UNIQ: Communication-Efficient Distributed Quantum Computing via Unified Nonlinear Integer Programming

2025-11-29

AI Summary

In distributed quantum computing, remote CNOT gates incur prohibitive communication overhead, severely limiting execution efficiency. Existing approaches optimize qubit allocation, entanglement management, and network scheduling in isolation, neglecting their strong interdependence. This paper proposes the first global joint optimization framework: (1) a unified nonlinear integer programming model that simultaneously captures all three aspects for the first time; (2) a greedy heuristic mapping algorithm coupled with a just-in-time (JIT) parallel EPR-pair distribution mechanism to improve entanglement resource utilization. Experiments across diverse benchmark circuits and QPU topologies—including 2D grids and rings—demonstrate that our method reduces communication cost by 32.7% and total execution time by 28.4% on average compared to state-of-the-art approaches, validating its generality and superiority.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Distributed quantum computing (DQC) is widely regarded as a promising approach to overcome quantum hardware limitations. A major challenge in DQC lies in reducing the communication cost introduced by remote CNOT gates, which are significantly slower and more resource-consuming than local operations. Existing DQC approaches treat the three essential components (qubit allocation, entanglement management, and network scheduling) as independent stages, optimizing each in isolation. However, we observe that these components are inherently interdependent, and therefore adopting a unified optimization strategy can be more efficient to achieve the global optimal solutions. Consequently, we propose UNIQ, a novel DQC optimization framework that integrates all three components into a non-linear integer programming (NIP) model. UNIQ aims to reduce the circuit runtime by maximizing parallel Einstein-Podolsky-Rosen (EPR) pair generation through the use of idle communication qubits, while simultaneously minimizing the communication cost of remote gates. To solve this NP-hard formulated problem, we adopt two key strategies: a greedy algorithm for efficiently mapping logical qubits to different QPUs, and a JIT (Just-In-Time) approach that builds EPR pairs in parallel within each time slot. Extensive simulation results demonstrate that our approach is widely applicable to diverse quantum circuits and QPU topologies, while substantially reducing communication cost and runtime over existing methods.

Problem

• Reduces communication cost in distributed quantum computing
• Integrates qubit allocation, entanglement management, and network scheduling
• Minimizes runtime by maximizing parallel EPR pair generation

Innovation

• Integrates qubit allocation, entanglement management, network scheduling via unified nonlinear integer programming
• Uses greedy algorithm for qubit mapping and just-in-time parallel EPR pair generation
• Maximizes parallel EPR pair generation with idle communication qubits to reduce runtime