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Simulationen von Gittereichtheorien spielen eine grundlegende Rolle bei First-Principles-Rechnungen im Kontext der Hochenergiephysik. Diese Arbeit zielt darauf ab, aktuelle Simulationsmethoden für First-Principle-Berechnungen zu verbessern und diese Methoden auf relevante physikalische Modelle anzuwenden. Wir gehen dieses Problem mit drei verschiedenen Ansätzen an: maschinelles Lernen, Quantencomputing und Tensornetzwerke. Im Rahmen des maschinellen Lernens haben wir eine Methode zur Schätzung thermodynamischer Observablen in Gitterfeldtheorien entwickelt. Genauer gesagt verwenden wir tiefe generative Modelle, um den absoluten Wert der freien Energie abzuschätzen. Wir haben die Anwendbarkeit unserer Methode durch die Untersuchung eines Spielzeugmodells demonstriert. Unser Ansatz erzeugt genauere Messungen im Vergleich mit dem Standard-Markov-Ketten-Monte-Carlo-Verfahren, wenn wir einen Phasenübergangspunkt überqueren. Im Kontext des Quantencomputings ist es unser Ziel, die aktuellen Algorithmen für Quantensimulationen zu verbessern. In dieser Arbeit haben wir uns mit zwei Themen moderner Quantencomputer befasst: der Quantenrauschunterdrückung und dem Design guter parametrischer Quantenschaltkreise. Wir haben eine Minderungsroutine zum Auslesen von Bit-Flip-Fehlern entwickelt, die Quantensimulationen drastisch verbessern kann. Wir haben auch eine dimensionale Aussagekraftanalyse entwickelt, die überflüssige Parameter in parametrischen Quantenschaltkreisen identifizieren kann. Darüber hinaus zeigen wir, wie man Expressivitätsanalysen mit Quantenhardware effizient umsetzen kann. Im Kontext des Tensornetzwerks haben wir ein Quantenbindungsmodell U(1) und 2+1-Dimensionen in einer Leitergeometrie mit DMRG untersucht. Unser Ziel ist es, die Eigenschaften des Grundzustands des Modells in einem endlichen chemischen Potential zu analysieren. Wir haben unterschiedliche Windungszahlsektoren beobachtet, als wir chemisches Potential in das System eingebracht haben. / Simulations of lattice gauge theories play a fundamental role in first principles calculations in the context of high energy physics. This thesis aims to improve state-of-the-art simulation methods for first-principle calculations and apply those methods to relevant physical models. We address this problem using three different approaches: machine learning, quantum computing, and tensor networks. In the context of machine learning, we have developed a method to estimate thermodynamic observables in lattice field theories. More precisely, we use deep generative models to estimate the absolute value of the free energy. We have demonstrated the applicability of our method by studying a toy model. Our approach produces more precise measurements in comparison with the standard Markov chain Monte Carlo method when we cross a phase transition point. In the context of quantum computing, our goal is to improve the current algorithms for quantum simulations. In this thesis, we have addressed two issues on modern quantum computers: the quantum noise mitigation and the design of good parametric quantum circuits. We have developed a mitigation routine ffor read-out bit-flip errors that can drastically improve quantum simulations. We have also developed a dimensional expressiveness analysis that can identify superfluous parameters in parametric quantum circuits. In addition, we show how to implement expressivity analysis using quantum hardware efficiently. In the context of the tensor network, we have studied a quantum bond model U(1) and 2+1 dimensions in a ladder geometry with DMRG. Our goal is to analyze the properties of the ground state of the model in a finite chemical potential. We have observed different winding number sectors when we have introduced chemical potential in the system.

