500/1-001 - Main Auditorium
-
10:50
→
11:15
-
11:15
→
12:00
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) -
12:00
→
13:02
Reinforcement Learning and Robust Learning 500/1-001 - Main AuditoriumConvener: Sofiene Jerbi (Freie Universität Berlin)
-
12:00
Quantum adaptive agents with efficient long-term memories 30mSpeaker: Thomas Elliott (University of Manchester)
-
12:30
Talk cancelled: Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks 1mSpeaker: Friederike Metz (EPFL)
-
12:31
Quantum machine learning with enhanced adversarial robustness 15mSpeaker: Maxwell West (The University of Melbourne)
-
12:47
Quantum algorithm for robust optimization via stochastic-gradient online learning 15mSpeaker: Debbie Huey Chih LIM (University of Latvia)
-
12:00
-
13:05
→
14:30
-
14:30
→
15:15
Quantum Monte Carlo 500/1-001 - Main AuditoriumConvener: Prof. David Windridge (Middlesex University, London, UK)
-
14:30
Quantum Computing Quantum Monte Carlo 30mSpeaker: Jinzhao Sun (Imperial college)
-
15:00
Quantum Metropolis-Hastings algorithm with the target distribution calculated by quantum Monte Carlo integration 15mSpeaker: Koichi Miyamoto (Osaka University)
-
14:30
-
15:15
→
15:45
Quantum Optimization 500/1-001 - Main AuditoriumConvener: Prof. David Windridge (Middlesex University, London, UK)
-
15:15
Quantum optimization of Binary Neural Networks 15mSpeaker: Pietro Torta (SISSA)
-
15:30
Performance Analysis and Comparative Study of Quantum Approximate Optimization Algorithm Variants 15mSpeaker: Kostas Blekos (University of Patras)
-
15:15
-
15:45
→
16:15
Quantum Reservoir Computing 500/1-001 - Main AuditoriumConvener: Prof. David Windridge (Middlesex University, London, UK)
-
15:45
Hybrid quantum-classical reservoir computing for solving chaotic systems 15mSpeaker: Filip Wudraski (USRA)
-
16:00
Next Generation Quantum Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics 15mSpeaker: Chotibut Thiparat (Chulalongkorn University)
-
15:45
-
16:15
→
16:30
-
09:00
→
09:30
