Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Gratsea, A."'
Autor:
Gratsea, Katerina, Selisko, Johannes, Amsler, Maximilian, Wever, Christopher, Eckl, Thomas, Samsonidze, Georgy
The Variational Quantum Eigensolver (VQE) is one of the most promising and widely used algorithms for exploiting the capabilities of current Noisy Intermediate-Scale Quantum (NISQ) devices. However, VQE algorithms suffer from a plethora of issues, su
Externí odkaz:
http://arxiv.org/abs/2407.10415
One promising field of quantum computation is the simulation of quantum systems, and specifically, the task of ground state energy estimation (GSEE). Ground state preparation (GSP) is a crucial component in GSEE algorithms, and classical methods like
Externí odkaz:
http://arxiv.org/abs/2401.05306
Over the past decade, research in quantum computing has tended to fall into one of two camps: near-term intermediate scale quantum (NISQ) and fault-tolerant quantum computing (FTQC). Yet, a growing body of work has been investigating how to use quant
Externí odkaz:
http://arxiv.org/abs/2311.14814
In recent years substantial research effort has been devoted to quantum algorithms for ground state energy estimation (GSEE) in chemistry and materials. Given the many heuristic and non-heuristic methods being developed, it is challenging to assess w
Externí odkaz:
http://arxiv.org/abs/2212.09492
There is an increasing interest in Quantum Machine Learning (QML) models, how they work and for which applications they could be useful. There have been many different proposals on how classical data can be encoded and what circuit ans\"atze and meas
Externí odkaz:
http://arxiv.org/abs/2211.03101
Driven by growing computational power and algorithmic developments, machine learning methods have become valuable tools for analyzing vast amounts of data. Simultaneously, the fast technological progress of quantum information processing suggests emp
Externí odkaz:
http://arxiv.org/abs/2111.08414
Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN) to perform classification tasks. There have been many proposals how to use variational quantum circuits a
Externí odkaz:
http://arxiv.org/abs/2105.01477
Autor:
Lewenstein, Maciej, Gratsea, Aikaterini, Riera-Campeny, Andreu, Aloy, Albert, Kasper, Valentin, Sanpera, Anna
We study the storage capacity of quantum neural networks (QNNs) described as completely positive trace preserving (CPTP) maps, which act on an $N$-dimensional Hilbert space. We demonstrate that QNNs can store up to $N$ linearly independent pure state
Externí odkaz:
http://arxiv.org/abs/2011.06113
Publikováno v:
53(44):445306, Oct 2020
Entanglement is a key resource in many quantum information applications and achieving high values independently of the initial conditions is an important task. Here we address the problem of generating highly entangled states in a discrete time quant
Externí odkaz:
http://arxiv.org/abs/2003.07141
Publikováno v:
Applied Physics B 126, 99 (2020)
Quantum vision is currently emerging as an interdisciplinary field synthesizing the physiology of human vision with modern quantum optics. We recently proposed a biometric scheme based on the human visual system's ability to perform photon counting.
Externí odkaz:
http://arxiv.org/abs/1907.06539