Optimizing FTQC Programs through QEC Transpiler and Architecture Codesign

TL;DR

This paper presents TACO, a codesign framework that optimizes fault-tolerant quantum computing by reducing Clifford gate costs and improving architecture efficiency, enabling higher throughput with fewer resources.

Contribution

TACO introduces a novel codesign approach that jointly optimizes FTQC compilers and architectures, significantly reducing Clifford gates and resource requirements.

Findings

Achieves 91.7% reduction in Clifford gates

Enables single-gate-per-cycle throughput

Reduces logical qubit tiles to 1.5n+4 from 2n+√8n+1

Abstract

Fault-tolerant quantum computing (FTQC) is essential for executing reliable quantum computations of meaningful scale. Widely adopted QEC codes for FTQC, such as the surface code and color codes, utilize Clifford+T gate sets, where T gates are generally considered as the primary bottleneck due to their high resource costs. Recent advances in T gate optimization have significantly reduced this overhead, making Clifford gate complexity an increasingly critical bottleneck that remains largely unaddressed in present FTQC compiler and architecture designs. To address this new bottleneck, this paper introduces TACO, a \textbf{T}ranspiler-\textbf{A}rchitecture \textbf{C}odesign \textbf{O}ptimization framework, to reduce Clifford cost. Specifically, we observe that, through codesign, insights rooted in the FTQC architecture can inform novel circuit-level optimizations for FTQC compilers. These optimizations, in turn, provide new opportunities to redesign and improve the underlying architecture. Evaluations show that TACO achieves an average 91.7% reduction in Clifford gates across diverse quantum circuits and significantly enhances gate parallelism compared to Pauli-based approaches. These improvements enable an efficient FTQC architecture that can achieve single-gate-per-cycle throughput using only $1.5n+4$ logical qubit tiles, considerably pushing forward upon previously proposed designs that require $2n+\sqrt{8n}+1$ tiles. These results highlight the benefits of bidirectional optimization through codesign. TACO will be open-source.