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Andre Waschk

Publications and source records attributed to Andre Waschk.

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QCI Connect: A Modular Full-Stack Quantum Computing Platform

In a world of various competing quantum computing architectures, hardware-agnostic, full-stack platforms are necessary to bring the full power of quantum computing hardware to domain experts via the cloud. QCI Connect and its Software Development Kit provide a reference architecture for a full-stack platform with a modular design and open-source interface definitions, built to facilitate a community-driven application ecosystem. Here, we present its overall design and features, central interfaces, and lessons learned, both for users of the platform and as a reference guide for future developments.

quant-ph

Automatic De-Quantization of Quantum Programs Using Constant Propagation

Quantum computing promises to solve problems beyond the reach of classical computers, but today's quantum hardware is error-prone and much slower than classical hardware. Every quantum operation is costly, making it crucial to minimize quantum resource usage in near-term algorithms. Quantum resources should only be used when they are truly essential for quantum advantage, and not wasted on operations that can be efficiently handled by classical computation. In this work, we focus on de-quantizing quantum operations to classical computation whenever possible. The approach we propose for this is hybrid quantum-classical constant propagation, an optimization which reduces quantum operations by trading them for fast, reliable classical instructions. This is done by tracking between quantum and classical states to identify and eliminate unnecessary quantum gates and controls. We formalize a hybrid state model for quantum-classical constant propagation, implement our optimizations in the open-source MQT Core tool, and evaluate them on benchmark circuits. The obtained results show that quantum-classical constant propagation can reduce costly multi-qubit operations, making quantum programs more practical and robust for near-term devices. This opens the door to new hybrid compiler strategies that leverage the best of both quantum and classical worlds.

quant-ph

Optimization of Hybrid Quantum-Classical Algorithms

Quantum computers do not run in isolation; rather, they are embedded in quantum-classical hybrid architectures. In these setups, a quantum processing unit communicates with a classical device in near-real time. To enable efficient hybrid computations, it is mandatory to optimize quantum-classical hybrid code. To the best of our knowledge, no previous work on the optimization of hybrid code nor on metrics for which to optimize such code exists. In this work, we take a step towards optimization of hybrid programs by introducing seven optimization routines and three metrics to evaluate the effectiveness of the optimization. We implement these routines for the hybrid quantum language Quil and show that our optimizations improve programs according to our metrics. This lays the foundation for new kinds of hybrid optimizers that enable real-time collaboration between quantum and classical devices.

cs.DC

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Visualizations rely on highlighting to attract and guide our attention. To make an object of interest stand out independently from a number of distractors, the underlying visual cue, e.g., color, has to be preattentive. In our prior work, we introduced Deadeye as an instantly recognizable highlighting technique that works by rendering the target object for one eye only. In contrast to prior approaches, Deadeye excels by not modifying any visual properties of the target. However, in the case of 2D visualizations, the method requires an additional setup to allow dichoptic presentation, which is a considerable drawback. As a follow-up to requests from the community, this paper explores Deadeye as a highlighting technique for 3D visualizations, because such stereoscopic scenarios support dichoptic presentation out of the box. Deadeye suppresses binocular disparities for the target object, so we cannot assume the applicability of our technique as a given fact. With this motivation, the paper presents quantitative evaluations of Deadeye in VR, including configurations with multiple heterogeneous distractors as an important robustness challenge. After confirming the preserved preattentiveness (all average accuracies above 90 %) under such real-world conditions, we explore VR volume rendering as an example application scenario for Deadeye. We depict a possible workflow for integrating our technique, conduct an exploratory survey to demonstrate benefits and limitations, and finally provide related design implications.

cs.HC