Mapping of earth resources, environmental monitoring, and many other systems require high-resolution wide-area imaging. Since images often have to be captured at night or in inclement weather conditions, a capability is provided by Synthetic Aperture Radar (SAR). SAR systems exploit radar signal's long-range propagation and utilize digital electronics to process complex information, all of which enables high-resolution imagery. This gives SAR systems advantages over optical imaging systems, since, unlike optical imaging, SAR is effective at any time of day and in any weather conditions. Moreover, advanced technology called Interferometric Synthetic Aperture Radar (InSAR), has the potential to apply phase information from SAR images and to measure ground surface deformation. However, given the current state of technology, the quality of InSAR data can be distorted by several factors, such as image co-registration, interferogram generation, phase unwrapping, and geocoding. Image co-registration aligns two or more images so that the same pixel in each image corresponds to the same point of the target scene. Super-Resolution (SR), on the other hand, is the process of generating high-resolution (HR) images from a low-resolution (LR) one. SR influences the co-registration quality and therefore could potentially be used to enhance later stages of SAR image processing. Our research resulted in two major contributions towards the enhancement of SAR processing. The first one is a new learning-based SR model that can be applied with SAR, and similar applications. A second major contribution is utilizing the devised model for improving SAR co-registration and InSAR interferogram generation, together with methods for evaluating the quality of the resulting images. In the case of phase unwrapping, the process of recovering unambiguous phase values from a two-dimensional array of phase values known only modulo $2\pi$ rad, our research produced a third major contribution. This third major contribution is the finding that quantum annealers can resolve problems associated with phase unwrapping. Even though other potential solutions to this problem do currently exist - based on network programming for example - network programming techniques do not scale well to larger images. We were able to formulate the phase unwrapping problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using a quantum annealer. Since quantum annealers are limited in the number of qubits they can process, currently available quantum annealers do not have the capacity to process large SAR images. To resolve this limitation, we developed a novel method of recursively partitioning the image, then recursively unwrapping each partition, until the whole image becomes unwrapped. We tested our new approach with various software-based QUBO solvers and various images, both synthetic and real. We also experimented with a D-Wave Systems quantum annealer, the first and only commercial supplier of quantum annealers, and we developed an embedding method to map the problem to the D-Wave 2000Q_6, which improved the result images significantly. With our method, we were able to achieve high-quality solutions, comparable to state-of-the-art phase-unwrapping solvers. / Graduate

[pt] Interferometria tem sido utilizada para estudar uma variedade de efeitos físicos, desde os experimentos iniciais de Michelson e Morley que forneceram evidências para a teoria da relatividade restrita até os aparelhos de detecção de ondas gravitacionais utilizado no Laser Interferometer Gravitational-Wave Observatory (LIGO). O Propósito dessa dissertação é entender como explorar anyons e suas características únicas para construir interferômetros. Anyons são quasipartículas bi-dimensionais conhecidas por apresentarem estatística fracionária e possuírem aplicações em modelos de computação quântica. Para estudar sua utilidade no contexto de interferometria nós apresentamos uma perspectiva de computação quântica para experimentos de interferência. Em seguida, introduzimos modelos anyônicos e suas aplicações em computação quântica universal. Propomos um circuito quântico que implementa um certo tipo de interferômetro, e como realizá-lo em diferentes modelos anyônicos. Finalmente, discutimos um modelo de computação quântica baseado em ótica linear de anyons fermiônicos que permitiria a criação de uma versão lógica do nosso interferômetro em termos de um interferômetro físico. / [en] Interferometry has been used to study a variety of physical effects, from the early experiments of Michelson and Morley that provided evidence to special relativity to the more recent gravity-wave detection devices used by the Laser Interferometer Gravitational-Wave Observatory (LIGO) experiment. The purpose of this thesis is to understand how one can exploit anyons and its unique characteristics to build interferometers, and understand whether there are immediate advantages in doing so. Anyons are two-dimensional quasiparticles known for their unusual fractional statistics and applications in quantum computing models. To study their usefulness in the context of interferometry, we present a quantum computational approach to interference experiments. Next we give an introduction to anyon models and how they can be used to perform universal quantum computing. We propose a quantum circuit which implements a certain type of interferometer, and how it can be realized in different anyon models. Finally, we discuss a quantum computing model based on linear optics with fermionic anyons that would enable the creation of a logical version of our interferometer in terms of a physical interferometer.

