Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 73 tok/s
Gemini 2.5 Pro 42 tok/s Pro
GPT-5 Medium 26 tok/s Pro
GPT-5 High 34 tok/s Pro
GPT-4o 96 tok/s Pro
Kimi K2 191 tok/s Pro
GPT OSS 120B 454 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code (2411.10343v1)

Published 15 Nov 2024 in quant-ph

Abstract: To avoid prohibitive overheads in performing fault-tolerant quantum computation, the decoding problem needs to be solved accurately and at speeds sufficient for fast feedback. Existing decoding systems fail to satisfy both of these requirements, meaning they either slow down the quantum computer or reduce the number of operations that can be performed before the quantum information is corrupted. We introduce the Local Clustering Decoder as a solution that simultaneously achieves the accuracy and speed requirements of a real-time decoding system. Our decoder is implemented on FPGAs and exploits hardware parallelism to keep pace with the fastest qubit types. Further, it comprises an adaptivity engine that allows the decoder to update itself in real-time in response to control signals, such as heralded leakage events. Under a realistic circuit-level noise model where leakage is a dominant error source, our decoder enables one million error-free quantum operations with 4x fewer physical qubits when compared to standard non-adaptive decoding. This is achieved whilst decoding in under 1 us per round with modest FPGA resources, demonstrating that high-accuracy real-time decoding is possible, and reducing the qubit counts required for large-scale fault-tolerant quantum computation.

Citations (2)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.