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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
Quantum state discrimination is an important problem in many information processing tasks. In this work we are concerned with finding its best possible sample complexity when the states are preprocessed by a quantum channel that is required to be loc
Externí odkaz:
http://arxiv.org/abs/2406.18658
Quantum systems typically reach thermal equilibrium rather quickly when coupled to a thermal environment. The usual way of bounding the speed of this process is by estimating the spectral gap of the dissipative generator. However the gap, by itself,
Externí odkaz:
http://arxiv.org/abs/2404.16780
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
We show that marginals of blocks of $t$ systems of any finitely correlated translation invariant state on a chain can be learned, in trace distance, with $O(t^2)$ copies -- with an explicit dependence on local dimension, memory dimension and spectral
Externí odkaz:
http://arxiv.org/abs/2312.07516
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
Autor:
König, Robert, Rouzé, Cambyse
Local update recovery seeks to maintain quantum information by applying local correction maps alternating with and compensating for the action of noise. Motivated by recent constructions based on quantum LDPC codes in the finite-dimensional setting,
Externí odkaz:
http://arxiv.org/abs/2309.16241
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-8 (2024)
Abstract We consider two related tasks: (a) estimating a parameterisation of a given Gibbs state and expectation values of Lipschitz observables on this state; (b) learning the expectation values of local observables within a thermal or quantum phase
Externí odkaz:
https://doaj.org/article/e573684f56454faea9e5ef0f60c30baa
