Minimizing Teleportation and Enhancing Fidelity in Distributed Quantum Computing using a Multi-Objective Evolutionary Algorithm

Minimizing Teleportation and Enhancing Fidelity in Distributed Quantum Computing using a Multi-Objective Evolutionary Algorithm

Abstract:

Quantum computing is considered a promising technology for solving tasks that are impossible even for classical computing. However, individual quantum computers are reaching their limits due to various challenges and can therefore only provide a limited number of freely available qubits. This limitation can be overcome by implementing the distributed quantum computing (DQC), a concept that increases the number of available qubits by connecting multiple quantum computers via a quantum network. Within such a system qubits are transferred from one quantum computer to another using quantum teleportation, a resource-intensive but indispensable protocol for communication in the DQC. Minimizing the number of teleportation is therefore essential, but carries the risk of affecting the functionality of the circuit when removing global gates that rely on teleportation. To overcome these challenges this paper presents an multi-objective evolutionary algorithm (EA) that leverages mechanisms such as crossover, mutation and selection to minimize the number of quantum teleportations while maximizing fidelity, which is a measure of similarity. The EA was tested on a set of QFT benchmark circuits and random circuits to evaluate its effectiveness in solving the problem. The results demonstrate that the evolutionary algorithm can significantly reduce the number of teleportations while maintaining fidelity above the threshold of 0.9. In comparison to the Kernighan-Lin-Algorithm, which only provides local optima, this approach consistently achieves better results.

Author:

Abasin Omerzai

Advisors:

Leo Sünkel, Thomas Gabor, Michael Kölle, Claudia Linnhoff-Popien

Student Thesis | Published January 2025 | Copyright © QAR-Lab
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