Michael Lachner

Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or constraints that depend on summary statistic information about the simulation of a physical system or process. This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver proposed by Stein et al. for the unit commitment problem by an… Mehr anzeigen

Quantum Simulation-based Optimization (QuSO) is a recently proposed class of optimization problems that entails industrially relevant problems characterized by cost functions or constraints that depend on summary statistic information about the simulation of a physical system or process. This work extends initial theoretical results that proved an up-to-exponential speedup for the simulation component of the QAOA-based QuSO solver proposed by Stein et al. for the unit commitment problem by an empirical evaluation of the optimization component using a standard benchmark dataset, the IEEE 57-bus system. Exploiting clever classical pre-computation, we develop a very efficient classical quantum circuit simulation that bypasses costly ancillary qubit requirements by the original algorithm, allowing for large-scale experiments. Utilizing more than 1000 QAOA layers and up to 20 qubits, our experiments complete a proof of concept implementation for the proposed QuSO solver, showing that it can achieve both highly competitive performance and efficiency in its optimization component compared to a standard classical baseline, i.e., simulated annealing. Weniger anzeigen

Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the turbine balancing problem can be solved with quantum computing, which is the NP-hard optimization problem of analytically balancing rotor blades in a single plane as found in turbine assembly. Small yet relevant instances occur in industry, which makes the problem interesting for early quantum computing benchmarks. We model… Mehr anzeigen

Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the turbine balancing problem can be solved with quantum computing, which is the NP-hard optimization problem of analytically balancing rotor blades in a single plane as found in turbine assembly. Small yet relevant instances occur in industry, which makes the problem interesting for early quantum computing benchmarks. We model it as a quadratic unconstrained binary optimization problem and compare the performance of a classical rule-based heuristic and D-Wave Systems' quantum annealer Advantage_system4.1. In this case study, we use real-world as well as synthetic datasets and observe that the quantum hardware significantly improves an actively used heuristic's solution for small-scale problem instances with bare disk imbalance in terms of solution quality. Motivated by this performance gain, we subsequently design a quantum-inspired classical heuristic based on simulated annealing that achieves very good results on all given problem instances, essentially solving the optimization problem sufficiently well for all considered datasets, according to industrial requirements. Weniger anzeigen

Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in existing benchmarking frameworks is the lack of equal effort in optimizing for the best quantum and, respectively, classical approaches… Mehr anzeigen

Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in existing benchmarking frameworks is the lack of equal effort in optimizing for the best quantum and, respectively, classical approaches. This paper presents a comprehensive set of guidelines comprising universal steps towards fair benchmarks. We discuss (1) application-specific algorithm choice, ensuring every solver is provided with the most fitting mathematical formulation of a problem; (2) the selection of benchmark data, including hard instances and real-world samples; (3) the choice of a suitable holistic figure of merit, like time-to-solution or solution quality within time constraints; and (4) equitable hyperparameter training to eliminate bias towards a particular method. The proposed guidelines are tested across three benchmarking scenarios, utilizing the Max-Cut (MC) and Travelling Salesperson Problem (TSP). The benchmarks employ classical mathematical algorithms, such as Branch-and-Cut (BNC) solvers, classical heuristics, Quantum Annealing (QA), and the Quantum Approximate Optimization Algorithm (QAOA). Weniger anzeigen

Physical unclonable functions (PUFs), physical objects that are practically unclonable because of their andom and uncontrollable manufacturing variations, are becoming increasingly popular as security primitives and unique identifiers in a fully digitized world. One of the central PUF premises states that both friends and foes, both legitimate manufacturers and external attackers alike, cannot clone a PUF, producing two instances that are the same. Using the latest nanofabrication techniques… Mehr anzeigen

Physical unclonable functions (PUFs), physical objects that are practically unclonable because of their andom and uncontrollable manufacturing variations, are becoming increasingly popular as security primitives and unique identifiers in a fully digitized world. One of the central PUF premises states that both friends and foes, both legitimate manufacturers and external attackers alike, cannot clone a PUF, producing two instances that are the same. Using the latest nanofabrication techniques, we show that this premise is not always met: We demonstrate the possibility of effective PUF duplication through sophisticated manufacturers by producing 63 copies of a non-trivial optical scattering structure which exhibit essentially the same scattering behavior. The remaining minuscule differences are close to or below noise levels, whence the duplicates have to be considered fully equivalent from a PUF perspective. The possibility for manufacturer-based optical PUF duplication has positive and negative consequences at the same time: While fully breaking the security of certain schemes, it enables new applications, too. For example, it facilitates unforgeable labels for valuable items; the first key-free group identification schemes over digital networks; or new types of encryption/decryption devices that do not contain secret keys. Weniger anzeigen

Based on the quantum-assisted genetic algorithm (QAGA) [11] and related approaches we introduce several modifications of QAGA to search for more promising solvers on (at least) graph coloring problems, knapsack problems, Boolean satisfiability problems, and an equal combination of these three. We empirically test the efficiency of these algorithmic changes on a purely classical version of the algorithm (simulated-annealing-assisted genetic algorithm, SAGA) and verify the benefit of selected… Mehr anzeigen

Based on the quantum-assisted genetic algorithm (QAGA) [11] and related approaches we introduce several modifications of QAGA to search for more promising solvers on (at least) graph coloring problems, knapsack problems, Boolean satisfiability problems, and an equal combination of these three. We empirically test the efficiency of these algorithmic changes on a purely classical version of the algorithm (simulated-annealing-assisted genetic algorithm, SAGA) and verify the benefit of selected modifications when using quantum annealing hardware. Our results point towards an inherent benefit of a simpler and more flexible algorithm design. Weniger anzeigen