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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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07/26/2023
Verifiable measurement-based quantum random sampling with trapped ions
Quantum computers are now on the brink of outperforming their classical ...
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06/23/2023
Understanding quantum machine learning also requires rethinking generalization
Quantum machine learning models have shown successful generalization per...
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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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03/06/2023
Towards provably efficient quantum algorithms for large-scale machine-learning models
Large machine learning models are revolutionary technologies of artifici...
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03/04/2023
Good Gottesman-Kitaev-Preskill codes from the NTRU cryptosystem
We introduce a new class of random Gottesman-Kitaev-Preskill (GKP) codes...
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12/16/2022
A super-polynomial quantum advantage for combinatorial optimization problems
Combinatorial optimization - a field of research addressing problems tha...
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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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10/13/2022
Noise can be helpful for variational quantum algorithms
Saddle points constitute a crucial challenge for first-order gradient de...
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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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06/23/2022
Classical surrogates for quantum learning models
The advent of noisy intermediate-scale quantum computers has put the sea...
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06/08/2022
Computational advantage of quantum random sampling
Quantum random sampling is the leading proposal for demonstrating a comp...
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05/18/2022
Anonymous conference key agreement in linear quantum networks
Sharing multi-partite quantum entanglement between parties allows for di...
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05/12/2022
Exploiting symmetry in variational quantum machine learning
Variational quantum machine learning is an extensively studied applicati...
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11/05/2021
Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction
The blind deconvolution problem amounts to reconstructing both a signal ...
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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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06/02/2021
Single-component gradient rules for variational quantum algorithms
Many near-term quantum computing algorithms are conceived as variational...
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05/07/2021
Hierarchical sparse recovery from hierarchically structured measurements with application to massive random access
A new family of operators, coined hierarchical measurement operators, is...
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04/06/2021
Hierarchical compressed sensing
Compressed sensing is a paradigm within signal processing that provides ...
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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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06/04/2020
Semi-device-dependent blind quantum tomography
Extracting tomographic information about quantum states is a crucial tas...
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05/20/2020
Hierarchical sparse recovery from hierarchically structured measurements
A new class of measurement operators for structured compressed sensing p...
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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
Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
Topological error correcting codes, and particularly the surface code, c...
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06/07/2018
Performance of Hierarchical Sparse Detectors for Massive MTC
Recently, the Hierarchical Hard Thresholding Pursuit (HiHTP) algorithm w...
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03/01/2018
Recovering quantum gates from few average gate fidelities
Characterising quantum processes is a key task in and constitutes a chal...
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01/29/2018