QTML 2024 Conference

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

Carillo 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 Conference Delegate Pic.jpg

QTML_Dinner.xlsx

QTML_poster_board_layout.pdf

Sunday, 24 November

11:30 Registration

1 Introduction to quantum error mitigation

Speaker: Kristan Temme (IBM Quantum)

TutorialKristan.pdf

2 Learning Theory

In this tutorial you will learn about mathematical formulations of computational learning theory and its generalization to problems in quantum information and computing. We will discuss basic definitions and problems investigated in this area. This will help you to start your journey in this exciting and high-profile field.

Speaker: Sergii Strelchuk (University of Oxford)

Sergeii Strelchuk YouTube Video

14:30 Coffee Break

3 Of ladders and frames: informationally complete measurements for quantum algorithms

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

Francesco.pdf

Tacchino_POVMs.pdf

4 Representation theory for quantum computing

Speaker: Martin Larocca

Monday, 25 November

07:00 Registration

Welcome: Opening remarks

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

5 Welcome to QTML

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

Opening_Usman.pptx

6 Welcome to the University of Melbourne

Speaker: Prof. Mark Cassidy (University of Melbourne, Deputy Vice Chancelor (Research))

MES_MarkCassidyWelcome.pptx

7 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

QTML_Cerezo.pptx

8 Lie-algebraic classical simulations for variational quantum computing

Speaker: Matthew Goh (University of Oxford)

g-sim QTML Abstract.pdf

gsim slides (QTML).pptx

9 Stabilizer Tensor Networks: universal quantum simulator on a basis of stabilizer states

Speaker: Sergi Masot Llima (Barcelona Supercomputing Center)

extended abstract.pdf

stabilizer TN.pptx

10 Computing exact moments of local random quantum circuits via tensor networks

Speaker: Paolo Braccia (Los Alamos National Laboratory)

qtml_talk_topdf.pdf

Tensor_Networks_Haar_Averages-2.pdf

11 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

12 Arbitrary Polynomial Separations in Trainable Quantum Machine Learning

Speaker: Eric Anschuetz (California Institute of Technology)

Eric.pdf

QTML_Presentation_20241125.pdf

10:30 Coffee Break

13 New Approaches for Training Quantum Neural Networks

Speaker: Nathan Wiebe (U Toronto)

Nathan.pdf

QTML.pptx

14 Non-Unitary Quantum Machine Learning

Speaker: Jamie Heredge (University of Melbourne)

EMERGENCY BACKUP PDF Non-Unitary QML.pdf

Non-Unitary QML (1).pptx

15 Exponential quantum advantages in learning quantum observables from classical data

Speaker: Riccardo Molteni (Leiden University)

QTML_extended_abstract.pdf

QTML_learning_observables_presentation.pdf

16 On spectral gaps of locally random one-dimensional nearest-neighbor quantum circuits

Speaker: Andrew Deneris (Los Alamos National Laboratory)

QTML_presi(2).pptx

Spectral_gap_abstract.pdf

12:30 Lunch

17 Quantum resources of quantum and classical variational methods

Speaker: Eliška Greplová (Delft University of Technology)

Eliska.pdf

QTML2024_Greplova.key

18 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

19 Quantum advantage against classical gradient methods for learning over non-uniform distributions

Speaker: Jarrod McClean (Google Quantum AI)

talk_final.pdf

20 On the expressivity of embedding quantum kernels

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

EGF_on the expressivity of embedding quantum kernels.pdf

21 Quantum linear algebra is all you need for Transformer architectures

Speaker: Naixu Guo (CQT, NUS)

Q algorithm for Transformer (2).pdf

Quantum_linear_algebra_QTML2024.key

Quantum_Transformer_QTML2024.pdf

22 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

QTML 2024.pptx

15:20 Coffee Break

19:00 Conference Reception

Tuesday, 26 November

08:00 Registration

23 Virtual Panel Session: The future of Quantum Computing

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

24 Progress and challenges in quantum feature engineering

Speaker: Kristan Temme (IBM Quantum)

InvitedKristan.pdf

25 Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness

Speaker: Chirag Wadhwa (University of Edinburgh)

qtml-nqpsq-ppt.pdf

26 Provable Advantage in Quantum PAC Learning

Speaker: Dr Sergii Strelchuk (University of Warwick)

