Zobrazeno 1 - 10
of 72
pro vyhledávání: '"França, Daniel Stilck"'
It is of great interest to understand the thermalization of open quantum many-body systems, and how quantum computers are able to efficiently simulate that process. A recently introduced disispative evolution, inspired by existing models of open syst
Externí odkaz:
http://arxiv.org/abs/2411.04885
In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models. We obtain efficient protocols, both in sampl
Externí odkaz:
http://arxiv.org/abs/2411.03163
Autor:
Mele, Antonio Anna, Angrisani, Armando, Ghosh, Soumik, Khatri, Sumeet, Eisert, Jens, França, Daniel Stilck, Quek, Yihui
Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that any noise `truncates' most quantum circuits to effectively logarithmic dept
Externí odkaz:
http://arxiv.org/abs/2403.13927
The preparation of thermal states of matter is a crucial task in quantum simulation. In this work, we prove that a recently introduced, efficiently implementable dissipative evolution thermalizes to the Gibbs state in time scaling polynomially with s
Externí odkaz:
http://arxiv.org/abs/2403.12691
Autor:
Beatty, Emily, França, Daniel Stilck
Optimal transport provides a powerful mathematical framework with applications spanning numerous fields. A cornerstone within this domain is the $p$-Wasserstein distance, which serves to quantify the cost of transporting one probability measure to an
Externí odkaz:
http://arxiv.org/abs/2402.16477
The combination of quantum many-body and machine learning techniques has recently proved to be a fertile ground for new developments in quantum computing. Several works have shown that it is possible to classically efficiently predict the expectation
Externí odkaz:
http://arxiv.org/abs/2311.07506
Autor:
Caro, Matthias, Gur, Tom, Rouzé, Cambyse, França, Daniel Stilck, Subramanian, Sathyawageeswar
Learning tasks play an increasingly prominent role in quantum information and computation. They range from fundamental problems such as state discrimination and metrology over the framework of quantum probably approximately correct (PAC) learning, to
Externí odkaz:
http://arxiv.org/abs/2311.05529
In quantum metrology, one of the major applications of quantum technologies, the ultimate precision of estimating an unknown parameter is often stated in terms of the Cram\'er-Rao bound. Yet, the latter is no longer guaranteed to carry an operational
Externí odkaz:
http://arxiv.org/abs/2307.06370
Autor:
Rouzé, Cambyse, França, Daniel Stilck
The unavoidable presence of noise is a crucial roadblock for the development of large-scale quantum computers and the ability to characterize quantum noise reliably and efficiently with high precision is essential to scale quantum technologies furthe
Externí odkaz:
http://arxiv.org/abs/2307.02959
Autor:
Harley, Dylan, Datta, Ishaun, Klausen, Frederik Ravn, Bluhm, Andreas, França, Daniel Stilck, Werner, Albert H., Christandl, Matthias
Publikováno v:
Nat. Commun. 15, 6527 (2024)
Quantum hardware has the potential to efficiently solve computationally difficult problems in physics and chemistry to reap enormous practical rewards. Analogue quantum simulation accomplishes this by using the dynamics of a controlled many-body syst
Externí odkaz:
http://arxiv.org/abs/2306.13739