QTML 2024 Conference

24 Nov 2024, 09:00 → 29 Nov 2024, 17:25 Australia/Melbourne

Carrillo Gantner Theatre (University of Melbourne)

Martin Sevior (University of Melbourne), Muhammad Usman (CSIRO/University of Melbourne)

Quantum Techniques in Machine Learning (QTML) is an annual international conference focusing on the interdisciplinary field of quantum technology and machine learning. The goal of the conference is to gather leading academic researchers and industry players to interact through a series of scientific talks focussed on the interplay between machine learning and quantum physics.

26_Nov_2024_updated.csv

26_Nov_2024.xlsx

27_Nov_2024.csv

27_Nov_2024_updated.csv

27_Nov_2024.xlsx

QTML_poster_board_layout.pdf

Sunday, 24 November

11:30 → 12:30 Registration

12:30 → 14:30 Introduction to quantum error mitigation

Speaker: Kristan Temme (IBM Quantum)

TutorialKristan.pdf

12:30 → 14:30 Learning Theory

Speaker: Sergii Strelchuk (University of Warwick)

14:30 → 15:00 Coffee Break

15:00 → 17:00 Of ladders and frames: informationally complete measurements for quantum algorithms

Speaker: Francesco Tacchino (IBM Quantum, IBM Research Europe - Zurich)

Francesco.pdf

15:00 → 17:00 Representation theory for quantum computing

Speaker: Martin Larocca

Monday, 25 November

07:00 → 08:00 Registration

08:00 → 08:30 Welcome: Openning remarks

Convener: Prof. Muhammad Usman (CSIRO and The University of Melbourne)

08:30 → 08:50 Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing

Speaker: Marco Cerezo (Los Alamos National Laboratory)

Does_absence_of_BPs_imply_simulability_ Abstract_QTML.pdf

08:50 → 09:10 Lie-algebraic classical simulations for variational quantum computing

Speaker: Matthew Goh (University of Oxford)

g-sim QTML Abstract.pdf

09:10 → 09:25 Stabilizer Tensor Networks: universal quantum simulator on a basis of stabilizer states

Speaker: Sergi Masot Llima (Barcelona Supercomputing Center)

extended abstract.pdf

09:25 → 09:40 Computing exact moments of local random quantum circuits via tensor networks

Speaker: Paolo Braccia (Los Alamos National Laboratory)

Tensor_Networks_Haar_Averages-2.pdf

09:40 → 09:55 On the relation between trainability and dequantization of variational quantum learning models

Speaker: Adrián Pérez-Salinas (Leiden University - Lorentz Institute)

EA_QTML2024.pdf

trainability_and_dequantization-QTML.pdf

09:55 → 10:25 Arbitrary Polynomial Separations in Trainable Quantum Machine Learning

Speaker: Eric Anschuetz (California Institute of Technology)

Eric.pdf

10:30 → 11:00 Coffee Break

11:00 → 11:45 New Approaches for Training Quantum Neural Networks

Speaker: Nathan Wiebe (U Toronto)

Nathan.pdf

11:45 → 12:00 Non-Unitary Quantum Machine Learning

Speaker: Jamie Heredge (University of Melbourne)

QTML_2024_Abstract_Non_Unitary_QML_Heredge.pdf

12:00 → 12:15 Exponential quantum advantages in learning quantum observables from classical data

Speaker: Riccardo Molteni (Leiden University)

QTML_extended_abstract.pdf

12:15 → 12:30 On spectral gaps of locally random one-dimensional nearest-neighbor quantum circuits

Speaker: Andrew Deneris (Los Alamos National Laboratory)

Spectral_gap_abstract.pdf

12:30 → 13:30 Lunch

13:30 → 14:00 Quantum resources of quantum and classical variational methods

Speaker: Eliška Greplová (Delft University of Technology)

Eliska.pdf

14:00 → 14:15 Symmetry breaking in geometric quantum machine learning in the presence of noise

Speaker: Cenk Tüysüz (DESY)