QTML__Extended_Abstract.pdf

Salmon_sergii PAC learning.pptx

10:30 Coffee Break

27 Quo Vardis Quantum Machine Learning

Speaker: Jens Eisert (FU Berlin)

Jens.pdf

QTML2024EisertPlenaryTalk.pdf

28 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)

ArXiv Paper Link

Learning_Shallow_Circs_QTML_Talk_withAnimations.pdf

learning_shallow_circuits.pdf

29 Learning Quantum Processes with Quantum Statistical Queries

Speaker: Mina Doosti (University of Edinburgh)

QTML-QPSQ.pdf

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 Lunch

31 Interplay between differential equations and quantum machine learning

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

nana.pdf

QTML2024.pdf

32 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

33 Counterdiabatic inspired higher-order Feedback-based Quantum Algorithms for Ground State preparation

Speaker: Vamshi Mohan Katukuri

Abstract_QTML_2024__Vamshi_.pdf

FBQA_QTML_2024_3.pdf

34 Inference from interference: hidden subgroups as a learning problem

Speaker: Ms Maria Schuld (Xanadu Quantum Technologies)

Maria-QTML.pdf

QTML_24_abstract (1).pdf

35 Non-asymptotic Approximation Error Bounds of Parametrized Quantum Circuits

Speaker: Zhan Yu (National University of Singapore)

Non-asymptotic Approximation Error Bounds of PQC_QTML2024.pptx

Nonasymptotic Approximation Error Bounds of PQCs.pdf

36 On fundamental aspects of Quantum Extreme Learning Machines and Reservoir Computing

Speaker: Thiparat Chotibut (Chulalongkorn University)

QRC_conf_submission.pdf

QRC_QTML2024.pdf

37 Learning quantum states of continuous variable systems

Speaker: Jens Eisert (FU Berlin)

QTML2024EisertPlenaryTalk.key

QTML2024EisertPlenaryTalk.key

QTML_abstract.pdf

QTMLContinuous.key

Posters

Wednesday, 27 November

08:00 Registration

38 Think Global, Measure Local

Speaker: Chris Ferrie (University of Technology Sydney)

qtml24_ferrie.pdf

39 Guided Quantum Compression for High Dimensional Data Classification

Speaker: Vasilis Belis (ETH Zurich)

gqc_qtml24.pdf

qtml24_gqc_dimred.pdf

40 Quantum Algorithm for Apprenticeship Learning

Speaker: Debbie Lim (University of Latvia)

QAL2_new.pdf

41 Quantum algorithms for spectral sums

Speaker: Alessandro Luongo (Centre for Quantum Technologies)

SpeQtral_Sums.pdf

SpeQtral_Sums_xprt.pdf

42 Quantum Algorithms for the Pathwise Lasso

Speaker: Tushar Vaidya (Nanyang Technological University)

QTML Extended abstract.pdf

QTMLlassotalk.pdf

43 Quantum Neural Networks Diagnostics with Neural Tangent Kernels

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

QTML2024_abstract_QNN_diagnostic_NTK.pdf

Tacchino_QTML2024.pdf

44 Predicting adaptively chosen observables in quantum systems

Speaker: Jerry Huang (California Institute of Technology)

extended_abstract.pdf

qtml_presentation.pdf

10:35 Coffee Break

45 Online learning of quantum processes

Speaker: Dr Sumeet Khatri (Freie Universität Berlin)

Extended_Abstract_QTML_Online_Learning_Processes.pdf

SumeetKhatri_QTML_2024.pdf

46 Robust shallow shadows

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

presentation.pdf

47 Towards a Rigorous Framework to Analyze Quantum Machine Learning

Speaker: Alexander Nietner (FU-Berlin)

ExtendedAbstract.pdf

TALK_QTML2024.pdf

48 Using variance of gradients to explain quantum phase recognition algorithms

Speaker: Dr Nathan McMahon (Friedrich-Alexander University)

QTML2024_Abstract.pdf

Using Variance of Gradients To Explain QCNN Phase Recognition - Nathan McMahon.pdf

Using Variance of Gradients To Explain QCNN Phase Recognition - Nathan McMahon.pdf

49 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 Lunch

Industry Talks: Presentations from Industry

Presentations from Industry

50 University of Melbourne

Speaker: Harry Quiney

QTML UoM Quantum Overview HQ.pptx

51 Quantum Technologies research at CSIRO

This presentation will give a brief overview of CSIRO (Australia's National Science Agency) and our work in quantum technologies.

