Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit. This hybrid methodology performs comparably to existing classical solvers. However, due to the proprietary nature of the software, the precise contribution of quantum computation remains unclear. Our analysis focuses on a practical, business-oriented scenario: scheduling AGVs within a factory constrained by limited space, simulating a realistic production setting. Our approach maps a realistic AGVs problem onto one reminiscent of railway scheduling and demonstrates that the AGVs problem is better suited to quantum computing than its railway counterpart, the latter being denser in terms of the average number of constraints per variable. The main idea here is to highlight the potential usefulness of a hybrid approach for handling AGVs scheduling problems of practical sizes. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.
00448nas a2200145 4500000000100000000000100001008004100002100002600043700002100069700002200090700002300112245010500135856005500240490000700295 2024 d 1 aTomasz Śmierzchalski 1 aZakaria Mzaouali 1 aSebastian Deffner 1 aBartłomiej Gardas 00 aEfficiency Optimization in Quantum Computing: Balancing Thermodynamics and Computational Performance uhttps://www.nature.com/articles/s41598-024-55314-z 0 v14 02601nas a2200181 4500000000100000000000100001008004100002260001200043100001800055700002600073700002300099700001500122700001900137245012900156856006200285490000700347520206500354 2025 d c05/2025 1 aAnna Dziubyna 1 aTomasz Śmierzchalski 1 aBartłomiej Gardas 1 aMarek Rams 1 aMasoud Mohseni 00 aLimitations of tensor-network approaches for optimization and sampling: A comparison to quantum and classical Ising machines uhttps://link.aps.org/doi/10.1103/PhysRevApplied.23.054049 0 v23 3 aOptimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. Here, to provide an additional perspective, we develop a heuristic tensor-network-based algorithm to reveal the low-energy spectra of Ising spin-glass systems with interaction graphs relevant to present-day quantum annealers. Our deterministic approach combines a branch-and-bound search strategy with an approximate calculation of marginals via tensor-network contractions. Its application to quasi-two-dimensional lattices with large unit cells of up to 24 spins, realized in current quantum annealing processors, requires a dedicated approach that uses sparse structures in the tensor-network representation and GPU hardware acceleration. We benchmark our approach on random problems defined on Pegasus and Zephyr graphs with up to a few thousand spins, comparing it against the D-Wave Advantage quantum annealer and the simulated bifurcation algorithm, with the latter representing an emerging class of classical Ising solvers. In addition to examining the quality of the best solutions, we compare the diversity of low-energy states sampled by all the solvers. For the largest considered independent and identically distributed problems with over 5000 spins, the state-of-the-art tensor-network approaches lead to solutions that are 0.1% to 1% worse than the best solutions obtained by Ising machines while being 2 orders of magnitude slower. We attribute these results to approximate contraction failures. For embedded tile planting instances, our approach reaches approximately 0.1% from the planted ground state, a factor of 3 better than the Ising solvers. While all three methods can output diverse low-energy solutions—e.g., differing by at least a quarter of spins with energy error below 1%—our deterministic branch-and-bound approach finds sets of a few such states at most. In contrast, both Ising machines prove capable of sampling sets of thousands of such solutions.
01466nas a2200205 4500000000100000000000100001008004100002100002600043700001800069700002300087700002100110700001900131700002300150700001500173245010500188856007200293490000700365520087400372022001401246 2025 d 1 aTomasz Śmierzchalski 1 aAnna Dziubyna 1 aKonrad Jałowiecki 1 aZakaria Mzaouali 1 aŁukasz Pawela 1 aBartłomiej Gardas 1 aMarek Rams 00 aSpinGlassPEPS.jl: Tensor-network package for Ising-like optimization on quasi-two-dimensional graphs uhttps://www.sciencedirect.com/science/article/pii/S2352711025002249 0 v31 3 aThis work introduces SpinGlassPEPS.jl, a software package implemented in Julia, designed to find low-energy configurations of generalized Potts models, including Ising and QUBO problems, utilizing heuristic tensor network contraction algorithms on quasi-2D geometries. In particular, the package employs the Projected Entangled-Pairs States to approximate the Boltzmann distribution corresponding to the model’s cost function. This enables an efficient branch-and-bound search (within the probability space) that exploits the locality of the underlying problem’s topology. As a result, our software enables the discovery of low-energy configurations for problems on quasi-2D graphs, particularly those relevant to modern quantum annealing devices. The modular architecture of SpinGlassPEPS.jl supports various contraction schemes and hardware acceleration.
a2352-7110 00409nas a2200145 4500000000100000008004100001100002000042700002600062700001700088700002300105700002200128700002100150245005500171856003700226 2026 d 1 aJakub Pawlowski 1 aTomasz Śmierzchalski 1 aFengping Jin 1 aBartłomiej Gardas 1 aSebastian Deffner 1 aZakaria Mzaouali 00 aQuantum annealers as programmable thermal machines uhttps://arxiv.org/abs/2608.04564