QTML_20241125.pdf

Symmetry breaking in geometric quantum machine learning in the presence of noise.pdf

14:15 → 14:35 Quantum advantage against classical gradient methods for learning over non-uniform distributions

Speaker: Jarrod McClean (Google Quantum AI)

abstract_QTML.pdf

talk_final.pdf

14:35 → 14:50 On the expressivity of embedding quantum kernels

Speaker: Elies Gil-Fuster (Freie Universität Berlin, Fraunhofer HHI Berlin)

EA_QTML2024.pdf

14:50 → 15:05 Quantum linear algebra is all you need for Transformer architectures

Speaker: Naixu Guo (CQT, NUS)

Q algorithm for Transformer (2).pdf

15:05 → 15:20 Concept learning of quantum models from limited measurements

Speaker: Beng Yee Gan (National University of Singapore)

Concept learning of quantum models from limited measurements.pdf

15:20 → 15:50 Coffee Break

19:00 → 22:00 Conference Reception

Tuesday, 26 November

08:00 → 08:30 Registration

08:30 → 09:30 Virtual Panel Session: The future of Quantum Computing

Speakers: Aram Harrow (Massachusetts Institute of Technology), Prof. Barry Sanders (University of Calgary)

09:30 → 10:00 Progress and challenges in quantum feature engineering

Speaker: Kristan Temme (IBM Quantum)

InvitedKristan.pdf

10:00 → 10:15 Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness

Speaker: Chirag Wadhwa (University of Edinburgh)

qtml-nisq-ea.pdf

10:15 → 10:30 Provable Advantage in Quantum PAC Learning

Speaker: Dr Sergii Strelchuk (University of Warwick)

QTML__Extended_Abstract.pdf

10:30 → 11:00 Coffee Break

11:00 → 11:45 Waiting for QML

Speaker: Jens Eisert (FU Berlin)

Jens.pdf

11:45 → 12:00 Learning shallow quantum circuits with many-qubit gates

Shallow quantum circuits lie at the forefront of modern experimental capabilities and theoretical understanding. In this work, we present the first computationally-efficient algorithm for average-case learning of a class of shallow circuits with many-qubit gates. Namely, we provide a quasipolynomial sample and time complexity algorithm for learning 1/poly(n)-approximate unitaries of QAC^0 circuits, i.e., constant-depth circuits with arbitrary single-qubit gates and polynomially many CZ gates of un-bounded width. Note that QAC^0 implements circuits requiring linear-depth in the 1D geometry and
logarithmic-depth in the all-to-all-connected geometry of constant-width gates. Furthermore, leveraging our learned QAC0 unitaries, we provide an efficient algorithm for circuit synthesis of poly-logarithmic depth QAC circuits, making progress towards a quasipolynomial proper-learning algorithm for QAC0.

Speaker: Francisca Vasconcelos (UC Berkeley)

learning_shallow_circuits.pdf

12:00 → 12:15 Learning Quantum Processes with Quantum Statistical Queries

Speaker: Mina Doosti (University of Edinburgh)

qtml-qpsq-EA.pdf

12:15 → 12:30 Quantum topological data analysis: learning graph properties from thermal states

Speaker: Chukwudubem Umeano (University of Exeter)

Learning graph properties from ground states.pptx

QTML_2024_abstract__TDA_and_graphs_.pdf

12:30 → 13:30 Lunch

13:30 → 14:00 Interplay between differential equations and quantum machine learning

Speaker: Nana Liu (University of Michigan- Shanghai Jiao Tong University)

nana.pdf

14:00 → 14:15 Group convolution quantum algorithms: an application to PDEs

Speakers: Mr Arsalan Motamedi (University of Waterloo), Ms Grecia Castelazo (Massachusetts Institute of Technology)

Quantum_algorithm_convolution_for_PDEs.pdf

14:15 → 14:30 Tomography-aided Feedback-based Quantum Algorithms for Ground State Calculation on Trapped-Ion Quantum Processors

