Tesseract: A Search-Based Decoder for Quantum Error Correction

TL;DR

Tesseract is an efficient, search-based decoder for quantum error correction that outperforms traditional methods in speed while maintaining accuracy, especially for certain quantum codes and protocols.

Contribution

This paper introduces Tesseract, a novel search-based decoding algorithm for quantum error correction that is faster than integer programming methods and effective for various quantum codes.

Findings

Tesseract is significantly faster than integer programming decoders.

Tesseract maintains comparable accuracy at moderate error rates.

Bivariate bicycle codes are more efficient than surface codes with Tesseract decoding.

Abstract

Tesseract is a Most-Likely Error decoder designed for low-density-parity-check quantum error-correcting codes. Tesseract conducts a search through a graph on the set of all subsets of errors to find the lowest cost subset of errors consistent with the input syndrome. Although this graph is exponentially large, the search can be made efficient in practice for random errors using $A^*$ search technique along with a few pruning heuristics. We show through benchmark circuits for surface, color, and bivariate-bicycle codes that Tesseract is significantly faster than integer programming-based decoders while retaining comparable accuracy at moderate physical error rates. We also find that Tesseract can decode transversal CNOT protocols for surface codes on neutral atom quantum computers. Finally, we compare surface code and bivariate bicycle code circuits, finding that the [[144,12,12]] bivariate bicycle code is $14\times$ to $19\times$ more efficient than surface codes using our most-likely error decoding, whereas using correlated matching and BP+OSD decoders would have implied only a $10\times$ improvement. Assuming instead that long-range couplers are $10\times$ noisier, the improvement drops to around $4\times$ using Tesseract or $2\times$ using correlated matching and BP+OSD.