Quantum computing is an emerging field with potential applications in machine learning. This research project aimed to compare the performance of a quantum kernel to that of a classical kernel in machine learning binary classification tasks. Two Support Vector Machines, a popular classification model, was implemented for the respective Variational Quantum kernel and the classical Radial Basis Function kernel and tested on the same sets of artificial quantum-based testing data. The results show that the quantum kernel significantly outperformed the classical kernel for the specific type of data and parameters used in the study. The findings suggest that quantum kernels have the potential to improve machine learning performance for certain types of problems, such as search engines and self-driving vehicles. Further research is, however, needed to confirm their utility in general situations. / Kvantberäkning är ett växande forskningsområde med möjliga tillämpningar inom maskininlärning. I detta forskningsprojekt jämfördes prestandan hos en klassisk kärna med den hos en kvantkärna i binär klassificering för maskininlärninguppgifter, och implikationerna av resultaten diskuterades. Genom att implementera två stödvektormaskiner, en populär klassifikationsmodell, för respektive variabel kvantkärna och klassisk radiell basfunktionskärna kunde vi direkt testa båda kärnorna på samma uppsättning av artificiella kvant-baserad testdata. Resultaten visar på betydande prestandafördelar för kvantkärnan jämfört med den klassiska kärnan när det gäller denna specifika typ av data och de parametrar som användes i vår studie. Vi drar slutsatsen att kvantkärnor inom maskininlärning har potential att överträffa klassiska kärnor, men att mer forskning krävs för att fastställa om detta har någon nytta i allmänna situationer. Om det finns betydande prestandafördelar kan det finnas många tillämpningar, till exempel för sökmotorer och självkörande fordon.

Quantum Computation is the quest for more efficient technologies. It can in principal be applied to Complex quantum systems, Quantum chemical systems, Cyber-security, Finance and AI. However, the introductory course in Quantum Mechanics at the Luleå University of Technology (F0047T) does not provide an introduction to Quantum Computation. This thesis investigates educational material and summarizes introductory concepts to Quantum Computation in the form of a compendium, as well as laboratory tasks in the form of simulation exercises as a potential integration of Quantum Computation into the course. The constructed compendium includes a historical overview, applications, introductory level Quantum Computation theory, Quantum Computational algorithms and a section of the Nobel prize in Physics 2022 which is relevant to both fields. An alternative proof of one of the algorithms, the Deutsch Jozsa Algorithm, presented in the compendium was created, which utilizes mathematics more in-line with the course. If the laboratory tasks were to be incorporated into the course, they would replace one of the current three laboratory tasks. Auxiliary aims for the laboratory tasks were thus imposed. These were: be of similar length/difficulty as the three laboratory tasks separately, be inspirational, be within the theoretical scope of the compendium and focus on quantum phenomenon. The laboratory tasks were chosen to center around Quantum Entanglement and the Deutsch Jozsa Algorithm which are to be preformed using IBM's Quantum Logic Circuit simulator 'Quantum Composer'. Both these tasks focus on Quantum phenomenon and are within the theoretical scope of the compendium. The length/difficulty and inspirational aspects of the tasks needs to be verified in a continuation study.