Speaker: Vamshi Mohan Katukuri

Abstract_QTML_2024__Vamshi_.pdf

14:30 → 14:45 Inference from interference: hidden subgroups as a learning problem

Speaker: Ms Maria Schuld (Xanadu Quantum Technologies)

QTML_24_abstract (1).pdf

14:45 → 15:00 Non-asymptotic Approximation Error Bounds of Parametrized Quantum Circuits

Speaker: Zhan Yu (National University of Singapore)

Nonasymptotic Approximation Error Bounds of PQCs.pdf

15:00 → 15:15 On fundamental aspects of Quantum Extreme Learning Machines and Reservoir Computing

Speaker: Prof. Thiparat Chotibut (Chulalongkorn University)

QRC_conf_submission.pdf

15:15 → 15:35 Learning quantum states of continuous variable systems

Speaker: Jens Eisert (FU Berlin)

QTML_abstract.pdf

16:00 → 17:30 Posters

Wednesday, 27 November

08:00 → 08:30 Registration

08:30 → 09:00 Think Global, Measure Local

Speaker: Chris Ferrie (University of Technology Sydney)

Chris.pdf

09:00 → 09:20 Guided Quantum Compression for High Dimensional Data Classification

Speaker: Vasilis Belis (ETH Zurich)

qtml24_gqc_dimred.pdf

09:20 → 09:35 Quantum Algorithm for Apprenticeship Learning

Speaker: Debbie Lim (University of Latvia)

QAL.pdf

09:35 → 09:50 Quantum algorithms for spectral sums

Speaker: Alessandro Luongo (Centre for Quantum Technologies)

SpeQtral_Sums.pdf

09:50 → 10:05 Quantum Algorithms for the Pathwise Lasso

Speaker: Tushar Vaidya (Nanyang Technological University)

QTML Extended abstract.pdf

10:05 → 10:20 Quantum Neural Networks Diagnostics with Neural Tangent Kernels

Speaker: Dr Francesco Tacchino (IBM Quantum, IBM Research, Zurich)

QTML2024_abstract_QNN_diagnostic_NTK.pdf

10:20 → 10:35 Predicting adaptively chosen observables in quantum systems

Speaker: Jerry Huang (California Institute of Technology)

extended_abstract.pdf

10:35 → 11:00 Coffee Break

11:00 → 11:20 Online learning of quantum processes

Speaker: Dr Sumeet Khatri (Freie Universität Berlin)

Extended_Abstract_QTML_Online_Learning_Processes.pdf

11:20 → 11:35 Robust shallow shadows

Speaker: Renato Mello da Silva Farias (Technology Innovation Institute)

abstract.pdf

11:35 → 11:55 Towards a Rigorous Framework to Analyze Quantum Machine Learning

Speaker: Alexander Nietner (FU-Berlin)

ExtendedAbstract.pdf

11:55 → 12:10 Using variance of gradients to explain quantum phase recognition algorithms

Speaker: Dr Nathan McMahon (Friedrich-Alexander University)

QTML2024_Abstract.pdf

12:10 → 12:25 Learning quantum states and unitaries of bounded gate complexity

Speaker: Laura Lewis (California Institute of Technology; Google Quantum AI)

extended_abstract_QTML.pdf

talk_final.pdf

12:25 → 14:00 Lunch

14:00 → 15:30 Industry Talks: Presentations from Industry

Presentations from Industry

14:00 University of Melbourne

Speaker: Harry Quiney

14:15 CSIRO

Speaker: Chris Value (CSIRO)

14:30 JP Morgan

Speaker: Shouvanik Chakrabarti (JP Morgan)

14:45 Government of Victoria

Speaker: Amanda Caples (Victorian Government (DJSIR))

15:00 Pasqal QPU toward quantum reality

Speaker: Heejeong Jeong (Pasqal)

