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Showing 1–15 of 15 results
Advanced filters: Author: Lukasz Cincio Clear advanced filters

  • Efficient characterisation of quantum many-body Hamiltonians has important applications for benchmarking NISQ devices. Here, the authors propose a method employing Chebyshev regression to learn the full Hamiltonian of a quantum system, with a sample complexity that scales efficiently with the system size.

    • Andi Gu
    • Lukasz Cincio
    • Patrick J. Coles
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-10

  • Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions.

    • Matthias C. Caro
    • Hsin-Yuan Huang
    • Zoë Holmes
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-9

  • The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.

    • Matthias C. Caro
    • Hsin-Yuan Huang
    • Patrick J. Coles
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-11

  • Variational quantum algorithms (VQAs) are a leading candidate for useful applications of near-term quantum computing, but limitations due to unavoidable noise have not been clearly characterized. Here, the authors prove that local Pauli noise can cause vanishing gradients rendering VQAs untrainable.

    • Samson Wang
    • Enrico Fontana
    • Patrick J. Coles
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-11

  • The Consistent Histories formalism can solve paradoxes in quantum mechanics, but finding such consistent sets of histories requires a computational overhead which is exponential in the problem’s size. Here, the authors report a variational hybrid algorithm solving this problem using polynomial resources.

    • Andrew Arrasmith
    • Lukasz Cincio
    • Patrick J. Coles
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-7

  • Parametrised quantum circuits are a promising hybrid classical-quantum approach, but rigorous results on their effective capabilities are rare. Here, the authors explore the feasibility of training depending on the type of cost functions, showing that local ones are less prone to the barren plateau problem.

    • M. Cerezo
    • Akira Sone
    • Patrick J. Coles
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-12

  • Barren plateaus are widely considered as one of the main limitations for variational quantum algorithms. This Review summarizes the latest understandings of barren plateaus, indicating its causes, architecture that will suffer from this phenomenon, and discusses strategies that can — and cannot — avoid it.

    • Martín Larocca
    • Supanut Thanasilp
    • M. Cerezo
    Reviews
    Nature Reviews Physics
    Volume: 7, P: 174-189

  • Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.

    • M. Cerezo
    • Guillaume Verdon
    • Patrick J. Coles
    Reviews
    Nature Computational Science
    Volume: 2, P: 567-576

  • The advent of commercial quantum devices has ushered in the era of near-term quantum computing. Variational quantum algorithms are promising candidates to make use of these devices for achieving a practical quantum advantage over classical computers.

    • M. Cerezo
    • Andrew Arrasmith
    • Patrick J. Coles
    Reviews
    Nature Reviews Physics
    Volume: 3, P: 625-644