aiqbench analysis
This paper presents a rigorous construction of high-rate quantum subsystem codes on rectangular grids, offering analytically proven distances and optimized tradeoffs between encoding rate and measurement weight without relying on numerical searches. By leveraging Lean code for formal verification, the authors provide a reproducible framework for designing fault-tolerant architectures that scale efficiently within strict qubit budgets. The work advances practical quantum error correction by delivering concrete parameters for logical qubit protection across various distances using low-weight measurements.
High-Rate Quantum Codes with Proven Distance and Low-Weight Measurements
arXiv:2610.02621v1 Announce Type: new Abstract: We study a family of quantum subsystem codes defined on rectangular coordinate grids of any dimension, with even side lengths of at least four. The directly measured operators act along coordinate lines. For every member of the family, we derive the number of protected logical qubits and prove its dressed distance without a numerical search. To illustrate the resulting design choices, we impose a budget of at most 10,000 data qubits and determine the complete tradeoff between encoding rate and measurement weight within this family at distances 16, 32, and 64. At distance 16, one code protects 4,096 logical qubits among 10,000 data qubits using weight-ten measurements. At distances 32 and 64, codes with 7,776 and 9,216 data qubits protect 1,024 and 256 logical qubits, respectively, using weight-six measurements. We include and explain Lean code snippets throughout the paper to guide readers through the formal verification of our subsystem-code construction and its dressed and bare distances.
Abstract:We study a family of quantum subsystem codes defined on rectangular coordinate grids of any dimension, with even side lengths of at least four. The directly measured operators act along coordinate lines. For every member of the family, we derive the number of protected logical qubits and prove its dressed distance without a numerical search. To illustrate the resulting design choices, we impose a budget of at most 10,000 data qubits and determine the complete tradeoff between encoding rate and measurement weight within this family at distances 16, 32, and 64. At distance 16, one code protects 4,096 logical qubits among 10,000 data qubits using weight-ten measurements. At distances 32 and 64, codes with 7,776 and 9,216 data qubits protect 1,024 and 256 logical qubits, respectively, using weight-six measurements. We include and explain Lean code snippets throughout the paper to guide readers through the formal verification of our subsystem-code construction and its dressed and bare distances.
Several Lean snippets in main text. 18 pages
arXiv:2610.02621 [quant-ph]
(or arXiv:2610.02621v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.02621 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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