Fault-Tolerant Logical Operations and Efficient State Preparation in Modular Quantum Architectures with Noisy Interfaces

Research Overview

This paper investigates the feasibility of scaling quantum computation through modular architectures, where multiple Quantum Processing Units (QPUs) are interconnected via shared entanglement. While monolithic quantum computers face physical resource limitations, modular systems offer a path to scalability. The authors focus on the performance of fault-tolerant logical operations—specifically nonlocal CNOT gates—implemented via lattice surgery across noisy inter-QPU interfaces.

Approach and Findings

Using circuit-level simulations of the rotated surface code, the researchers analyzed how noise at the module interfaces affects the overall fault-tolerance threshold. They discovered that these architectures are remarkably resilient: the threshold is primarily dominated by local gate noise, and the system can tolerate interface noise up to ten times higher than local noise with minimal performance degradation.

Beyond gate operations, the authors addressed the challenge of preparing distributed logical GHZ states. They demonstrated that minimizing the number of ancilla patches required for these states is mathematically equivalent to solving a minimum vertex-cover problem on a graph. To solve this efficiently, they introduced a heuristic algorithm (Star-Search) that outperforms standard breadth-first and depth-first search approaches in reducing resource overhead.

This work provides quantitative evidence that distributed quantum error correction is a viable strategy for building large-scale quantum computers. By establishing that modular interfaces do not significantly compromise the fault-tolerance threshold, the study justifies the development of networked quantum processors. Furthermore, the resource-allocation heuristics provide practical guidance for optimizing the trade-offs between ancilla count, execution time, and entanglement consumption in real-world modular designs.