16:00 → 18:00 Posters

19:00 → 22:00 Conference Dinner

Thursday, 28 November

08:30 → 09:00 Registration

09:00 → 09:30 An Efficient Classical Algorithm for Simulating Short Time 2D Quantum Dynamics

Speaker: Yewei Yuan

xiao.pdf

09:30 → 09:50 Engineered dissipation to mitigate barren plateaus

Speaker: Antonio Sannia (IFISC)

QTML24.pdf

09:50 → 10:05 Adversarial Robustness Guarantees for Quantum Classifiers

Speaker: Max West (Melbourne University)

qtml_ab-1.pdf

10:05 → 10:20 Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping

Speaker: Jacob Biamonte

QTML_NDAR_Abstract.pdf

10:20 → 11:00 Coffee Break

11:00 → 11:30 Exponential Quantum Communication Advantage in Distributed Inference and Learning

Speaker: Dar Gilboa (Google)

Exponential_Quantum_Communication_Advantage_in_Distributed_Learning (14).pdf

11:30 → 11:50 Training-efficient density quantum machine learning

Speaker: Snehal Raj (QC Ware, France. LIP6, CNRS, Sorbonne University)

DensityQNN.pdf

11:50 → 12:10 State reconstruction via a photonic quantum extreme learning machine in the angular momentum of single photons

Speaker: Taira Giordani (Sapienza Università di Roma)

qtml_2024_Melbourne.pdf

12:10 → 12:30 Towards large-scale quantum optimization solvers with few qubits

Speaker: Lucas Borges (TII/UFRJ)

Submission_QTML.pdf

12:30 → 14:00 Lunch

14:00 → 14:30 Quantum Computing for High Energy Physics: state of the art and challenges

Speaker: Sofia Vallecorsa

SophiaV.pdf

14:30 → 14:45 Limitations of measure-first protocols in quantum machine learning

Speaker: Casper Gyurik (Leiden University)

Extended_abstract_QTML.pdf

14:45 → 15:00 Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms

Speaker: Kasidit Srimahajariyapong (Chulalongkorn University)

Kasidit_QTML_abstract.pdf

Kasidit_QTML_appendix.pdf

Kasidit_QTML_reference.pdf

15:00 → 15:15 Classification of the Fashion-MNIST Dataset on a Quantum Computer

Speaker: Kevin Shen

abstract___QTML___2 (1).pdf

15:15 → 15:30 Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics

Speaker: Heitor Casagrande (Okinawa Institute of Science and Technlogy)

MPSLearningOfPCA-3.pdf

15:30 → 16:00 Coffee

16:00 → 16:15 Prospects of Privacy Advantage in Quantum Machine Learning

Speaker: Dr Shouvanik Chakrabarti (JP Morgan)

QTML_2024_Extended_Abstract_Prospects_Of_Privacy_Advantage_QML.pdf

16:15 → 16:30 Drug Discovery with a Quantum Annealing Born Machine

Speaker: Vladimir Vargas Calderón (Zapata AI)

2024_QTML_Abstract_drug.pdf

16:30 → 16:45 Quantum kernel learning for bosonic modes

Speaker: Dr Carolyn Wood (The University of Queensland)

Wood_QTML2024_abstract.pdf

16:45 → 17:00 Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks

Speaker: Koki Chinzei (Quantum Laboratory, Fujitsu Limited)

abstract_QTML2024_v4.pdf

Friday, 29 November

08:30 → 09:00 Registration

09:00 → 09:20 Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, [Huang et al. Science 2022], introduced an approach for learning properties of the ground state of an n-qubit gapped local Hamiltonian H from only poly(n) data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by [Lewis et al. Nature Communications 2024], to log(n) samples when the geometry of the n-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size n, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known beforehand. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments that confirm the improved scaling of our approach compared to earlier results.

