Paper 2025/1769

Average-Case Complexity of Quantum Stabilizer Decoding

Andrey Boris Khesin, University of Oxford
Jonathan Z. Lu, Massachusetts Institute of Technology
Alexander Poremba, Boston University
Akshar Ramkumar, California Institute of Technology
Vinod Vaikuntanathan, Massachusetts Institute of Technology
Abstract

Random classical linear codes are widely believed to be hard to decode. While slightly sub-exponential time algorithms exist when the coding rate vanishes sufficiently rapidly, all known algorithms at constant rate require exponential time. By contrast, the complexity of decoding a random quantum stabilizer code has remained an open question for quite some time. This work closes the gap in our understanding of the algorithmic hardness of decoding random quantum versus random classical codes. We prove that decoding a random stabilizer code with even a single logical qubit is at least as hard as decoding a random classical code at constant rate—the maximally hard regime. This result suggests that the easiest random quantum decoding problem is at least as hard as the hardest random classical decoding problem, and shows that any sub-exponential algorithm decoding a typical stabilizer code, at any rate, would immediately imply a breakthrough in cryptography. More generally, we also characterize many other complexity-theoretic properties of stabilizer codes. While classical decoding admits a random self-reduction, we prove significant barriers for the existence of random self-reductions in the quantum case. This result follows from new bounds on Clifford entropies and Pauli mixing times, which may be of independent interest. As a complementary result, we demonstrate various other self-reductions which are in fact achievable, such as between search and decision. We also demonstrate several ways in which quantum phenomena, such as quantum degeneracy, force several reasonable definitions of stabilizer decoding—all of which are classically identical—to have distinct or non-trivially equivalent complexity.

Metadata
Available format(s)
PDF
Category
Foundations
Publication info
Preprint.
Keywords
learning parity with noisequantum error correctionstabilizer codesdecodinglearning stabilizers with noise
Contact author(s)
lujz @ mit edu
poremba @ bu edu
aramkuma @ caltech edu
History
2025-10-03: approved
2025-09-27: received
See all versions
Short URL
https://ia.cr/2025/1769
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2025/1769,
      author = {Andrey Boris Khesin and Jonathan Z. Lu and Alexander Poremba and Akshar Ramkumar and Vinod Vaikuntanathan},
      title = {Average-Case Complexity of Quantum Stabilizer Decoding},
      howpublished = {Cryptology {ePrint} Archive, Paper 2025/1769},
      year = {2025},
      url = {https://eprint.iacr.org/2025/1769}
}
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