NASA NTRS ยท 20205010624
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Andrew Christian. Why is Predicting Audibility so Hard?. https://ntrs.nasa.gov/citations/20205010624
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State-of-the-art noisy-intermediate-scale quantum processors are currently implemented across a variety of hardware platforms, each with their own distinct gatesets. As such, circuit compilation should not only be aware of but also deeply connect to the native gateset and noise properties of each. Trapped-ion processors are one such platform that provides a gateset that can be continuously parameterized across both one- and two-qubit gates. Here we use the Quantum Scientific Computing Open User Testbed to study noise-aware compilations focused on continuously parameterized two-qubit ๐โข๐ gates (based on the Mรธlmer-Sรธrensen interaction) using $\scriptsize{SUPERSTAQ}$, a quantum software platform for hardware-aware circuit compiler optimizations. We discuss the realization of ๐โข๐ gates with arbitrary angle on the all-to-all connected trapped-ion system. Then we discuss a variety of different compiler optimizations that innately target these ๐โข๐ gates and their noise properties. These optimizations include moving from a restricted maximally entangling gateset to a continuously parameterized one, swap mirroring to further reduce the total entangling angle of the operations, focusing the heaviest ๐โข๐ angle participation on the best-performing gate pairs, and circuit approximation to remove the least impactful ๐โข๐ gates. We demonstrate these compilation approaches on the hardware with randomized quantum volume circuits, observing the potential to realize a larger quantum volume as a result of these optimizations. Using differing yet complementary analysis techniques, we observe the distinct improvements in system performance provided by these noise-aware compilations and study the role of stochastic and coherent error channels for each compilation choice.
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Proposed Urban Air Mobility (UAM) operations offer an alternative to road and rail traffic for local and regional movement of people and goods. To allow for large-scale adoption of UAM vertical takeoff and landing (VTOL) aircraft, it is critical to predict human annoyance response to the acoustic noise generated by these vehicles. We propose a model that predicts an individualโs perceived level of annoyance when presented with UAM VTOL aircraft noise in the context of a representative masking noise. The annoyance model is based on the psychoacoustic annoyance model of Fastl and Zwicker (Zwicker and Fastl, 1999), with an additional tonality term based on subjective testing of UAM sound quality (Boucher, et al., 2023). The model also predicts changes in annoyance when UAM noise is masked by a background sound, based on subjective evaluation of detection, noticeability, and annoyance of noise in the presence of a masker.