Optimization of QKD Networks with Classical and Quantum Annealing

06/28/2022
by   Bob Godar, et al.
0

This paper analyses a classical and a quantum annealing approach to compute the minimum deployment of Quantum Key Distribution (QKD) hardware in a tier 1 provider network. The ensemble of QKD systems needs to be able to exchange as many encryption keys between all network nodes in order to encrypt the data payload, which is defined by traffic demand matrices. Redundancy and latency requirements add additional boundary conditions. The result of the optimization problem yields a classical heuristic network planners may utilize for planning future QKD quantum networks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/31/2020

Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations

Homomorphic encryption has been an area of study in classical computing ...
research
06/26/2017

Efficiency of quantum versus classical annealing in non-convex learning problems

Quantum annealers aim at solving non-convex optimization problems by exp...
research
06/08/2022

Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms

We solve robot trajectory planning problems at industry-relevant scales....
research
10/01/2020

Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems

With unprecedented increases in traffic load in today's wireless network...
research
09/03/2021

Challenge: A Cost and Power Feasibility Analysis of Quantum Annealing for NextG Cellular Wireless Networks

In order to meet mobile cellular users' ever-increasing network usage, t...
research
05/28/2021

Quantum Optimisation of Complex Systems with a Quantum Annealer

We perform an in-depth comparison of quantum annealing with several clas...
research
08/24/2022

Financial Index Tracking via Quantum Computing with Cardinality Constraints

In this work, we demonstrate how to apply non-linear cardinality constra...

Please sign up or login with your details

Forgot password? Click here to reset