Speaker: Marc Wanner (Department of Computer Science and Engineering, Chalmers University of Technology)

Presentation_QTML2024.pdf

09:20 → 09:35 Graybox quantum system identification and control

Speaker: Dr Akram Youssry (RMIT University)

Akram_abstract.pdf

09:35 → 09:50 TetrisCNN: Unsupervised detection of quantum phases and their local order parameters from projective measurements

Speaker: Kacper Cybiński (Faculty of Physics, University of Warsaw)

Abstract_QTML.pdf

Cybinski_TetrisCNN_QTML.pdf

09:50 → 10:05 Classical shadows with symmetries

Speaker: Frederic Sauvage (Quantinuum)

abstract_classical_shadows_with_symmetries.pdf

talk_classical_shadows_with_symmetries.pdf

10:05 → 10:20 Unveiling quantum phase transitions from traps in variational quantum algorithms

Speaker: Chenfeng Cao (Phasecraft)

Machine_learning_for_phase_transitions__Abstract_-4.pdf

10:20 → 10:35 Architectures and random properties of symplectic quantum circuits

Speaker: Dr Diego García-Martín (Los Alamos National Laboratory)

extended_abstract_qtml.pdf

10:35 → 11:00 Coffee Break

11:00 → 11:30 Scalable quantum dynamics compilation with quantum machine learning

Speaker: Lukasz Cincio (Los Alamos National Laboratory)

lukasz.pdf

11:30 → 11:45 Quantum encoder for fixed Hamming-weight subspaces

Speaker: Dr Thiago O. Maciel (Technology Innovation Institute)

qtml_seminar.pdf

11:45 → 12:00 Opportunities and limitations of explaining quantum machine learning

Speaker: Jonas Naujoks (Fraunhofer HHI)

EA_QTML2024.pdf

12:00 → 12:15 Multidimensional Quantum Generative Modeling by Quantum Hartley Transform

Speaker: Hsin-Yu Wu (University of Exeter)

QTML 2024_Hsin-Yu Wu.pdf

12:15 → 12:30 Improving quantum machine learning via quantum refrigerator

Speaker: Nayeli Azucena Rodriguez Briones (Technische Universität Wien)

Improving_QML_quantum_refrigerator.pdf

12:30 → 14:00 Lunch

14:00 → 14:30 Variational quantum algorithms: adaptive and pulse-based approaches

Speaker: Sophia Economou (Virginia Tech)

SophiaE.pdf

14:30 → 14:45 Quantum-Like Contextuality in Large Language Models

Speaker: Kin Ian Lo (University College London (UCL))

LoSadrMans.pdf

14:45 → 15:00 Optimal Universal Quantum Encoding for Statistical Inference

Speaker: Farhad Farokhi (The University of Melbourne)

Paper

Presentation.pdf

15:00 → 15:15 Probabilistic Compression Gadgets

Speaker: Francisca Vasconcelos (UC Berkeley)

Probabilistic_Compression_Gadget_QTML_Abstract.pdf

15:15 → 15:30 Adaptive Quantum magnetometry using Myopic Entropy reduction

Speaker: Julian Greentree (University of Melbourne)

QTML 2024 abstract.pdf

15:30 → 16:00 Coffee Break

16:00 → 16:15 Quantum Enhanced Knowledge Distillation

Speaker: Simone Piperno ((University of Rome "La Sapienza", Italy))

Knowledge_Distillation_QTML_2024.pdf

16:15 → 16:30 Reducing Measurement Costs by Recycling the Hessian in Adaptive Variational Quantum Algorithms

Speaker: Mafalda Ramôa (Virginia Tech, INL, University of Minho, INESC TEC)

QTML_Abstract.pdf

Recycling the Hessian.pptx

16:30 → 16:45 Information compression via hidden subgroup quantum autoencoders

Speakers: Dr Fei Meng (City University of Hong Kong), Prof. Oscar Dahlsten (City University of Hong Kong)

QTML-Extended Abstract.pdf

16:45 → 17:00 Enabling Aerodynamics Simulations with Quantum Neural Networks and Novel Trainable Features Maps

Speaker: Youssef Achari Berrada (BMW Group / FAU)

QTML_2024_extended_abstract.pdf