Anwendung und Optimierung eingeschränkter Hamiltonians

Quantum Computing Hardware ist noch immer begrenzt in der Anzahl der Qubits, deren Kohärenzeigenschaften und deren Konnektivität auf den Quantenprozessoren. Um daher bereits frühzeitig einen möglichen praktischen Quantenvorteil zum klassischen Computing zu erzielen, gilt es deshalb, sowohl relevante Anwendungsfälle mit großem Potenzial, als auch entsprechend umsetzbare Algorithmen für die aktuell verfügbare Noisy Intermediate-Scale Quantum (NISQ) Computer zu identifizieren. Viele relevante Problemstellungen der Industrie und Wissenschaft lassen sich auf Optimierungs- und Suchprobleme abbilden. Die Komplexität aus Anwendersicht ist die Transformation der Probleme in entsprechende Repräsentationen und Algorithmen für sowohl gatterbasierte als auch annealingbasierte Quantum Computing Hardware. Während Gatter-Architekturen das Problem in Form eines Quantenschaltkreis als Eingabe bekommen, werden zur Problembeschreibung für Quantum Annealing Hardware Ising-Hamiltonians oder Quadratic Unconstrained Binary Optimization (QUBO) Probleme verwendet. Viele Realwelt-Anwendungsfälle enthalten zudem Nebenbedingungen, die bei der Lösung berücksichtigt werden müssen. In Bezug auf die Ising-Hamiltonians werden diese Bedingungen in Penalty-Terme kodiert und mit Penalty-Parametern gewichtet, die bei einer Verletzung die entsprechende Lösung als invalide kennzeichnen. Diese Eigenheit beschreibt dann sogenannte eingeschränkte Ising-Hamiltonians. Die vorliegende Arbeit beschäftigt sich mit der Anwendung und Optimierung eingeschränkter Ising-Hamiltonians. Zu Beginn werden hybride Algorithmen, die unter anderem auf einem eingeschränkten Ising-Hamiltonian basieren, für die Domäne der Spieltheorie vorgestellt. Die Ansätze werden sowohl auf gatter- als auch annealingbasierter Hardware evaluiert und ihre Anwendbarkeit bestimmt. Anschließend wird eine adaptierte Kreuzentropie-Methode, zur Optimierung der Penalty-Parameter eingeschränkter Ising-Hamiltonians verschiedener akademischer kombinatorischer und graphbasierter Optimierungsprobleme, präsentiert. Die Evaluation der Hamiltonians mit den optimierten Penalty-Parametern zeigt eine signifikant gesteigerte Lösungsqualität, die mit einer größeren minimalen Spektrallücke in dem jeweiligen Eigenspektrum korreliert. Schließlich werden Methoden des maschinellen Lernens eingesetzt, um allgemeine Muster und Richtlinien zur Wahl der Penalty-Parameter für ausgewählte eingeschränkte Ising-Hamiltonians zu identifizieren und zu lernen. Zusammenfassend hebt diese Arbeit die Bedeutung der Optimierung eingeschränkter Ising-Hamiltonians hervor.Quantum computing hardware is still limited in the number of qubits, their coherence properties and their connectivity on quantum processors. Therefore, in order to achieve a possible practical quantum advantage over classical computing at an early stage, it is necessary to identify both relevant use cases with great potential and correspondingly implementable algorithms for currently available Noisy Intermediate-Scale Quantum (NISQ) computers. Many relevant problems from industry and science can be understood as optimization and search problems. The complexity from the user's perspective is the transformation of the problems into appropriate representations and algorithms for both gate-based and annealing-based quantum computing hardware. While quantum gate architectures receive the problem in the form of a quantum circuit as input, Ising-Hamiltonians or Quadratic Unconstrained Binary Optimization (QUBO) problems are used to describe the problem for quantum annealing hardware. Some real-world use cases also contain constraints that must be considered in the solution. With respect to Ising-Hamiltonians, these constraints are encoded in penalty-terms and weighted by penalty-parameters which, when violated, mark the corresponding solution as invalid. This characteristic then describes so-called constrained Ising-Hamiltonians. The present work deals with the application and optimization of constrained Ising-Hamiltonians. At the beginning, hybrid algorithms based on a constrained Ising-Hamiltonian are presented for the domain of game theory. The approaches are evaluated on both gate- and annealing-based quantum hardware and their applicability is determined. Subsequently an adapted cross-entropy method, for optimizing the penalty-parameters of constrained Ising-Hamiltonians of various academic combinatorial and graph-based optimization problems is presented. The evaluation of the Hamiltonians with the optimized penalty-parameters shows a significantly increased solution quality, which correlates with a larger minimum spectral gap in the respective eigenspectrum. Finally, machine learning methods are used to identify and learn general patterns and guidelines for choosing penalty-parameters for selected constrained Ising-Hamiltonians. In summary, this work highlights the importance of optimizing constrained Ising-Hamiltonians

Ambient effects on the electrical conductivity of carbon nanotubes

We show that the electrical conductivity of single walled carbon nanotubes (SWCNT) networks is affected by oxygen and air humidity under ambient conditions by more than a magnitude. Later, we intentionally modified the electrical conductivity by functionalization with iodine and investigated the changes in the band structure by optical absorption spectroscopy.Measuring in parallel the tubes electrical conductivity and optical absorption spectra, we found that conduction mechanism in SWCNT is comparable to that of intrinsically conducting polymers. We identified, in analogy to conducting polymers, in the infrared spectra a new absorption band which is responsible for the increased conductivity, leading to a closing gap in semiconducting SWCNT.We could show that by different functionalizations of the same SWCNT starting material the properties like conductivity can be dramatically changed, leading to different imaginable applications. We investigated here, an ultraviolet sensor with weakly modified SWCNT

SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training

Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.Comment: Published at ICAART 202

Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines

Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.Comment: 7 pages, 3 figures (5 if counting subfigures), 1 table. To be published in the proceedings of the 2023 IEEE International Conference on Quantum Computing and Engineering (QCE

Orexin Receptor Antagonism: A New Principle in Neuroscience

Orexins are hypothalamic neuropeptides interacting with G-protein coupled receptors in the brain. They play a role in the regulation of sleep–wake cycles in mammals, as suggested by the deficits in orexinergic function that are associated with rodent, canine and human narcolepsy. Selective or dual orexin1-receptor and/or orexin2-receptor antagonists or agonists that cross the blood-brain-barrier (BBB) may be of therapeutic interest for disorders of disturbed arousal and alertness. This article summarizes recent research to identify and characterize orexin receptor antagonists and their therapeutic potential for normalizing sleep in insomnia patients