Experimental measurement-device-independent quantum digital signatures

Abstract

The development of quantum networks will be paramount towards practical and secure telecommunications. These networks will need to sign and distribute information between many parties with information-theoretic security, requiring both quantum digital signatures (QDS) and quantum key distribution (QKD). Here, we introduce and experimentally realise a quantum network architecture, where the nodes are fully connected using a minimum amount of physical links. The central node of the network can act either as a totally untrusted relay, connecting the end users via the recently introduced measurement-device-independent (MDI)-QKD, or as a trusted recipient directly communicating with the end users via QKD. Using this network, we perform a proof-of-principle demonstration of QDS mediated by MDI-QKD. For that, we devised an efficient protocol to distil multiple signatures from the same block of data, thus reducing the statistical fluctuations in the sample and greatly enhancing the final QDS rate in the finite-size scenario.

Laser-seeding Attack in Quantum Key Distribution

“…Although MDI-QKD is immune to all detector sidechannel attacks, our work shows Eve's capability of hacking the source of a QKD system and highlights that further research is needed to protect the system against source side-channel attacks. Moreover, we remark that the laser seeding attack may compromise as well the security of other quantum decoy-state based cryptographic systems beyond QKD, like, for instance, various twoparty protocols with practical signals [74], quantum digital signatures [75,76], and blind quantum computing [77,78].…”

Practical Phase-Modulation Stabilization in Quantum Key Distribution via Machine Learning

“…Here we only take a phase-coding BB84 QKD system as an example to run our model and get excellent experimental results. In principle, this model should also be applicable to any other coding scheme and QKD protocols [17][18][19][20], since the machine learning model only needs enough training data to construct out corresponding mapping relationship between "features" and "labels", and has no requirements for specific systems or protocols. Therefore, we believe that our present machine learning based operating mode could replace traditional "scanning-and-transmitting" program and play an important role in practical applications of large-scale quantum communication networks in the near future.…”

Multi-bit quantum digital signature based on temporal quantum ghost imaging

“…If both of the noise factors in the two ghost images, defined as the inverse of signal-to-noise ratio, are lower than the acceptance threshold T h B accept , Bob will accept the message and forwards it to Charlie through the classical channel. The noise factor here is similar to the concept of mismatch in previous QDS [15][16][17][18][19][20][21][22][23] where Bob checks Alice's raw keys to decide whether he accepts the message or not.…”

“…Wallden et al furthermore presented QDS protocols [11] requiring only the same technical components as quantum key distribution (QKD), which has been greatly developed over the past two decades [12,13]. Such breakthroughs motivated several notable advances in experimental demonstrations of QDS [14], where the transmission distance has been remarkably extended by utilizing phase-encoded states [15][16][17] and polarization-encoded states [18][19][20] in recent years. Additionally, several pro- * zwei@tsinghua.edu.cn posals have been implemented for improving the security of QDS, such as measurement-device-independent (MDI) QDS [21,22], which is immune to detector side-channel attacks, and passive decoy-state QDS for circumventing the leakage of the signal and decoy information to attackers [23].…”