Circuit Partitioning and Genetic Optimization for Efficient Qubit Distribution in Distributed Quantum Computing

Circuit Partitioning and Genetic Optimization for Efficient Qubit Distribution in Distributed Quantum Computing

Abstract:

Quantum computers are capable of solving specific computational problems in a time frame that is faster than that of a classical computer. The current era is that of Noisy Intermediate-Scale Quantum Computing, which is defined by the presence of noise that limits the capabilities of quantum computation. This presents a significant challenge in the development of large-scale quantum computers. The encoding of problems is accomplished through the use of quantum circuits comprising qubits. The distribution of qubits across quantum computers may facilitate the execution of larger circuits. In Distributed Quantum Computing, qubits are distributed across multiple Quantum Processing Units, which are connected via a quantum network. Alternatively, large quantum circuits can be run using circuit partitioning, which reduces depth and allows for parallel execution. However, partitioning a circuit might not take the constraints of the network into account. A method for integrating network constraints into the distribution process is through the use of an evolutionary algorithm. This approach has been employed to improve the distribution of qubits on a quantum network, albeit to a limited extent. The objective of this study is to consider the distinctive characteristics of a network and, moreover, the particular costs associated with each operation. To evaluate the efficiency of our algorithm, we conducted experiments on two distinct network topologies and compared the results to three baselines. The results demonstrate that our approach exhibits superior performance in the distribution of circuits across diverse topologies when compared to the established baselines.

Author:

Simon Schlichting

Advisors:

Leo Sünkel, Maximilian Zorn, Claudia Linnhoff-Popien

Student Thesis | Published December 2024 | Copyright © QAR-Lab
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