Name: QTML 2024 Conference
Start: 2024-11-24T09:00:00+11:00
End: 2024-11-29T17:25:00+11:00
Location: University of Melbourne

Adaptive Quantum magnetometry using Myopic Entropy reduction

Adversarial Robustness Guarantees for Quantum Classifiers

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

Arbitrary Polynomial Separations in Trainable Quantum Machine Learning

Architectures and random properties of symplectic quantum circuits

Classical shadows with symmetries

Classification of the Fashion-MNIST Dataset on a Quantum Computer

Computing exact moments of local random quantum circuits via tensor networks

Concept learning of quantum models from limited measurements

Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms

CSIRO (in session "Industry Talks")

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

Drug Discovery with a Quantum Annealing Born Machine

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

Engineered dissipation to mitigate barren plateaus

Exponential quantum advantages in learning quantum observables from classical data

Exponential Quantum Communication Advantage in Distributed Inference and Learning

Government of Victoria (in session "Industry Talks")

Graybox quantum system identification and control

Group convolution quantum algorithms: an application to PDEs

Guided Quantum Compression for High Dimensional Data Classification

Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping

Improving quantum machine learning via quantum refrigerator

Inference from interference: hidden subgroups as a learning problem

Information compression via hidden subgroup quantum autoencoders

Interplay between differential equations and quantum machine learning

Introduction to quantum error mitigation

JP Morgan (in session "Industry Talks")

Learning Quantum Processes with Quantum Statistical Queries

Learning quantum states and unitaries of bounded gate complexity

Learning quantum states of continuous variable systems

Learning shallow quantum circuits with many-qubit gates

Learning Theory

Lie-algebraic classical simulations for variational quantum computing

Limitations of measure-first protocols in quantum machine learning

Multidimensional Quantum Generative Modeling by Quantum Hartley Transform

New Approaches for Training Quantum Neural Networks

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

Non-asymptotic Approximation Error Bounds of Parametrized Quantum Circuits

Non-Unitary Quantum Machine Learning

Of ladders and frames: informationally complete measurements for quantum algorithms

On fundamental aspects of Quantum Extreme Learning Machines and Reservoir Computing

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

On the expressivity of embedding quantum kernels

On the relation between trainability and dequantization of variational quantum learning models

Online learning of quantum processes

Opportunities and limitations of explaining quantum machine learning

Optimal Universal Quantum Encoding for Statistical Inference

Pasqal QPU toward quantum reality (in session "Industry Talks")

Predicting adaptively chosen observables in quantum systems

Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

Probabilistic Compression Gadgets

Progress and challenges in quantum feature engineering

Prospects of Privacy Advantage in Quantum Machine Learning

Provable Advantage in Quantum PAC Learning

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

Quantum Algorithm for Apprenticeship Learning

Quantum algorithms for spectral sums

Quantum Algorithms for the Pathwise Lasso

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

Quantum encoder for fixed Hamming-weight subspaces

Quantum Enhanced Knowledge Distillation

Quantum kernel learning for bosonic modes

Quantum linear algebra is all you need for Transformer architectures

Quantum Neural Networks Diagnostics with Neural Tangent Kernels

Quantum resources of quantum and classical variational methods

Quantum topological data analysis: learning graph properties from thermal states

Quantum-Like Contextuality in Large Language Models

Reducing Measurement Costs by Recycling the Hessian in Adaptive Variational Quantum Algorithms

Representation theory for quantum computing

Robust shallow shadows

Scalable quantum dynamics compilation with quantum machine learning

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

State reconstruction via a photonic quantum extreme learning machine in the angular momentum of single photons

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

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics

TetrisCNN: Unsupervised detection of quantum phases and their local order parameters from projective measurements

Think Global, Measure Local

Tomography-aided Feedback-based Quantum Algorithms for Ground State Calculation on Trapped-Ion Quantum Processors

Towards a Rigorous Framework to Analyze Quantum Machine Learning

Towards large-scale quantum optimization solvers with few qubits

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

Training-efficient density quantum machine learning

University of Melbourne (in session "Industry Talks")

Unveiling quantum phase transitions from traps in variational quantum algorithms

Using variance of gradients to explain quantum phase recognition algorithms

Variational quantum algorithms: adaptive and pulse-based approaches

Virtual Panel Session: The future of Quantum Computing

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