arXiv subjects

Fritz Schinkel

Publications and source records attributed to Fritz Schinkel.

4 recordsLinked to original sources

arXiv2026

Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers

Quadratic unconstrained binary optimization (QUBO) provides problem formulations for various computational problems that can be solved with dedicated QUBO solvers, which can be based on classical or quantum computation. A common approach to constrained combinatorial optimization problems is to enforce the constraints in the QUBO formulation by adding penalization terms. Penalization introduces an additional hyperparameter that significantly affects the solver's efficacy: the relative weight between the objective terms and the penalization terms. We develop a pre-computation strategy for determining penalization weights with provable guarantees for Gibbs solvers and polynomial complexity for broad problem classes. Experiments across diverse problems and solver architectures, including large-scale instances on Fujitsu's Digital Annealer, show robust performance and order-of-magnitude speedups over existing heuristics.

quant-ph↗

arXiv2025

state-o-gram --A Novel 2D Visualization for Quantum States

Quantum computing is rapidly gaining popularity, necessitating intuitive visualization tools for complex quantum states. While the Bloch Sphere effectively visualizes single-qubit states, it fundamentally lacks scalability for multi-qubit systems. Existing multi-qubit visualization attempts, such as VENUS, have shown promise but often face limitations in scalability beyond a few qubits. This paper introduces state-o-gram, a novel 2D visualization approach designed to intuitively represent quantum states for an arbitrary number of qubits. state-o-gram effectively visualizes probability amplitudes and phase angles in a unified 2D framework, addressing the limitations of prior art. We detail its design principles, visual elements, and application to multi-qubit systems, aiming to provide a scalable and intuitive tool for quantum state analysis. We evaluate the applicability by visualizing the states throughout the Deutsch-Josza algorithm.

quant-ph↗

arXiv2025

A comprehensive benchmark of an Ising machine on the Max-Cut problem

QUBO formulations of combinatorial optimization problems allow for solving them using various quantum heuristics. While large-scale quantum computations are currently still out of reach, we can already numerically test such QUBO formulations on a perhaps surprisingly large scale. In this work, we benchmark Fujitsu's Digital Annealer (DA) on the Max-Cut problem, which captures the main complexity of the QUBO problem. We make a comprehensive benchmark against leading other heuristic algorithms on graphs with up to 53,000 variables by focusing on the wall-clock time. Moreover, we compare the DA performance against published performance results of the D-Wave hybrid quantum-classical annealer and the recently proposed QIS3 heuristic. Based on performance statistics for over 2,000 graphs from the MQLib, we find that the DA yields competitive results. We hope that this benchmark demonstrates the extent to which large QUBO instances can be heuristically solved today, yielding consistent results across different solvers.

quant-ph↗

arXiv2023

Transformation-Dependent Performance-Enhancement of Digital Annealer for 3-SAT

Quadratic Unconstrained Binary Optimization (QUBO) problems are NP-hard problems and many real-world problems can be formulated as QUBO. Currently there are no algorithms known that can solve arbitrary instances of NP-hard problems efficiently. Therefore special-purpose hardware such as Digital Annealer, other Ising machines, as well as quantum annealers might lead to benefits in solving such problems. We study a particularly hard class of problems which can be formulated as QUBOs, namely Boolean satisfiability (SAT) problems, and specifically 3-SAT. One intriguing aspect about 3-SAT problems is that there are different transformations from 3-SAT to QUBO. We study the transformations' influence on the problem solution, using Digital Annealer as a special-purpose solver. Besides well-known transformations we investigate a novel in this context not yet discussed transformation, using less auxiliary variables and leading to very good performance. Using exact diagonalization, we explain the differences in performance originating from the different transformations. We envision that this knowledge allows for specifically engineering transformations that improve a solvers capacity to find high quality solutions. Furthermore, we show that the Digital Annealer outperforms a quantum annealer in solving hard 3-SAT instances.

quant-ph↗