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QTML 2024 Conference
24–29 Nov 2024
University of Melbourne
Australia/Melbourne timezone
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qtml2024@gmail.com
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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
CSIRO (in session "Industry Talks")
Designing Quantum Experiments with Computers: New Windows to the Universe through Artificial Intelligence
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
DSTG (in session "Industry Talks")
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
Geometic QTML
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 of differential equations and quantum machine learning
Introduction to quantum error mitigation
JP Morgan (in session "Industry Talks")
Language models for the simulation of quantum many-body systems
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
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 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 (in session "Industry Talks")
Potentials and Limitations of Analog Quantum Simulators in Variational Quantum Algorithms
Predicting adaptively chosen observables in quantum systems
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
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
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
Waiting for QML
Wave Function Collapse through Procedural Optimization
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23/11/2024
24/11/2024
25/11/2024
26/11/2024
27/11/2024
28/11/2024
29/11/2024
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