Quantum computers have the potential to solve certain problems in minutes that would otherwise take classical computers thousands of years due to the exponential speed-up certain quantum algorithms have over classical algorithms. In order to leverage such quantum algorithms, it is necessary for them to run on quantum devices. Examples of such devices include, but are not limited to, semiconductor and superconducting qubits, and semiconductor single and entangled photon emitters. The conventional method of constructing a semiconductor qubit is to apply gates on a semiconductor surface to localize electrons, where the electronic spin states are mapped to a qubit basis. Examples of this include the spin qubit where the spin-1/2 states of a single electron is the qubit basis and the gated singlet-triplet qubit where the states of two coupled electrons are mapped to a qubit basis. In general, gated semiconductor spin qubits are subject to decoherence from the environment which alters the electronic wavefunction by entanglement with the nuclear spins and phonons in the lattice compromising the stability of the qubit. Semiconductor nanostructures can also be designed as photon emitters. Self-assembled quantum dots are an example of such nanostructures and have been shown to emit single photons through exciton recombination and entangled photons through biexciton-exciton cascade. The difficulty in designing photon sources using self-assembled quantum dots is that the size and shape varies from dot to dot, implying that the electronic and magnetic properties also vary. In this thesis, I present the design of a single photon emitter using an InAsP quantum dot embedded in an InP nanowire and the design of a singlet-triplet qubit that is topologically protected from decoherence using an array of such quantum dots embedded in an InP nanowire. The advantage of using quantum dot nanowires over self-assembled quantum dots as photon emitters is that the quantum dot thickness, radius and composition can be controlled deterministically using a technique known as vapour-liquid-solid epitaxy which allows the emission spectrum to be engineered. Using a microscopic model, I simulated an InAsP quantum dot embedded in a nanowire with upwards of millions of atoms and showed that the emission spectrum came in agreement with the actual InAsP/InP quantum dot nanowires that were fabricated at the National Research Council of Canada. Moreover, I showed that altering the distribution of As atoms in the quantum dot can cause dramatic change in the emission spectrum. For the design of the topologically protected singlet-triplet qubit, I demonstrated that the ground state of an array of such quantum dots embedded in an InP nanowire, with four electrons in each dot, is four-fold degenerate and is topologically protected from higher energy states, making the ground state robust against perturbations. This state is known as the Haldane phase and can be understood in terms of two spin-1/2 quasiparticles at each edge of the array. Though the spectral gap in my simulation was of the order of 1 meV, this work provides insight into the potential design of a room temperature operating Haldane qubit where the spectral gap is of the order of room temperature.

Nuclei are objects made of nucleons, protons and neutrons. Several dynamical processes that occur in nuclei are of great interest for the scientific community and for possible applications. For example, nuclear fusion can help us produce a large amount of energy with a limited use of resources and environmental impact. Few-nucleon scattering is an essential ingredient to understand and describe the physics of the core of a star. The classical computational algorithms that aim to simulate microscopic quantum systems suffer from the exponential growth of the computational time when the number of particles is increased. Even using today's most powerful HPC devices, the simulation of many processes, such as the nuclear scattering and fusion, is out of reach due to the excessive amount of computational time needed. In the 1980s, Feynman suggested that quantum computers might be more efficient than classical devices in simulating many-particle quantum systems. Following Feynman's idea of quantum computing, a complete change in the computation devices and in the simulation protocols has been explored in the recent years, moving towards quantum computations. Recently, the perspective of a realistic implementation of efficient quantum calculations was proved both experimentally and theoretically. Nevertheless, we are not in an era of fully functional quantum devices yet, but rather in the so-called "Noisy Intermediate-Scale Quantum" (NISQ) era. As of today, quantum simulations still suffer from the limitations of imperfect gate implementations and the quantum noise of the machine that impair the performance of the device. In this NISQ era, studies of complex nuclear systems are out of reach. The evolution and improvement of quantum devices will hopefully help us solve hard quantum problems in the coming years. At present quantum machines can be used to produce demonstrations or, at best, preliminary studies of the dynamics of a few nucleons systems (or other equivalent simple quantum systems). These systems are to be considered mostly toy models for developing prospective quantum algorithms. However, in the future, these algorithms may become efficient enough to allow simulating complex quantum systems