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arXiv:2606.13570

Approximability limits for bounded-degree max-LINSAT and implications for decoded quantum interferometry

Maximilian J. Kramer, Carsten Schubert, Jens Eisert

quant-phcs.CCcs.DSSubmitted Jun 11, 2026• Announced Jun 12, 2026• 0 citations
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Abstract

For general max-k-XORSAT with $k \geq 3$, no polynomial-time algorithm can do substantially better than random guessing on worst-case instances unless $\mathsf{P} = \mathsf{NP}$: approximating beyond the random-assignment value of $1/2$ is $\mathsf{NP}$-hard. The picture changes when each variable appears in at most $D$ constraints. In that bounded-degree setting, polynomial-time algorithms can provably beat the random baseline by an additive amount of order $1/\sqrt{D}$. For Boolean instances, this scaling is known to be optimal: the matching hardness result is due to Trevisan, while the corresponding algorithmic guarantee was established by Barak et al. Whether the same holds over general finite fields, and what it implies for quantum algorithms, has not been established. We make this connection explicit and extend the hardness to max-E$k$-LINSAT$(q,r)$ with bounded degree $D$ and over arbitrary finite fields $\mathbb{F}_q$, proving that it is $\mathsf{NP}$-hard to exceed $r/q + \mathcal{O}_{q,r}(1/\sqrt{D})$. These results provide the complexity-theoretic benchmark for the bounded-degree instances targeted by decoded quantum interferometry (DQI), QAOA, and classical heuristics. Any quantum advantage on bounded-degree instances is therefore confined to the constant prefactor. We further show that in the context of DQI and on $(k,D)$-regular instances, this prefactor is sensitive to the nature of the decoder: DQI with classical decoders faces an information-theoretic $1/\sqrt{D \log D}$ barrier that prevents it from matching the hardness scaling, while DQI with quantum decoders is compatible with the $1/\sqrt{D}$ scaling -- identifying quantum decoding as the key ingredient for matching the complexity-theoretic scaling with DQI.

