The European Physical Journal (EPJ) is a series of peer-reviewed journals covering the whole spectrum of physics and related interdisciplinary subjects. EPJ is committed to high scientific quality in publishing and is indexed in all main citation databases.
- Published on 23 January 2019
Study improves the lower boundary and secret key capacity of an encryption channel
The secure encryption of information units based on a method called quantum key distribution (QKD) involves distributing secret keys between two parties - namely, Alice, the sender, and Bob, the receiver - by using quantum systems as information carriers. However, the most advanced quantum technology, QKD, is currently limited by the channel's capacity to send or share secret bits. In a recent study published in EPJ D, Gan Wang, who is affiliated with both Peking University, Bejing, China, and the University of York, UK, and colleagues show how to better approach the secret key capacity by improving the channel's lower boundary.
- Published on 22 January 2019
New study yields more precise characterisation of monogamous and polygamous entanglement of quantum information units
Encrypted communication is achieved by sending quantum information in basic units called quantum bits, or qubits. The most basic type of quantum information processing is quantum entanglement. However, this process remains poorly understood. Better controlling quantum entanglement could help to improve quantum teleportation, the development of quantum computers, and quantum cryptography. Now, a team of Chinese physicists have focused on finding ways to enhance the reliability of quantum secret sharing. In a new study published in EPJ D, Zhaonan Zhang from Shaanxi Normal University, Xi'an, China, and colleagues provide a much finer characterisation of the distributions of entanglement in multi-qubit systems than previously available. In the context of quantum cryptography, these findings can be used to estimate the quantity of information an eavesdropper can capture regarding the secret encryption key.
EPJ Data Science Highlight - Twitter’s tampered samples: Limitations of big data sampling in social media
- Published on 16 January 2019
Social networks are widely used as sources of data in computational social science studies, and so it is of particular importance to determine whether these datasets are bias-free. In EPJ Data Science, Jürgen Pfeffer, Katja Mayer and Fred Morstatter demonstrate how Twitter’s sampling mechanism is prone to manipulation that could influence how researchers, journalists, marketeers and policy analysts interpret their data.
(Guest post by Jürgen Pfeffer, Katja Mayer and Fred Morstatter, originally published in the SpringerOpen blog)