We study the problem of estimating frequency response functions of systems of coupled, classical harmonic oscillators using a quantum computer. The functional form of these response functions can be mapped to a corresponding eigenproblem of a Hermitian matrix H , thus suggesting the use of quantum phase estimation. Our proposed quantum algorithm operates in the standard s -sparse, oracle-based query access model. For a network of N oscillators with maximum norm ∥ H ∥ max , and when the eigenvalue tolerance ɛ is much smaller than the minimum eigenvalue gap, we use O ( log ( N s ∥ H ∥ max / ɛ ) algorithmic qubits and obtain a rigorous worst-case query complexity upper bound O ( s ∥ H ∥ max / ( δ 2 ɛ ) ) up to logarithmic factors, where δ denotes the desired precision on the coefficients appearing in the response functions. Crucially, our proposal does not suffer from the infamous state preparation bottleneck and can as such potentially achieve large quantum speedups compared to relevant classical methods. As a proof-of-principle of exponential quantum speedup, we show that a simple adaptation of our algorithm solves the random glued-trees problem in polynomial time. We discuss practical limitations as well as potential improvements for quantifying finite-size, end-to-end complexities for application to relevant instances. Published by the American Physical Society 2025

Over the past decades, global air traffic has increased continuously, with passenger kilometers roughly doubling every fifteen to twenty years, and this trend is estimated to continue, with some adjustments due to COVID-19 impact. In response to the resulting environmental challenges, the European initiatives Flightpath 2050 and Clean Sky serve as central drivers of technological development aimed at achieving ambitious sustainability goals. Flightpath 2050 targets, relative to a reference engine from the year 2000, include a 75% reduction in CO₂ emissions per passenger kilometer, a 90% reduction in NO_x emissions, and a 65% reduction in noise emissions. These objectives highlight the urgent need for emission reduction strategies across all manufacturing domains, including turbine component production. This study evaluates the environmental impacts of the preturning and roughing operations employed in turbine disk production. The analysis focuses on these specific processes rather than the entire product, as the approach of process-level Life Cycle Assessments (LCA) are more universally applicable across different products, and their systematic combination can ultimately form a comprehensive product-level LCA. Operational data, such as energy usage, cooling lubricants, and compressed air, were gathered and processed from the equipment involved in manufacturing. The collected data were analyzed and modeled in Spheras life cycle assessment software LCA for Experts (version 10.9.0.20) to quantify the environmental effects of each process. The findings of the current research emphasize notable patterns of resource utilization and their respective environmental impacts. Furthermore, the Industrial Digital Twin Association (IDTA) Product Carbon Footprint (PCF) template was utilized to present the findings in a standardized manner, enabling effective data transfer between stakeholders. The results demonstrate the critical need to leverage machine data for sustainability analysis, providing inputs for industry practice enhancement and progress toward better environmental performance.

In this paper, a methodical approach to evaluate the potential of quantum computing for manufacturing simulation, using the example of multi-axis milling of thin-walled aerospace components, is discussed. A developed approach for identifying bottlenecks in manufacturing simulations, for which the application of quantum computing potentially provides a speed-up or increase in accuracy, is presented. Moreover, indicators of quantum computing suitability and feasibility are defined with the main objective of identifying whether a manufacturing simulation bottleneck is suitable for quantum computing applications. First results of testing a hybrid routine as an application approach for the milling dynamics simulation on quantum machines are presented.