A new entropic quantum correlation measure for adversarial systems

Autor: Biveen Shajilal, Elanor Huntington, Ping Koy Lam, Syed Assad

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)

Abstract Quantum correlation often refers to correlations exhibited by two or more local subsystems under a suitable measurement. These correlations are beyond the framework of classical statistics and the associated classical probability distributio

Externí odkaz: https://doaj.org/article/e6185ffd02d64a4bb11ccea75b5e2a9b

Thermodynamic resources in continuous-variable quantum systems

Autor: Varun Narasimhachar, Syed Assad, Felix C. Binder, Jayne Thompson, Benjamin Yadin, Mile Gu

Publikováno v: npj Quantum Information, Vol 7, Iss 1, Pp 1-7 (2021)

Abstract Thermodynamic resources, beyond their well-known usefulness in work extraction and other thermodynamic tasks, are often important also in tasks that are not evidently thermodynamic. Here we develop a framework for identifying such resources

Externí odkaz: https://doaj.org/article/bd5bacca3a664edf894ef0f005097c1a

On the equivalence between squeezing and entanglement potential for two-mode Gaussian states

Autor: Aritra Das, Bohan Li, Spyros Tserkis, Prineha Narang, Ping Koy Lam, Syed Assad

The maximum amount of entanglement achievable under passive transformations by continuous-variable states is called the entanglement potential. Recent work has demonstrated that the entanglement potential is upper-bounded by a simple function of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7c6911c4478019adae2ac0353caf005
http://arxiv.org/abs/2307.10386

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder

Autor: Hui Zhang, Lingxiao Wan, Tobias Haug, Wai-Keong Mok, Stefano Paesani, Yuzhi Shi, Hong Cai, Lip Ket Chin, Muhammad Faeyz Karim, Limin Xiao, Xianshu Luo, Feng Gao, Bin Dong, Syed Assad, M. S. Kim, Anthony Laing, Leong Chuan Kwek, Ai Qun Liu

Publikováno v: Zhang, H, Wan, L, Haug, T, Mok, W-K, Paesani, S, Shi, Y, Cai, H, Chin, L K, Karim, M F, Xiao, L, Luo, X, Gao, F, Dong, B, Assad, S, Kim, M S, Laing, A, Kwek, L C & Liu, A Q 2022, ' Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder ', Science Advances, vol. 8, no. 40, 9783 . https://doi.org/10.1126/sciadv.abn9783

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a comp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7158e727357bc459b4a3140c261114c9
https://curis.ku.dk/ws/files/327937365/sciadv.abn9783.pdf

Benchmarking a Quantum Random Number Generator with Machine Learning

Autor: Jing Yan Haw, Nhan Duy Truong, Hong Jie Ng, Raymond Ho, Syed Assad, Chao Wang, Ping Koy Lam, Omid Kavehei

Publikováno v: Quantum Information and Measurement VI 2021.

We develop a predictive machine learning (ML) analysis to examine the uniformity and unpredictability aspects of a random number generator. We show that our tool is capable of uncovering hidden correlations and experimental imperfections.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::69abe8c0f09f1da8e05b0ee560b4e43c
https://doi.org/10.1364/qim.2021.w3b.5