Near-field microwave microscopy and multivariate analysis of XRD data

The combinatorial approach to materials research is based on the synthesis of hundreds or thousands of related materials in a single experiment. The popularity of this approach has created a demand for new tools to rapidly characterize these materials libraries and new techniques to analyze the resulting data. The research presented here is intended to make a contribution towards meeting this demand, and thereby advance the pace of materials research. The first part of the dissertation discusses the development of a materials characterization tool called a near field microwave microscope (NFMM). We focus on one particular NFMM topology, the open ended coaxial resonator. The traditional application of this NFMM topology is the characterization of the dielectric properties of materials at GHz frequencies. With the goal of expanding the capabilities of the NFMM beyond this role, we explore two non-traditional modes of operation. The first mode is scanning ferromagnetic resonance spectroscopy. Using this technique, we map the magnetostatic spin wave modes of a single crystal gallium doped yttrium iron garnet disk. The second mode of operation entails combining near field microscopy with scanning tunneling microscopy (STM). Operating in this mode, we show that the NFMM is capable of obtaining atomic resolution images by coupling microwaves through an atomic scale tunnel junction. The second part of the dissertation discusses the analysis of X-Ray Diffraction (XRD) data from combinatorial libraries. We focus on two techniques that are designed to simultaneously analyze all of the XRD spectra from a given experiment, providing a faster method than the traditional one-at-a-time approach. First, we discuss agglomerative hierarchical cluster analysis, which is used to identify regions of composition space that have similar crystal structures. Second, we discuss non-negative matrix factorization (NMF). NFM is used to decompose many experimental diffraction patterns into a smaller number of constituent patterns; ideally, these constituent patterns represent the unique crystal structures present in the samples. Compared to hierarchical clustering, NMF has the advantage of identifying multi-phase regions within the composition space. These techniques are also applicable to other types of spectral data, such as FTIR, Raman spectroscopy, XPS, and mass spectrometry

Big data clustering: Data preprocessing, variable selection, and dimension reduction

[no abstract available

Machine Learning Applications for Thermal Manufacturing Processes

This thesis introduces a novel approach for the extraction of physically meaningful thermal component time series during the manufacturing of casting parts. I treat their extraction as Blind Source Separation (BSS) problem by exploiting process-related prior knowledge. The proposed method arranges temperature time series into a data matrix, which is then decomposed by Non-negative Matrix Factorization (NMF). The latter is guided by a knowledge-based strategy, which initializes the NMF component matrix with time curves designed according to basic physical processes. It is shown how to extract components linked to physical phenomena that typically occur during production and cannot be monitored directly. The proposed methods are applied to real world data, collected in a foundry during the series production of casting parts for the automobile industry

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Nanoinformatics

Machine learning; Big data; Atomic resolution characterization; First-principles calculations; Nanomaterials synthesi

Big data clustering: Data preprocessing, variable selection, and dimension reduction

[no abstract available

Implementation of variational quantum algorithms on superconducting qudits

Quantum computing is considered an emerging technology with promising applications in chemistry, materials, medicine, and cryptography. Superconducting circuits are a leading candidate hardware platform for the realisation of quantum computing, and superconducting devices have now been demonstrated at a scale of hundreds of qubits. Further scale-up faces challenges in wiring, frequency crowding, and the high cost of control electronics. Complementary to increasing the number of qubits, using qutrits (3-level systems) or qudits (d-level systems, d>3) as the basic building block for quantum processors can also increase their computational capability. A commonly used superconducting qubit design, the transmon, has more than two levels. It is a good candidate for a qutrit or qudit processor. Variational quantum algorithms are a type of quantum algorithm that can be implemented on near-term devices. They have been proposed to have a higher tolerance to noise in near-term devices, making them promising for near-term applications of quantum computing. The difference between qubits and qudits makes it non-trivial to translate a variational algorithm designed for qubits onto a qudit quantum processor. The algorithm needs to be either rewritten into a qudit version or an emulator needs to be developed to emulate a qubit processor with a qudit processor. This thesis describes research on the implementation of variational quantum algorithms, with a particular focus on utilising more than two computational levels of transmons. The work comprises building a two-qubit transmon device and a multi-level transmon device that is used as a qutrit or a qudit (d = 4). We fully benchmarked the two-qubit and the single qudit devices with randomised benchmarking and gate-set tomography, and found good agreement between the two approaches. The qutrit Hadamard gate is reported to have an infidelity of 3.22 ± 0.11 × 10−3, which is comparable to state-of-the-art results. We use the qudit to implement a two-qubit emulator and report that the two-qubit Clifford gate randomised benchmarking result on the emulator (infidelity 9.5 ± 0.7 × 10−2) is worse than the physical two-qubit (infidelity 4.0 ± 0.3 × 10−2) result. We also implemented active reset for the qudit transmon to demonstrate preparing high-fidelity initial states with active feedback. We found the initial state fidelity improved from 0.900 ± 0.011 to 0.9932 ± 0.0013 from gate set tomography. We finally utilised the single qudit device to implement quantum algorithms. First, a single qutrit classifier for the iris dataset was implemented. We report a successful demonstration of the iris classifier, which yields the training accuracy of the qutrit classifier as 0.96 ± 0.03 and the testing accuracy as 0.94 ± 0.04 among multiple trials. Second, we implemented a two-qubit emulator with a 4-level qudit and used the emulator to demonstrate a variational quantum eigensolver for hydrogen molecules. The solved energy versus the hydrogen bond distance is within 1.5 × 10−2 Hartree, below the chemical accuracy threshold. From the characterisation, benchmarking results, and successful demonstration of two quantum algorithms, we conclude that higher levels of a transmon can be used to increase the size of the Hilbert space used for quantum computation with minimal extra cost