5 papers
Quantum annealers as programmable thermal machines
Jakub Pawłowski, Tomasz Śmierzchalski, Fengping Jin +3
Programmable quantum annealers are used for optimization, probabilistic sampling, and simulation, but their performance is commonly reported without the energy exchanged during com…
Neural and Tensor Networks in the Study of Quantum Annealing Processors
Tomasz Åmierzchalski
Quantum annealing targets low-energy solutions of Ising/QUBO problems, but reliable assessment requires more than best-energy comparisons. This dissertation develops a benchmarking…
Limitations of tensor network approaches for optimization and sampling: A comparison to quantum and classical Ising machines
Anna Maria Dziubyna, Tomasz Åmierzchalski, BartÅomiej Gardas +2
Optimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. To provide a new…
SpinGlassPEPS.jl: Tensor-network package for Ising-like optimization on quasi-two-dimensional graphs
Tomasz Åmierzchalski, Anna M. Dziubyna, Konrad JaÅowiecki +4
This work introduces SpinGlassPEPSjl, a software package implemented in Julia, designed to find low-energy configurations of generalized Potts models, including Ising and QUBO p…
Hybrid quantum-classical computation for automatic guided vehicles scheduling
Tomasz Åmierzchalski, Jakub PawÅowski, Artur Przybysz +6
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 qu…