This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems. In addition, we include demonstrations of quantum algorithms on IBM Quantum back-ends and discuss the potential benefits of quantum algorithms for problems in financial services. We conclude with a summary of technical challenges and future prospects.

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Searching and presenting rich data using mobile devices is hard given their inherent I/O limitations. One approach for alleviating these limitations is device symbiosis, whereby the interaction with one's personal mobile device is augmented by additionally engaging with more capable infrastructure devices, such as kiosks and displays. The Celadon framework, previously developed by our team, builds upon device symbiosis for delivering zone-based services through mobile and infrastructure devices in public spaces such as shopping malls, train stations and theme parks.An approach for rich data visualization that is gaining wide popularity is mashups. In this paper we describe User-Defined Mashups -a general methodology that combines device symbiosis and automated creation of mashups. We have applied this methodology to build a system that enables Celadon users to flexibly interact with rich zone information through their mobile devices, leveraging large public displays. Our system bridges public and personal devices, data and services.

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