Co-design threading model and circuit cutting for static and adaptive quantum circuits

Abstract As quantum computing rapidly evolves, scaling quantum algorithms to utilize multiple quantum processing units (QPUs) becomes crucial for overcoming the limitations of current noisy intermediate-scale quantum (NISQ) devices. This paper focuses on distributed quantum computing (DQC), specifically targeting the challenges associated with circuit cutting and circuit distribution in multi-QPU environments. By leveraging techniques such as hypergraph partitioning and threading models, this paper presents alternative strategies for dividing and managing both static and adaptive quantum circuits across multiple QPUs. A central research question is how the partitioning and distribution of quantum circuits can be optimized to minimize communication overhead and maximize computational performance in multi-QPU systems, for static and adaptive quantum circuits. To answer this question, a hypergraph partitioning method is proposed to effectively segment large static quantum circuits into manageable subcircuits, ensuring minimal inter-QPU gate operations and reduced time for the partitioning process. Additionally, a co-design threading model is presented, tailored for adaptive quantum circuits. This category of circuits can dynamically adjust their sequence of gates in runtime, based on intermediate measurements and classical control flows, creating unique challenges for circuit partition. Finally, to support the coordination between circuit partitions with static and adaptive circuits, we propose a quantum resource manager (QRM) architecture, bridging the gap between partitioning techniques and practical coordination for a scalable quantum computing system with multi-QPU architecture.