SearcharxivSearch

arXiv · 2607.04857

Terastate-per-second QUBO Brute-Force on a Single GPU: A Matrix Prefix-Suffix Decomposition

Abstract

This paper presents a parallel QUBO exhaustive search algorithm for dense matrices, based on a prefix-suffix decomposition and Gray code ordering. The algorithm achieves O(1) per-state complexity: for the QUBO objective function computation only one arithmetic operation per state is performed. An adjustable energy components cache size enables placement in the fastest available memory tier. This reduces memory bandwidth requirements to a negligible level and transforms the problem from memory-bound to compute-bound. Our CUDA-based implementation achieves a state-of-the-art evaluation rate of $7.5\times10^{12}$ states per second on a single GPU, setting a new performance benchmark for the full-space-search subclass of exact solvers.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Aleksandr Maltsev, Mikhail Remnev, Alexey Kapranov, Ekaterina Krivtsova. 2026-07-06. Terastate-per-second QUBO Brute-Force on a Single GPU: A Matrix Prefix-Suffix Decomposition. https://arxiv.org/abs/2607.04857

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS