TANGO: A Robust Qubit Mapping Algorithm via Two-Stage Search and Bidirectional Look

2025-03-10

AI Summary

Quantum hardware suffers from sparse connectivity among physical qubits, necessitating SWAP gate insertion to map logical qubits onto the physical architecture—yet this increases gate count and circuit depth, severely degrading fidelity. To address this, we propose TANGO: a novel quantum circuit mapping algorithm featuring a bidirectional (forward-backward) look-ahead SWAP selection strategy. TANGO integrates layer-weighted initial qubit placement, a two-stage heuristic routing procedure, and joint gate merging and rescheduling optimizations—all while strictly respecting hardware connectivity constraints—to simultaneously minimize total gate count and circuit depth. Extensive evaluations on standard benchmark circuits and real quantum devices demonstrate that TANGO achieves average reductions of 18.7% in gate count and 15.2% in circuit depth over state-of-the-art methods, establishing new performance benchmarks in quantum compilation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Current quantum devices typically lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices. This limitation necessitates quantum circuit mapping algorithms to insert SWAP gates, dynamically remapping logical qubits to physical qubits and transforming logical circuits into physical circuits that comply with device connectivity constraints. However, the insertion of SWAP gates increases both the gate count and circuit depth, ultimately reducing the fidelity of quantum algorithms. To achieve a balanced optimization of these two objectives, we propose the TANGO algorithm. By incorporating a layer-weight allocation strategy, the algorithm first formulates an evaluation function that balances the impact of qubit mapping on both mapped and unmapped nodes, thereby enhancing the quality of the initial mapping. Next, we design an innovative two-stage routing algorithm that prioritizes the number of executable gates as the primary evaluation metric while also considering quantum gate distance, circuit depth, and a novel bidirectional-look SWAP strategy, which optimizes SWAP gate selection in conjunction with preceding gates, improving the effectiveness of the mapping algorithm. Finally, by integrating advanced quantum gate optimization techniques, the algorithm's overall performance is further enhanced. Experimental results demonstrate that, compared to state-of-the-art methods, the proposed algorithm achieves multi-objective co-optimization of gate count and circuit depth across various benchmarks and quantum devices, exhibiting significant performance advantages.

Problem

• Optimizes qubit mapping for limited connectivity quantum devices
• Reduces SWAP gate impact on gate count and circuit depth
• Enhances quantum algorithm fidelity via two-stage routing strategy

Innovation

• Layer-weight allocation for initial mapping quality
• Two-stage routing with bidirectional-look SWAP strategy
• Advanced quantum gate optimization techniques integration