The Q-Spellbook: Crafting Surface Code Layouts and Magic State Protocols for Large-Scale Quantum Computing

TL;DR

This paper introduces a framework for optimizing large-scale quantum architectures by balancing surface code layouts and magic state protocols, improving efficiency and scalability in quantum computing.

Contribution

It presents a novel optimization framework for data block layouts and magic state distillation protocols, with comparative analysis of algorithms and a heuristic for practical algorithm selection.

Findings

Brute force yields optimal results in tile minimization.

Greedy algorithm deviates by 7% in steps minimization.

Dynamic programming matches brute force in tile minimization.

Abstract

Quantum error correction is a cornerstone of reliable quantum computing, with surface codes emerging as a prominent method for protecting quantum information. Surface codes are efficient for Clifford gates but require magic state distillation protocols to process non-Clifford gates, such as T gates, essential for universal quantum computation. In large-scale quantum architectures capable of correcting arbitrary circuits, specialized surface codes for data qubits and distinct codes for magic state distillation are needed. These architectures can be organized into data blocks and distillation blocks. The system works by having distillation blocks produce magic states and data blocks consume them, causing stalls due to either a shortage or excess of magic states. This bottleneck presents an opportunity to optimize quantum space by balancing data and distillation blocks. While prior research offers insights into selecting distillation protocols and estimating qubit requirements, it lacks a tailored optimization approach. We present a framework for optimizing large-scale quantum architectures, focusing on data block layouts and magic state distillation protocols. We evaluate three data block layouts and four distillation protocols under three optimization strategies: minimizing tiles, minimizing steps, and achieving a balanced trade-off. Through a comparative analysis of brute force, dynamic programming, greedy, and random algorithms, we find that brute force delivers optimal results, while greedy deviates by 7% for minimizing steps and dynamic programming matches brute force in tile minimization. We observe that total steps increase with columns, while total tiles scale with qubits. Finally, we propose a heuristic to help users select algorithms suited to their objectives, enabling scalable and efficient quantum architectures.