Towards efficient quantum algorithms for diffusion probability models.
CoRR, February, 2025
Potential and limitations of random Fourier features for dequantizing quantum machine learning.
Quantum, 2025
Quantum complexity phase transitions in monitored random circuits.
Quantum, 2025
PAC-learning of free-fermionic states is NP-hard.
Quantum, 2025
Simulation results for "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes".
Dataset, June, 2024
Good Gottesman-Kitaev-Preskill codes from the NTRU cryptosystem.
Quantum, 2024
Decoding quantum color codes with MaxSAT.
Quantum, 2024
On the expressivity of embedding quantum kernels.
Mach. Learn. Sci. Technol., 2024
Opportunities and limitations of explaining quantum machine learning.
CoRR, 2024
An unconditional distribution learning advantage with shallow quantum circuits.
CoRR, 2024
More global randomness from less random local gates.
CoRR, 2024
Interactive proofs for verifying (quantum) learning and testing.
CoRR, 2024
Artificially intelligent Maxwell's demon for optimal control of open quantum systems.
CoRR, 2024
Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes.
CoRR, 2024
Online learning of quantum processes.
CoRR, 2024
Dataset containing raw threshold and runtime simulation data for a paper evaluation on decoding quantum color codes.
Dataset, 2024
Dataset containing raw simulation data for a paper on decoding bosonic quantum LDPC codes.
Dataset, November, 2023
Correcting non-independent and non-identically distributed errors with surface codes.
Quantum, September, 2023
Anonymous conference key agreement in linear quantum networks.
Quantum, September, 2023
Semi-device-dependent blind quantum tomography.
Quantum, July, 2023
Dataset containing raw threshold and runtime simulation data for a paper evaluation on decoding quantum color codes.
Dataset, March, 2023
Analog information decoding of bosonic quantum LDPC codes.
CoRR, 2023
Potential and limitations of random Fourier features for dequantizing quantum machine learning.
CoRR, 2023
Verifiable measurement-based quantum random sampling with trapped ions.
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
CoRR, 2023
Understanding quantum machine learning also requires rethinking generalization.
CoRR, 2023
On the average-case complexity of learning output distributions of quantum circuits.
CoRR, 2023
Towards provably efficient quantum algorithms for large-scale machine-learning models.
CoRR, 2023
Randomizing multi-product formulas for Hamiltonian simulation.
Quantum, September, 2022
Tensor network models of AdS/qCFT.
Quantum, 2022
Gottesman-Kitaev-Preskill codes: A lattice perspective.
Quantum, 2022
A super-polynomial quantum advantage for combinatorial optimization problems.
CoRR, 2022
A super-polynomial quantum-classical separation for density modelling.
CoRR, 2022
Noise can be helpful for variational quantum algorithms.
CoRR, 2022
Scalably learning quantum many-body Hamiltonians from dynamical data.
CoRR, 2022
A single T-gate makes distribution learning hard.
CoRR, 2022
Classical surrogates for quantum learning models.
CoRR, 2022
Computational advantage of quantum random sampling.
CoRR, 2022
Exploiting symmetry in variational quantum machine learning.
CoRR, 2022
A smallest computable entanglement monotone.
Proceedings of the IEEE International Symposium on Information Theory, 2022
On the Quantum versus Classical Learnability of Discrete Distributions.
Quantum, 2021
Non-Pauli topological stabilizer codes from twisted quantum doubles.
Quantum, 2021
Encoding-dependent generalization bounds for parametrized quantum circuits.
Quantum, 2021
Reinforcement learning decoders for fault-tolerant quantum computation.
Mach. Learn. Sci. Technol., 2021
Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction.
CoRR, 2021
Learnability of the output distributions of local quantum circuits.
CoRR, 2021
Single-component gradient rules for variational quantum algorithms.
CoRR, 2021
Hierarchical compressed sensing.
CoRR, 2021
Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2021
Reliable Recovery of Hierarchically Sparse Signals for Gaussian and Kronecker Product Measurements.
IEEE Trans. Signal Process., 2020
Stochastic gradient descent for hybrid quantum-classical optimization.
Quantum, 2020
Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell.
Quantum, 2020
By-passing fluctuation theorems.
Quantum, 2020
Hierarchical sparse recovery from hierarchically structured measurements.
CoRR, 2020
Tensor network approaches for learning non-linear dynamical laws.
CoRR, 2020
Subsystem symmetries, quantum cellular automata, and computational phases of quantum matter.
Quantum, 2019
Guaranteed recovery of quantum processes from few measurements.
Quantum, 2019
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning.
CoRR, 2019
Expressive power of tensor-network factorizations for probabilistic modeling.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
The boundaries and twist defects of the color code and their applications to topological quantum computation.
Quantum, 2018
Anticoncentration theorems for schemes showing a quantum speedup.
Quantum, 2018
Performance of Hierarchical Sparse Detectors for Massive MTC.
CoRR, 2018
Recovering quantum gates from few average gate fidelities.
CoRR, 2018
Secure massive IoT using hierarchical fast blind deconvolution.
Proceedings of the 2018 IEEE Wireless Communications and Networking Conference Workshops, 2018
Hierarchical restricted isometry property for Kronecker product measurements.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018
Axiomatic Characterization of the Quantum Relative Entropy and Free Energy.
Entropy, 2017
HiHTP: A custom-tailored hierarchical sparse detector for massive MTC.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017
Improving Compressed Sensing With the Diamond Norm.
IEEE Trans. Inf. Theory, 2016
Reliable recovery of hierarchically sparse signals and application in machine-type communications.
CoRR, 2016
Majorana fermions and non-locality.
Quantum Inf. Comput., 2014
Boson-Sampling in the light of sample complexity.
CoRR, 2013
Deciding whether a Quantum Channel is Markovian is NP-hard
CoRR, 2009
Quantum margulis expanders.
Quantum Inf. Comput., 2008
Proceedings of the Handbook of Nature-Inspired and Innovative Computing, 2006
Entanglement transformations of pure Gaussian states.
Quantum Inf. Comput., 2003