S
Stefano Pirandola
Researcher at University of York
Publications - 311
Citations - 18606
Stefano Pirandola is an academic researcher from University of York. The author has contributed to research in topics: Quantum & Quantum entanglement. The author has an hindex of 51, co-authored 286 publications receiving 14410 citations. Previous affiliations of Stefano Pirandola include Centre for Quantum Technologies & Massachusetts Institute of Technology.
Papers
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Security of two-way quantum cryptography against asymmetric Gaussian attacks
TL;DR: This work considers asymmetric strategies where the Gaussian interactions can be different and classically correlated and proves that the enhancement of security still holds when the two-way protocols are used in direct reconciliation.
Journal ArticleDOI
Quantum-Enhanced Cluster Detection in Physical Images
TL;DR: It is shown that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here it is demonstrated that this advantage also holds for an unsupervised learning task, namely clustering.
Posted Content
Bounding the benefit of adaptivity in quantum metrology using the relative fidelity
TL;DR: In this paper, the relative fidelity of a given pair of channels and a pair of input states to those channels is defined, and it is shown that for any protocol acting on one of two possible channels in terms of the minimum relative fidelity, the quantum Fisher information (QFI) of the output of an $N$-use protocol is no more than $N 2$ times the one-shot QFI.
Posted Content
Quantum Hypothesis Testing for Fundamental Physics
TL;DR: In this article, an optomechanical system composed of two cavities employed to perform quantum channel discrimination is presented, and the results are applied to the discrimination of models of spontaneous collapse of the wavefunction, highlighting the possibilities of this scheme for fundamental physics searches.
Proceedings ArticleDOI
Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
TL;DR: In this paper , the authors proposed the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time.