Mirela Selimović | Searcharxiv

arXiv2026

Deployment of Entanglement-Based QKD in Financial Infrastructure

We demonstrate the feasibility of entanglement-based quantum key distribution (eQKD) in high-security financial infrastructure over a 22 km fiber link with 8 dB loss between two data centers using polarization entanglement. The fully automated system continuously generated secure keys for four months at an average rate of 63.8 kb/s, which were stored into a key management system and consumed to establish a VPN tunnel. The setup achieved 93.7% total up-time, with no downtime caused by the quantum optical components. Active polarization control kept the quantum bit error rate below 2% for 97.4% of the time and timing synchronization based on the entangled photon pairs' intrinsic temporal correlations achieved sub-300 ps precision. Our standalone system requires neither polarized guide lasers nor external high-precision time references. These results show practical integration of eQKD into operational financial infrastructure.

quant-ph↗

arXiv2025

Experimental neuromorphic computing based on quantum memristor

Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviours, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We benchmark our model on four tasks, a nonlinear function and three time series prediction. In these cases, we highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks.

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