SAT + NAUTY: Orderly Generation of Small Kochen-Specker Sets Containing the Smallest State-independent Contextuality Set

arXiv:2604.199475.2h-index: 11
Predicted impact top 79% in LO · last 90 daysOriginality Incremental advance
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This work addresses a foundational problem in quantum contextuality for researchers in quantum foundations and computational physics, but it is incremental as it builds on known sets and methods.

The authors tackled the problem of exhaustively enumerating small Kochen-Specker sets in dimension 3, specifically extensions of the 13-ray Yu-Oh set, and found that the 33-ray set discovered by Schütte is the smallest three-dimensional KS set containing the complete 25-ray state-independent contextuality set, with the search completed in 1,641 CPU hours.

We present a search for small Kochen-Specker (KS) sets in dimension 3, specifically targeting extensions of the 13-ray Yu-Oh set, which has been proven to be the minimal witness to state-independent contextuality. To enable this search, we introduce a novel SAT-based orderly generation framework integrating recursive canonical labeling (RCL) with the graph isomorphism tool NAUTY. We demonstrate that previous SAT approaches relying on lexicographical canonicity suffer from exponential scaling on canonical graphs. This limitation renders them intractable on the large instances (25 to 33 vertices) encountered in our search, whereas our RCL check maintains consistent millisecond-level performance, effectively eliminating the bottleneck. Overcoming this bottleneck allows us to perform the first exhaustive enumeration of all KS sets with up to 33 rays containing the complete 25-ray state-independent contextuality (SI-C) set obtained by rigid extensions of the Yu-Oh set in 1,641 CPU hours. We found and verified that the 33-ray set discovered by Schütte is the smallest three-dimensional KS set containing the complete 25-ray SI-C set. All non-existence results are backed by independently verifiable proof certificates via an extension of the DRAT proof format.

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