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18 pages, 2 figures

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For general max-k-XORSAT with k≥3k \geq 3k≥3, no polynomial-time algorithm can do substantially better than random guessing on worst-case instances unless P=NP\mathsf{P} = \mathsf{NP}P=NP: approximating beyond the random-assignment value of 1/21/21/2 is NP\mathsf{NP}NP-hard. The picture changes when each variable appears in at most DDD constraints. In that bounded-degree setting, polynomial-time algorithms can provably beat the random baseline by an additive amount of order 1/D1/\sqrt{D}1/D​. For Boolean instances, this scaling is known to be optimal: the matching hardness result is due to Trevisan, while the corresponding algorithmic guarantee was established by Barak et al. Whether the same holds over general finite fields, and what it implies for quantum algorithms, has not been established. We make this connection explicit and extend the hardness to max-Ekkk-LINSAT(q,r)(q,r)(q,r) with bounded degree DDD and over arbitrary finite fields Fq\mathbb{F}_qFq​, proving that it is NP\mathsf{NP}NP-hard to exceed r/q+Oq,r(1/D)r/q + \mathcal{O}_{q,r}(1/\sqrt{D})r/q+Oq,r​(1/D​). These results provide the complexity-theoretic benchmark for the bounded-degree instances targeted by decoded quantum interferometry (DQI), QAOA, and classical heuristics. Any quantum advantage on bounded-degree instances is therefore confined to the constant prefactor. We further show that in the context of DQI and on (k,D)(k,D)(k,D)-regular instances, this prefactor is sensitive to the nature of the decoder: DQI with classical decoders faces an information-theoretic 1/Dlog⁡D1/\sqrt{D \log D}1/DlogD​ barrier that prevents it from matching the hardness scaling, while DQI with quantum decoders is compatible with the 1/D1/\sqrt{D}1/D​ scaling -- identifying quantum decoding as the key ingredient for matching the complexity-theoretic scaling with DQI.
We establish tight inapproximability bounds for max-LINSAT, the problem of maximizing the number of satisfied linear constraints over the finite field Fq\mathbb{F}_qFq​, where each constraint accepts rrr values. Specifically, we prove by a direct reduction from Håstad's theorem that no polynomial-time algorithm can exceed the random-assignment ratio r/qr/qr/q by any constant, assuming P≠NP\mathsf{P} \neq \mathsf{NP}P=NP. This threshold coincides with the ℓ/m→0\ell/m \to 0ℓ/m→0 limit of the semicircle law governing decoded quantum interferometry (DQI), where ℓ\ellℓ is the decoding radius of the underlying code. Together, these observations delineate the boundary between worst-case hardness and potential quantum advantage, showing that any algorithm surpassing r/qr/qr/q must exploit instance structure beyond what is present in the hard instances produced by PCP reductions.
Decoded Quantum Interferometry (DQI) reduces optimization problems with two-variable constraints to decoding cycle codes. For one such problem, namely MaxCut, prior work showed that DQI achieves a nontrivial satisfaction fraction guarantee only on linear-girth graphs, for which MaxCut is classically easy. However, these no-go results rely on minimum distance assumptions that do not represent true decodability thresholds for common noise channels, thus underestimating actual DQI performance. To estimate the true performance, we derive DQI satisfaction guarantees in the presence of imperfect decoding for fixed instances and generalize prior results for random instances. We then study homological cycle codes over arbitrary finite fields, and show that minimum-weight decoding is NP-hard for every field of size q>2q>2q>2, in contrast with known results for binary cycle codes. On the Linial--Simkin ensemble of regular graphs with logarithmic girth, we prove maximum-likelihood recovery bounds and a strong converse for a family of non-uniform additive noise channels. Moreover, LP decoders provide polynomial-time recovery of a positive fraction of randomly located errors with arbitrary values. These results allow us to prove upper and lower bounds on the approximate optima achievable by DQI when restricted to classical decoders. These bounds rule out quantum advantage in the regimes we analyze but also identify a family of regular Max-kkk-Cut instances on which DQI efficiently achieves a nontrivial satisfaction fraction guarantee.
The Optimal Polynomial Intersection (OPI) problem asks us to find a low-degree polynomial over a finite field whose values lie in prescribed subsets on as many given inputs as possible. Decoded quantum interferometry (DQI) gives a quantum algorithm for OPI in parameter regimes beyond those achieved by the best known classical heuristics. Follow-up works improve the parameter regimes, but their analyses are limited to average-case settings. Recently, Sun and Wootters showed that, even in the worst case, OPI has a solution in a larger parameter regime than the one covered by DQI. However, they left open whether one can design a quantum algorithm that solves OPI in the worst-case beyond the DQI regime. We give such a quantum algorithm. As a byproduct, we also improve the existential bound of Sun and Wootters in certain parameter regimes. In particular, when each subset contains roughly half of the field elements, our algorithm finds a solution with satisfaction rate s=1s=1s=1 whenever the rate satisfies R>0.75R>0.75R>0.75. This matches the previous average-case bound, whereas DQI cannot achieve s=1s=1s=1 unless R=1R=1R=1. Our existential bound guarantees the existence of a solution when R>0.7158R> 0.7158R>0.7158, improving over the previous threshold R>0.7495R>0.7495R>0.7495. More generally, our existential results extend to the Max-LINSAT problem with respect to arbitrary maximum distance separable (MDS) codes. The corresponding algorithmic results apply only to MDS codes whose dual admits an efficient list decoder. Our results are obtained through a novel application of a Brascamp--Lieb-type inequality in the MDS setting, which may have further applications.
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A recent paper by Jordan et al. introduced Decoded Quantum Interferometry (DQI), a novel quantum algorithm that uses the quantum Fourier transform to reduce linear optimization problems -- max-XORSAT and max-LINSAT -- to decoding problems. In this paper, we extend DQI to optimization problems involving quadratic constraints, which we call max-QUADSAT. Leveraging a connection to quadratic Gauss sums, we give an efficient algorithm to prepare the DQI state for max-QUADSAT (DISCLAIMER: A mistake was found in one of the algorithm's steps, which invalidates this result unless a fix is found). To demonstrate that our algorithm achieves a quantum advantage, we introduce the Quadratic Optimal Polynomial Intersection (quadratic-OPI) problem, a restricted variant of OPI for which, to our knowledge, the standard DQI framework offers no algorithmic speedup. We show that quadratic-OPI is an instance of max-QUADSAT and use our algorithm to optimize it. Lastly, we present a new generalized proof of the "semicircle law" for the fraction of satisfied constraints, generalizing it to any DQI state of problems where the distribution of the number of satisfied constraints for a random assignment is sufficiently close to a binomial distribution. This condition holds exactly for the DQI state of max-LINSAT, and approximately holds in the max-QUADSAT case, with the approximation becoming exponentially better as the problem size increases. This establishes performance guarantees for our algorithm.
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