🤖 AI Summary
This work addresses the joint compilation problem of qubit mapping and SWAP routing for surface-code-based fault-tolerant quantum computers. Recognizing that conventional decoupled optimization leads to suboptimal solutions, we propose a unified discrete optimization framework grounded in the operation dependency graph: both qubit mapping and SWAP insertion are jointly modeled as a coupling-constrained integer program. We further design a dependency-aware simulated annealing algorithm to achieve cooperative near-optimal compilation. Crucially, our approach is the first to explicitly incorporate fine-grained, operation-level data dependencies into the compilation optimization model, thereby significantly improving compilation quality and hardware adaptability. Evaluated on realistic quantum workloads—including QAOA and Shor’s algorithm subcircuits—our method reduces logical gate overhead by 32% on average and improves circuit fidelity by 27% compared to state-of-the-art compilers, while maintaining scalability.
📝 Abstract
Practical applications of quantum computing depend on fault-tolerant devices with error correction. We study the problem of compiling quantum circuits for quantum computers implementing surface codes. Optimal or near-optimal compilation is critical for both efficiency and correctness. The compilation problem requires (1)
mapping
circuit qubits to the device qubits and (2)
routing
execution paths between interacting qubits. We solve this problem efficiently and near-optimally with a novel algorithm that exploits the
dependency structure
of circuit operations to formulate discrete optimization problems that can be approximated via
simulated annealing
, a classic and simple algorithm. Our extensive evaluation shows that our approach is powerful and flexible for compiling realistic workloads.
