arXiv subjects

Konrad Jałowiecki

Publications and source records attributed to Konrad Jałowiecki.

11 recordsLinked to original sources

arXiv2026

Omnisolver: An extensible interface to Ising spin-glass and QUBO solvers: adding a distributed GPU brute-force plugin

This software update extends Omnisolver with \texttt{omnisolver-bruteforce}, a first-class plugin for exact exhaustive search of QUBO and Ising instances on CUDA-enabled GPUs. The update contributes three components: packaging of the single-GPU brute-force kernel of [Computer Physics Communications 260, 107728, 2021] as a first-class Omnisolver plugin with a uniform Python and CLI interface, a Ray-based distributed framework that splits the search into $2^{k}$ fixed-variable subproblems and dispatches them across multiple GPUs and hosts, with a controller that gathers and merges partial results, and numerical stabilization of the fast \texttt{float32} ground-state path through compensated incremental updates, periodic exact energy re-anchoring, and best-buffer refresh. Stabilization activates automatically for $N \geq 40$ leaving the public sampler API is unchanged. On dense random Ising instances we measure exhaustive solve times up to $N=60$ on $8\times$ NVIDIA H100 (96 GB) GPUs, reaching $\approx\!3.15$ days at $N=60$ in close agreement with the empirical $t_{N+1}=2\,t_N$ doubling rule, with the distributed sampler reaching the full $\approx\!8\times$ speedup over a single H100 from $N\!\gtrsim\!44$ onwards. The plugin thereby serves as a practical ground-truth oracle for state-of-the-art classical heuristics such as the Simulated Bifurcation Machine.

physics.comp-ph↗

arXiv2025

SpinGlassPEPS.jl: Tensor-network package for Ising-like optimization on quasi-two-dimensional graphs

This 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.

quant-ph↗

arXiv2024

Omnisolver: an extensible interface to Ising spin glass solvers

We introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. We demonstrate the ease of use of the proposed software by presenting a step-by-step, concise implementation of an example plugin.

cs.SE↗

arXiv2023

Validation and benchmarking of quantum annealing technology

In this thesis, we focus on the problem of validating and benchmarking quantum annealers. To this end, we propose two algorithms for solving real-world problems and test how they perform on the current generation of quantum annealers. The first algorithm allows for solving the dynamics of quantum systems (or, in fact, any dynamical systems). The second of the proposed algorithms is suitable for solving a particular family of railway dispatching problems. We assess the performance of those algorithms on the current generation of D-Wave quantum annealers with the assistance of two novel, classical strategies for solving an Ising model also presented in the thesis. The first, tensor network-based approach is a heuristic algorithm tailored for solving instances defined on Chimera-like graphs, thus making it ideal for providing a baseline with which the results from physical annealers can be compared. The other presented approach is a massively parallel implementation of the exhaustive (brute-force) search through the whole solution space. Although the brute-force approach is limited to moderate instance sizes, it has the advantage of being able to compute the low energy spectrum and certify the solutions. Our results suggest that present-day quantum annealers are able to solve a subset of the aforementioned problems. In particular, we show that the D-Wave annealers are capable of capturing the dynamics of a simple quantum system in a specific regime of parameters, and can be used to obtain good-quality solutions for instances of railway conflict management problems. Finally, our findings indicate that the current generation of D-Wave annealers is far from perfect. We discuss problem instances for which the annealers failed to find a good or even feasible solution. We also provide, where possible, a plausible explanation of why some of the presented problems might be hard for the annealers.

quant-ph↗

arXiv2023

PyQBench: a Python library for benchmarking gate-based quantum computers

We introduce PyQBench, an innovative open-source framework for benchmarking gate-based quantum computers. PyQBench can benchmark NISQ devices by verifying their capability of discriminating between two von Neumann measurements. PyQBench offers a simplified, ready-to-use, command line interface (CLI) for running benchmarks using a predefined parametrized Fourier family of measurements. For more advanced scenarios, PyQBench offers a way of employing user-defined measurements instead of predefined ones.

