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09/20/2023
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Quantum machine learning is arguably one of the most explored applicatio...
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06/08/2023
Classical Verification of Quantum Learning
Quantum data access and quantum processing can make certain classically ...
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05/09/2023
On the average-case complexity of learning output distributions of quantum circuits
In this work, we show that learning the output distributions of brickwor...
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10/26/2022
A super-polynomial quantum-classical separation for density modelling
Density modelling is the task of learning an unknown probability density...
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09/28/2022
Scalably learning quantum many-body Hamiltonians from dynamical data
The physics of a closed quantum mechanical system is governed by its Ham...
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07/07/2022
A single T-gate makes distribution learning hard
The task of learning a probability distribution from samples is ubiquito...
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10/11/2021
Learnability of the output distributions of local quantum circuits
There is currently a large interest in understanding the potential advan...
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06/07/2021
Encoding-dependent generalization bounds for parametrized quantum circuits
A large body of recent work has begun to explore the potential of parame...
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08/19/2020
The effect of data encoding on the expressive power of variational quantum machine learning models
Quantum computers can be used for supervised learning by treating parame...
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07/28/2020
On the Quantum versus Classical Learnability of Discrete Distributions
Here we study the comparative power of classical and quantum learners fo...
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10/02/2019
Stochastic gradient descent for hybrid quantum-classical optimization
Within the context of hybrid quantum-classical optimization, gradient de...
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07/08/2019
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
Tensor-network techniques have enjoyed outstanding success in physics, a...
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10/16/2018