Search Results - "Sweke, Ryan"

Stochastic gradient descent for hybrid quantum-classical optimization by Sweke, Ryan, Wilde, Frederik, Meyer, Johannes, Schuld, Maria, Faehrmann, Paul K., Meynard-Piganeau, Barthélémy, Eisert, Jens

“…Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with…”
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On the Quantum versus Classical Learnability of Discrete Distributions by Sweke, Ryan, Seifert, Jean-Pierre, Hangleiter, Dominik, Eisert, Jens

“…Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More…”
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Encoding-dependent generalization bounds for parametrized quantum circuits by Caro, Matthias C., Gil-Fuster, Elies, Meyer, Johannes Jakob, Eisert, Jens, Sweke, Ryan

“…A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of…”
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Reinforcement learning decoders for fault-tolerant quantum computation by Sweke, Ryan, Kesselring, Markus S, van Nieuwenburg, Evert P L, Eisert, Jens

“…Topological error correcting codes, and particularly the surface code, currently provide the most feasible road-map towards large-scale fault-tolerant quantum…”
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Transparent reporting of research-related greenhouse gas emissions through the scientific CO2nduct initiative by Sweke, Ryan, Boes, Paul, Ng, Nelly, Sparaciari, Carlo, Eisert, Jens, Goihl, Marcel

“…Estimating the greenhouse gas emissions of research-related activities is a critical first step towards the design of mitigation policies and actions. Here we…”
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Classical Verification of Quantum Learning by Caro, Matthias C, Hinsche, Marcel, Ioannou, Marios, Nietner, Alexander, Sweke, Ryan

“…In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp…”
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A super-polynomial quantum-classical separation for density modelling by Pirnay, Niklas, Sweke, Ryan, Eisert, Jens, Seifert, Jean-Pierre

“…Phys. Rev. A 107, 042416 (2023) Density modelling is the task of learning an unknown probability density function from samples, and is one of the central…”
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Encoding-dependent generalization bounds for parametrized quantum circuits by Caro, Matthias C, Gil-Fuster, Elies, Meyer, Johannes Jakob, Eisert, Jens, Sweke, Ryan

“…Quantum 5, 582 (2021) A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within…”
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Universal simulation of Markovian open quantum systems by Sweke, Ryan, Sinayskiy, Ilya, Bernard, Denis, Petruccione, Francesco

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On the Quantum versus Classical Learnability of Discrete Distributions by Sweke, Ryan, Seifert, Jean-Pierre, Hangleiter, Dominik, Eisert, Jens

“…Quantum 5, 417 (2021) Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct…”
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The effect of data encoding on the expressive power of variational quantum machine learning models by Schuld, Maria, Sweke, Ryan, Meyer, Johannes Jakob

“…Phys. Rev. A 103, 032430 (2021) Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs…”
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Lieb-Robinson bounds for open quantum systems with long-ranged interactions by Sweke, Ryan, Eisert, Jens, Kastner, Michael

“…J. Phys. A 52, 424003 (2019) We state and prove four types of Lieb-Robinson bounds valid for many-body open quantum systems with power law decaying…”
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Stochastic gradient descent for hybrid quantum-classical optimization by Sweke, Ryan, Wilde, Frederik, Meyer, Johannes, Schuld, Maria, Faehrmann, Paul K, Meynard-Piganeau, Barthélémy, Eisert, Jens

“…Quantum 4, 314 (2020) Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of…”
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Universal simulation of Markovian open quantum systems by Sweke, Ryan, Sinayskiy, Ilya, Bernard, Denis, Petruccione, Francesco

“…Phys. Rev. A 91, 062308 (2015) We consider the problem of constructing a "universal set" of Markovian processes, such that any Markovian open quantum system,…”
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Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation by Sweke, Ryan, Kesselring, Markus S, van Nieuwenburg, Evert P. L, Eisert, Jens

“…Mach. Learn. Sci. Technol. 2, 025005 (2021) Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap…”
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Simulation of single-qubit open quantum systems by Sweke, Ryan, Sinayskiy, Ilya, Petruccione, Francesco

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Dissipative preparation of large W states in optical cavities by Sweke, Ryan, Sinayskiy, Ilya, Petruccione, Francesco

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Dynamic parameterized quantum circuits: expressive and barren-plateau free by Deshpande, Abhinav, Hinsche, Marcel, Najafi, Sona, Sharma, Kunal, Sweke, Ryan, Zoufal, Christa

“…Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution…”
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Interactive proofs for verifying (quantum) learning and testing by Caro, Matthias C, Eisert, Jens, Hinsche, Marcel, Ioannou, Marios, Nietner, Alexander, Sweke, Ryan

“…We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place…”
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Potential and limitations of random Fourier features for dequantizing quantum machine learning by Sweke, Ryan, Recio, Erik, Jerbi, Sofiene, Gil-Fuster, Elies, Fuller, Bryce, Eisert, Jens, Meyer, Johannes Jakob

“…Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational…”
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