A Proactive Connection Setup Mechanism for Large Quantum Networks
December 25, 2020 Β· Declared Dead Β· π IEEE International Conference on Electronics, Computing and Communication Technologies
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dibakar Das, Shiva Kumar Malapaka, Jyotsna Bapat, Debabrata Das
arXiv ID
2012.13566
Category
quant-ph: Quantum Computing
Cross-listed
cs.NI
Citations
5
Venue
IEEE International Conference on Electronics, Computing and Communication Technologies
Last Checked
5 months ago
Abstract
Quantum networks use quantum mechanics properties of entanglement and teleportation to transfer data from one node to another. Hence, it is necessary to have an efficient mechanism to distribute entanglement among quantum network nodes. Most of research on entanglement distribution apply current state of network and do not consider using historical data. This paper presents a novel way to quicken connection setup between two nodes using historical data and proactively distribute entanglement in quantum network. Results show, with quantum network size increase, the proposed approach improves success rate of connection establishments.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted