Hardware-aware quantum error correction using cryogenic FeFET-weighted Max-SAT decoding
Abstract
Abstract Quantum error correction is indispensable for fault-tolerant quantum computing, yet the computational cost of classical decoding is expected to increase substantially as quantum processors scale. Optimization-based decoders offer an attractive alternative because their objective functions can be mapped onto emerging hardware accelerators. However, existing hardware-focused studies typically rely on idealized device models or synthetic fault injection, leaving the influence of experimentally measured device behaviour largely unexplored. Here, we present a device-oriented weighted Max-SAT decoding framework using experimentally characterized cryo-genic HZO ferroelectric field-effect transistors. Differential read currents measured at 10 K are directly translated into optimization coefficients, allowing transistor-level behaviour to define the decoding landscape. Stable differential current margins are maintained through approximately 10 5 program/erase cycles, preserving logical error rates comparable to ideal weights, whereas endurance-induced degradation progressively reduces decoding performance. This measurement-anchored framework directly connects cryogenic device physics with optimization-based quantum error correction, providing a realistic pathway toward future hardware-assisted quantum decoders.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.