quant-ph↗

arXiv2022

Extraction of ergotropy: free energy bound and application to open cycle engines

The second law of thermodynamics uses change in free energy of macroscopic systems to set a bound on performed work. Ergotropy plays a similar role in microscopic scenarios, and is defined as the maximum amount of energy that can be extracted from a system by a unitary operation. In this analysis, we quantify how much ergotropy can be induced on a system as a result of system's interaction with a thermal bath, with a perspective of using it as a source of work performed by microscopic machines. We provide the fundamental bound on the amount of ergotropy which can be extracted from environment in this way. The bound is expressed in terms of the non-equilibrium free energy difference and can be saturated in the limit of infinite dimension of the system's Hamiltonian. The ergotropy extraction process leading to this saturation is numerically analyzed for finite dimensional systems. Furthermore, we apply the idea of extraction of ergotropy from environment in a design of a new class of stroke heat engines, which we label open-cycle engines. Efficiency and work production of these machines can be completely optimized for systems of dimensions 2 and 3, and numerical analysis is provided for higher dimensions.

quant-ph↗

arXiv2021

Quantum annealing in the NISQ era: railway conflict management

We are in the Noisy Intermediate-Scale Quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrating the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the network segment being considered. This problem is computationally hard and needs to be solved almost in real-time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving Ising instances. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) perform much worse on those instances than its predecessor.

quant-ph↗

arXiv2021

Approximate optimization, sampling and spin-glass droplets discovery with tensor networks

We devise a deterministic algorithm to efficiently sample high-quality solutions of certain spin-glass systems that encode hard optimization problems. We employ tensor networks to represent the Gibbs distribution of all possible configurations. Using approximate tensor-network contractions, we are able to efficiently map the low-energy spectrum of some quasi-two-dimensional Hamiltonians. We exploit the local nature of the problems to compute spin-glass droplets geometries, which provides a new form of compression of the low-energy spectrum. It naturally extends to sampling, which otherwise, for exact contraction, is $\#$P-complete. In particular, for one of the hardest known problem-classes devised on chimera graphs known as deceptive cluster loops and for up to $2048$ spins, we find on the order of $10^{10}$ degenerate ground states in a single run of our algorithm, computing better solutions than have been reported on some hard instances. Our gradient-free approach could provide new insight into the structure of disordered spin-glass complexes, with ramifications both for machine learning and noisy intermediate-scale quantum devices.

cond-mat.stat-mech↗

arXiv2021

Quantum computing approach to railway dispatching and conflict management optimization on single-track railway lines

In this work, we consider a practical railway dispatching problem: delay and conflict management on a single-track railway line. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the segment being considered. This problem is computationally hard and often needs to be solved timely. Here, we introduce a quadratic unconstrained binary optimization (QUBO) model of the problem in question, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving QUBO problems.

cs.ET↗

arXiv2019

Brute-forcing spin-glass problems with CUDA

We demonstrate how to compute the low energy spectrum for small ($N\le 50$), but otherwise arbitrary, spin-glass instances using modern Graphics Processing Units or similar heterogeneous architecture. Our algorithm performs an exhaustive (i.e., brute-force) search of all possible configurations to select $S\ll 2^N$ lowest ones together with their corresponding energies. We mainly focus on the Ising model defined on an arbitrary graph. An open-source implementation based on CUDA Fortran and a suitable Python wrapper are provided. As opposed to heuristic approaches, ours is exact and thus can serve as a references point to benchmark other algorithms and hardware, including quantum and digital annealers. Our implementation offers unprecedented speed and efficiency already visible on commodity hardware. At the same time, it can be easily launched on professional, high-end graphics cards virtually at no extra effort. As a practical application, we employ it to demonstrate that the recent Matrix Product State based algorithm-despite its one-dimensional nature-can still accurately approximate the low energy spectrum of fully connected graphs of size $N$ approaching $50$.

physics.comp-ph↗

arXiv2019

Parallel in time dynamics with quantum annealers

Recent years have witnessed an unprecedented increase in experiments and hybrid simulations involving quantum computers. In particular, quantum annealers. Although quantum supremacy has not been established thus far, there exist a plethora of algorithms promising to outperform classical computers in the near-term future. Here, we propose a parallel in time approach to simulate dynamical systems designed to be executed already on present-day quantum annealers. In essence, purely classical methods for solving dynamics systems are serial. Therefore, their parallelization is substantially limited. In the presented approach, however, the time evolution is rephrased as a ground--state search of a classical Ising model. Such a problem is solved intrinsically in parallel by quantum computers. The main idea is exemplified by simulating the Rabi oscillations generated by a two-level quantum system (i.e. qubit) experimentally.

quant-ph↗