Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Sweke, Ryan"'
Learning topological states from randomized measurements using variational tensor network tomography
Autor:
Teng, Yanting, Samajdar, Rhine, Van Kirk, Katherine, Wilde, Frederik, Sachdev, Subir, Eisert, Jens, Sweke, Ryan, Najafi, Khadijeh
Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic methods such as classical shadow and MPS tomography have shown promise in
Externí odkaz:
http://arxiv.org/abs/2406.00193
Autor:
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 quantum machine learning where parameterized quantum circuits (PQCs) are used as learning model
Externí odkaz:
http://arxiv.org/abs/2309.11647
Publikováno v:
In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 24:1-24:23, Schloss Dagstuhl -- Leibniz-Zentrum f\"ur Informatik (2024)
Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in the near future. Thus, reliable schemes that allow classical clients
Externí odkaz:
http://arxiv.org/abs/2306.04843
Autor:
Nietner, Alexander, Ioannou, Marios, Sweke, Ryan, Kueng, Richard, Eisert, Jens, Hinsche, Marcel, Haferkamp, Jonas
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely used as an abstract computational model for most generic learning alg
Externí odkaz:
http://arxiv.org/abs/2305.05765
Publikováno v:
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 problems of unsupervised machine learning. In this work, we show that there exists a density modelling problem for which fault-t
Externí odkaz:
http://arxiv.org/abs/2210.14936
Autor:
Wilde, Frederik, Kshetrimayum, Augustine, Roth, Ingo, Hangleiter, Dominik, Sweke, Ryan, Eisert, Jens
The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this w
Externí odkaz:
http://arxiv.org/abs/2209.14328
Autor:
Hinsche, Marcel, Ioannou, Marios, Nietner, Alexander, Haferkamp, Jonas, Quek, Yihui, Hangleiter, Dominik, Seifert, Jean-Pierre, Eisert, Jens, Sweke, Ryan
Publikováno v:
Phys. Rev. Lett. 130, 240602 (2023)
The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advan
Externí odkaz:
http://arxiv.org/abs/2207.03140
Publikováno v:
Commun. Phys. 5, 150 (2022)
Estimating the greenhouse gas emissions of research-related activities is a critical first step towards the design of mitigation policies and actions. Here we propose and motivate a transparent framework for reporting research-related greenhouse gas
Externí odkaz:
http://arxiv.org/abs/2206.00857
Autor:
Hinsche, Marcel, Ioannou, Marios, Nietner, Alexander, Haferkamp, Jonas, Quek, Yihui, Hangleiter, Dominik, Seifert, Jean-Pierre, Eisert, Jens, Sweke, Ryan
There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two different oracle models, the probably approximately correct (PAC) learnabilit
Externí odkaz:
http://arxiv.org/abs/2110.05517
Publikováno v:
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 the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-samp
Externí odkaz:
http://arxiv.org/abs/2106.03880