Analyzing Hyperspectral EO-Images with Quantum Computers

Mit den Fortschritten des maschinellen Lernens hat die Analyse von Erdbeobachtungsdaten in den letzten Jahren an Popularität gewonnen. Vor allem bei der besonders schwierigen Aufgabe der Klassifzierung von Hyperspektralbildern wurden deutliche Verbesserungen erzielt. Ein Hyperspektralbild ist ein dreidimensionaler Datenwürfel, bei dem zwei Dimensionen räumliche Informationen und eine Dimension spektrale Informationen in Form von elektromagnetischen Wellenlängen enthalten. Seine hohe Informationsdichte ermöglicht im Vergleich zu RGB-Bildern eine viel detailliertere Analyse der erfassten Daten. Durch Klassifzierung ordnen wir die Pixel von Hyperspektralbildern einer Reihe von Klassen zu und können so Materialien oder Objekte von Interesse identifzieren. In dieser Arbeit untersuchen wir die Anwendung einer neuartigen Form des maschinellen Lernens, des Quanten-Maschinenlernens, auf den Hyperspektralbilddatensatz der Universität Pavia. Quantum Machine Learning nutzt Quantenalgorithmen, um entweder bestehende Subroutinen des maschinellen Lernens zu beschleunigen oder um Methoden zu entwickeln, die eine höhere Kapazität und Ausdrucksfähigkeit haben als klassische maschinelle Lernmethoden. Wir erforschen verschiedene Formen klassischer Bilddarstellungen auf einem Quantencomputer und entwickeln drei Quantenalgorithmen für maschinelles Lernen, um eine pixelweise Klassifzierung hyperspektraler Bilder durchzuführen. Zwei der drei Methoden verwenden Quantenkernel, um eine Quantenversion der Support-Vektor-Maschine zu implementieren, und die dritte ist ein neuronales Quantennetzwerk. Wir implementieren diese Methoden mit Hilfe des PennyLane-Frameworks und zeigen, dass sie mit ähnlicher Genauigkeit klassifzieren wie eine klassische Support-Vektor-Maschine als Benchmark-Methode. Da wir Quantensimulatoren verwenden, haben die Ergebnisse keine unmittelbaren Auswirkungen auf die Leistung der Modelle, sondern zeigen vielmehr, dass sie funktionieren

Quantum Shift Scheduling - A Comparison to Classical Approaches

Solving discrete optimization problems with constraints is a very common task in industry and research as it is fundamental in solving many planning tasks. In this paper we will look at an instance of a time table problem for generating shift schedules at the German Space Operation Center (GSOC). We describe the implementation of a quantum approach and compare the differences to classical optimization strategies, knowing that the problem sizes given to the quantum systems are not competitive yet. By doing so we are establishing a software chain that is able to map our problem to different physical systems which paves the way to problem solving as a hybrid solution where sub-problems are distributed among classical and quantum hardware. In this study we included three approaches to tackle the described problem. For the quantum part, we included a programmatically generated quantum circuit that yields a solution to a (sub) problem using Grovers algorithm, able to be run on any general quantum computer with sufficiently many qubits of sufficiently high quality. On the classical side, as a validation and benchmark reference, we use a heuristic search method, implemented by GSOCs own planning tool set Plato and PINTA (Lenzen et al. 2012; Nibler et al. 2021) as well as a constraint integer programming formulation solved by an external software framework, such as e. g. GLPK or SCIP (Gamrath et al. 2020). This paper builds on and extends results from (Scherer et al. 2021)

OnCall Operator Scheduling for Satellites with Grover's Algorithm

The application of quantum algorithms on some problems in NP promises a significant reduction of time complexity. This work uses Grover's Algorithm, designed to search an unstructured database with quadratic speedup, to find valid a solution for an instance of the on-call operator scheduling problem at the German Space Operation Center. We explore new approaches in encoding the problem and construct the Grover oracle automatically from the given constraints and independent of the problem size. Our solution is not designed for currently available quantum chips but aims to scale with their growth in the next years

Effect of a combined brief narrative exposure therapy with case management versus treatment as usual in primary care for patients with traumatic stress sequelae following intensive care medicine: study protocol for a multicenter randomized controlled trial (PICTURE)

BACKGROUND Traumatic events like critical illness and intensive care are threats to life and bodily integrity and pose a risk factor for posttraumatic stress disorder (PTSD). PTSD affects the quality of life and morbidity and may increase health-care costs. Limited access to specialist care results in PTSD patients being treated in primary care settings. Narrative exposure therapy (NET) is based on the principles of cognitive behavioral therapy and has shown positive effects when delivered by health-care professionals other than psychologists. The primary aims of the PICTURE trial (from \textquotedblPTSD after ICU survival\textquotedbl) are to investigate the effectiveness and applicability of NET adapted for primary care with case management in adults diagnosed with PTSD after intensive care. METHODS/DESIGN This is an investigator-initiated, multi-center, primary care-based, randomized controlled two-arm parallel group, observer-blinded superiority trial conducted throughout Germany. In total, 340 adult patients with a total score of at least 20 points on the posttraumatic diagnostic scale (PDS-5) 3 months after receiving intensive care treatment will be equally randomized to two groups: NET combined with case management and improved treatment as usual (iTAU). All primary care physicians (PCPs) involved will be instructed in the diagnosis and treatment of PTSD according to current German guidelines. PCPs in the iTAU group will deliver usual care during three consultations. In the experimental group, PCPs will additionally be trained to deliver an adapted version of NET (three sessions) supported by phone-based case management by a medical assistant. At 6 and 12 months after randomization, structured blinded telephone interviews will assess patient-reported outcomes. The primary composite endpoint is the absolute change from baseline at month 6 in PTSD symptom severity measured by the PDS-5 total score, which also incorporates the death of any study patients. Secondary outcomes cover the domains depression, anxiety, disability, health-related quality-of-life, and cost-effectiveness. The principal analysis is by intention to treat. DISCUSSION If the superiority of the experimental intervention over usual care can be demonstrated, the combination of brief NET and case management could be a treatment option to relieve PTSD-related symptoms and to improve primary care after intensive care. TRIAL REGISTRATION ClinicalTrials.gov, NCT03315390 . Registered on 10 October 2017. German Clinical Trials Register, DRKS00012589 . Registered on 17 October 2017