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Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits

Autor: Letcher, Alistair, Woerner, Stefan, Zoufal, Christa

Publikováno v: Quantum 8, 1484 (2024)

The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalability of variational quantum algorithms (VQAs), and are known to arise in

Externí odkaz: http://arxiv.org/abs/2309.12681

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Akademický článek

A Glance into the Near Future: Cultivated Meat from Mammalian and Insect Cells

Autor: Fabiana Giglio, Carmen Scieuzo, Sofia Ouazri, Valentina Pucciarelli, Dolores Ianniciello, Sophia Letcher, Rosanna Salvia, Ambrogio Laginestra, David L. Kaplan, Patrizia Falabella

Publikováno v: Small Science, Vol 4, Iss 10, Pp n/a-n/a (2024)

The increasing global population and demand for meat have led to the need to find sustainable and viable alternatives to traditional production methods. One potential solution is cultivated meat (CM), which involves producing meat in vitro from anima

Externí odkaz: https://doaj.org/article/48d2410b17e74109b6ebaec77e0058f9

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Adversarial Cheap Talk

Autor: Lu, Chris, Willi, Timon, Letcher, Alistair, Foerster, Jakob

Adversarial attacks in reinforcement learning (RL) often assume highly-privileged access to the victim's parameters, environment, or data. Instead, this paper proposes a novel adversarial setting called a Cheap Talk MDP in which an Adversary can mere

Externí odkaz: http://arxiv.org/abs/2211.11030

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Discovered Policy Optimisation

Autor: Lu, Chris, Kuba, Jakub Grudzien, Letcher, Alistair, Metz, Luke, de Witt, Christian Schroeder, Foerster, Jakob

Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which were designed using a combination of mathematical derivations, intuition

Externí odkaz: http://arxiv.org/abs/2210.05639

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COLA: Consistent Learning with Opponent-Learning Awareness

Autor: Willi, Timon, Letcher, Alistair, Treutlein, Johannes, Foerster, Jakob

Learning in general-sum games is unstable and frequently leads to socially undesirable (Pareto-dominated) outcomes. To mitigate this, Learning with Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting, by accounting for each

Externí odkaz: http://arxiv.org/abs/2203.04098

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