Speaker: Chris Value (CSIRO)

QTOverview.pptx

52 JP Morgan

Speaker: Marco Pistoia (JP Morgan)

53 Government of Victoria

Speaker: Amanda Caples (Victorian Government (DJSIR))

54 Pasqal QPU toward quantum reality

Speaker: Heejeong Jeong (Pasqal)

Pasqal QPU toward quantum reality.pdf

Posters

19:00 Conference Dinner

Thursday, 28 November

08:30 Registration

55 An Efficient Classical Algorithm for Simulating Short Time 2D Quantum Dynamics

Speaker: Xiao Yuan (Peking University)

2DsimulationYuan.pdf

xiao.pdf

56 Engineered dissipation to mitigate barren plateaus

Speaker: Antonio Sannia (IFISC)

QTML24 Antonio Sannia.pptx

QTML24.pdf

57 Adversarial Robustness Guarantees for Quantum Classifiers

Speaker: Max West (Melbourne University)

qtml24.pdf

qtml_ab-1.pdf

10:20 Coffee Break

58 Exponential Quantum Communication Advantage in Distributed Inference and Learning

Speaker: Dar Gilboa (Google)

Exponential_Quantum_Communication_Advantage_in_Distributed_Learning (14).pdf

QTML 2024 - Exponential Quantum Communication Advantage in Inference and Learning.pdf

QTML 2024 - Exponential Quantum Communication Advantage in Inference and Learning.pptx

59 Training-efficient density quantum machine learning

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

DensityQNN.pdf

QTML_Snehal.pptx

60 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

talk_reservoir.pdf

12:30 Lunch

61 Quantum Computing for High Energy Physics: state of the art and challenges

Speaker: Sofia Vallecorsa

CERN QTI Sofia Vallecorsa.pdf

SophiaV.pdf

62 Limitations of measure-first protocols in quantum machine learning

Speaker: Casper Gyurik (Leiden University)

Extended_abstract_QTML.pdf

QTML 2024 new.pdf

63 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

QTML2024_Connecting_phases_of_matter_to_the_flatness_of_the_loss_landscape_in_analog_VQAs.pdf

64 Classification of the Fashion-MNIST Dataset on a Quantum Computer

Speaker: Kevin Shen

abstract___QTML___2 (1).pdf

qtml2024.pptx

65 Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics

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

HeitorPresentation.pptx

MPSLearningOfPCA-3.pdf

15:30 Coffee

66 Prospects of Privacy Advantage in Quantum Machine Learning

Speaker: Jamie Heredge (University of Melbourne)

Heredge_Privacy_QML_Short_Oral.pptx

QTML_2024_Extended_Abstract_Prospects_Of_Privacy_Advantage_QML.pdf

67 Drug Discovery with a Quantum Annealing Born Machine

Speaker: Vladimir Vargas Calderón (Zapata AI (no longer affiliated))

2024_QTML_Abstract_drug.pdf

DrugDiscoveryQTMLNew.key

68 Quantum kernel learning for bosonic modes

Speaker: Dr Carolyn Wood (The University of Queensland)

Wood_KerrKernels_QTML2024.pptx

Wood_QTML2024_abstract.pdf

69 Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks

Speaker: Koki Chinzei (Quantum Laboratory, Fujitsu Limited)

abstract_QTML2024_v4.pdf

qtml2024_chinzei.pptx

Friday, 29 November

08:30 Registration

70 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

Presentation_QTML2024.pptx

71 Graybox quantum system identification and control

Speaker: Dr Akram Youssry (RMIT University)

Akram_abstract.pdf

Akram_QTML2024.pptx

72 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_Backup.pdf

Cybiński_TetrisCNN_QTML.key

73 Classical shadows with symmetries

Speaker: Frederic Sauvage (Quantinuum)

abstract_classical_shadows_with_symmetries.pdf

presentation_classical_shadows_with_symmetries.pptx

talk_classical_shadows_with_symmetries.pdf

74 Unveiling quantum phase transitions from traps in variational quantum algorithms

Speaker: Chenfeng Cao (Phasecraft)

