A Resource Estimation Model for the Hardware-Software Co-Design of Distributed Quantum Architectures

2026-07-24

AI Summary

This work addresses the joint optimization of communication and computational resources under limited physical qubits in distributed quantum computing by introducing, for the first time, the Economic Order Quantity (EOQ) model from perishable inventory theory into entanglement resource management. The proposed framework accounts for both latency and decoherence costs to estimate resource requirements accurately. By co-optimizing the static allocation of communication qubits in the hardware architecture and dynamic reservation strategies in the compiler, the approach achieves Pareto-optimal entanglement distribution. The method is applicable to both heterogeneous and homogeneous architectures, providing optimal static allocation schemes and dynamic reservation guidelines tailored to each, thereby significantly enhancing overall system performance.

Abstract

In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits to generate and distribute entanglement. Because the number of physical qubits within a QPU is finite, a trade-off emerges where allocating more communication qubits increases the capacity of quantum channels for concurrent non-local operations, but reduces the number of computational qubits available for local gate operations. Distributed quantum compilation routinely ignores this channel capacity, while hardware architects lack a method to determine it prior to quantum circuit partitioning. Moreover, scheduling entanglement on demand introduces severe latency, whereas pre-fetching exposes stored pairs to decoherence. We propose an economic order quantity model from perishable inventory theory to optimize the trade-off between entanglement distribution latency and the time cost of decoherence. The resulting estimate is driven by algorithmic demand and physical constraints, offering a dual application for the hardware-software co-design of high-performance DQC: for hardware architects, it gives the optimal allocation of dedicated communication qubits in static heterogeneous architectures; for compiler developers, it gives the optimal number to reserve dynamically in homogeneous architectures.

Problem

• distributed quantum computing
• communication qubits
• entanglement distribution
• resource allocation
• decoherence

Innovation

• distributed quantum computing
• entanglement distribution
• hardware-software co-design
• economic order quantity model
• quantum resource estimation