Machine_learning_for_phase_transitions__Abstract_-4.pdf

QPT4.pdf

75 Architectures and random properties of symplectic quantum circuits

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

extended_abstract_qtml.pdf

slides_qtml.pdf

10:35 Coffee Break

76 Scalable quantum dynamics compilation with quantum machine learning

Speaker: Lukasz Cincio (Los Alamos National Laboratory)

abstract.pdf

talk_Lukasz_Cincio.pdf

77 Quantum encoder for fixed Hamming-weight subspaces

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

qtml_seminar-1.pdf

78 Opportunities and limitations of explaining quantum machine learning

Speaker: Jonas Naujoks (Fraunhofer HHI)

EA_QTML2024.pdf

Naujoks_XQML_QTML2024_talk.pdf

79 Multidimensional Quantum Generative Modeling by Quantum Hartley Transform

Speaker: Hsin-Yu Wu (University of Exeter)

QTML 2024_Hsin-Yu Wu_.pdf

QTML2024_Hsin-Yu Wu_talk_pdf.pdf

80 Improving quantum machine learning via quantum refrigerator

Speaker: Nayeli Azucena Rodriguez Briones (Technische Universität Wien)

81 Towards large-scale quantum optimization solvers with few qubits

Speaker: Lucas Borges (TII/UFRJ)

qtml_presentation.pdf

Submission_QTML.pdf

12:50 Lunch

82 Variational quantum algorithms: adaptive and pulse-based approaches

Speaker: Sophia Economou (Virginia Tech)

Economou-QTML-Nov2024.pdf

Economou-QTML-Nov2024.pptx

SophiaE.pdf

83 Quantum-Like Contextuality in Large Language Models

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

LoSadrMans.pdf

QTML_talk_GenWino.pdf

84 Optimal Universal Quantum Encoding for Statistical Inference

Speaker: Farhad Farokhi (The University of Melbourne)

Paper

Presentation.pdf

85 Methods for Reducing Ancilla Overhead in Block Encodings

Speakers: András Gilyén, Francisca Vasconcelos (UC Berkeley)

Ancilla_Reduction_with_Animations.pdf

Probabilistic_Compression_Gadget_QTML_Abstract.pdf

86 Adaptive Quantum magnetometry using Myopic Entropy reduction

We present an entropy based method for adaptive magnetometry, focusing on myopic entropy optimisation of NV Ramsey measurements.

Speaker: Julian Greentree (University of Melbourne)

QTML 2024 abstract.pdf

QTML presentation slides.pptx

15:30 Coffee Break

87 Quantum Enhanced Knowledge Distillation

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

Knowledge_Distillation_QTML_2024.pdf

Piperno-QTMLPres.pdf

88 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 28.pptx

89 Information compression via hidden subgroup quantum autoencoders

We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown symmetries of the hidden subgroup type. We prove that data with a given group structure can be compressed with the same query complexity as the hidden subgroup problem, which is exponentially faster than the best-known classical algorithms. We moreover design a quantum algorithm that variationally finds the group structure and uses it to compress the data. There is an encoder and a decoder, along the paradigm of quantum autoencoders. After the training, the encoder outputs a compressed data string and a description of the hidden subgroup symmetry, from which the input data can be recovered by the decoder. In illustrative examples, our algorithm outperforms the classical autoencoder on the mean squared value of test data. This classical-quantum separation in information compression capability has thermodynamical significance: the free energy assigned by a quantum agent to a system can be much higher than that of a classical agent. Taken together, our results show that a possible application of quantum computers is to efficiently compress certain types of data that cannot be efficiently compressed by current methods using classical computers.

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

20241129-QTML-Information compression via Hidden subgroup quantum autoencoders - Copy.pptx

QTML-Extended Abstract.pdf

90 Enabling Aerodynamics Simulations with Quantum Neural Networks and Novel Trainable Features Maps

Speaker: Youssef Achari Berrada (BMW Group / FAU)

241129_ACHARI-BERRADA_QTML_2024_new_4.pptx

QTML_2024_extended_abstract.pdf