in a quantum device, proving more efficient than classical devices, and eventually helping us study hard quantum systems. This is the main goal of this work, developing quantum algorithms, potentially useful in studying the quantum many body problem, and attempting to implement such quantum algorithms in different, existing quantum devices. In particular, the simulations made use of the IBM QPU's , of the Advanced Quantum Testbed (AQT) at Lawrence Berkeley National Laboratory (LBNL), and of the quantum testbed recently based at Lawrence Livermore National Laboratory (LLNL) (or using a device-level simulator of this machine). The our research aims are to develop quantum algorithms for general quantum processors. Therefore, the same developed quantum algorithms are implemented in different quantum processors to test their efficiency. Moreover, some uses of quantum processors are also conditioned by their availability during the time span of my PhD. The most common way to implement some quantum algorithms is to combine a discrete set of so-called elementary gates. A quantum operation is then realized in term of a sequence of such gates. This approach suffers from the large number of gates (depth of a quantum circuit) generally needed to describe the dynamics of a complex system. An excessively large circuit depth is problematic, since the presence of quantum noise would effectively erase all the information during the simulation. It is still possible to use error-correction techniques, but they require a huge amount of extra quantum register (ancilla qubits). An alternative technique that can be used to address these problems is the so-called "optimal control technique". Specifically, rather than employing a set of pre-packaged quantum gates, it is possible to optimize the external physical drive (for example, a suitably modulated electromagnetic pulse) that encodes a multi-level complex quantum gate. In this thesis, we start from the work of Holland et al. "Optimal control for the quantum simulation of nuclear dynamics" Physical Review A 101.6 (2020): 062307, where a quantum simulation of real-time neutron-neutron dynamics is proposed, in which the propagation of the system is enacted by a single dense multi-level gate derived from the nuclear spin-interaction at leading order (LO) of chiral effective field theory (EFT) through an optimal control technique. Hence, we will generalize the two neutron spin simulations, re-including spatial degrees of freedom with a hybrid algorithm. The spin dynamics are implemented within the quantum processor and the spatial dynamics are computed applying classical algorithms. We called this method classical-quantum coprocessing. The quantum simulations using optimized optimal control methods and discrete get set approach will be presented. By applying the coprocessing scheme through the optimal control, we have a possible bottleneck due to the requested classical computational time to compute the microwave pulses. A solution to this problem will be presented. Furthermore, an investigation of an improved way to efficiently compile quantum circuits based on the Similarity Renormalization Group will be discussed. This method simplifies the compilation in terms of digital gates. The most important result contained in this thesis is the development of an algorithm for performing an imaginary time propagation on a quantum chip. It belongs to the class of methods for evaluating the ground state of a quantum system, based on operating a Wick rotation of the real time evolution operator. The resulting propagator is not unitary, implementing in some way a dissipation mechanism that naturally leads the system towards its lowest energy state. Evolution in imaginary time is a well-known technique for finding the ground state of quantum many-body systems. It is at the heart of several numerical methods, including Quantum Monte Carlo techniques, that have been used with great success in quantum chemistry, condensed matter and nuclear physics. The classical implementations of imaginary time propagation suffer (with few exceptions) of an exponential increase in the computational cost with the dimension of the system. This fact calls for a generalization of the algorithm to quantum computers. The proposed algorithm is implemented by expanding the Hilbert space of the system under investigation by means of ancillary qubits. The projection is obtained by applying a series of unitary transformations having the effect of dissipating the components of the initial state along excited states of the Hamiltonian into the ancillary space. A measurement of the ancillary qubit(s) will then remove such components, effectively implementing a "cooling" of the system. The theory and testing of this method, along with some proposals for improvements will be thoroughly discussed in the dedicated chapter.

Advanced enough quantum computers promise to revolutionise fields such as cryptography, drug discovery and simulations of complex systems. Quantum computers are built on qubits which are fragile and susceptible to error-inducing interference, which is called noise. The aim of this study was to examine the effects of varying levels of noise interference on the success rate and runtimes of a quantum computer circuit design built to implement Shor’s quantum factorisation algorithm. This was conducted using the Qiskit framework for quantum computer simulation and custom noise model creation. Our results show a correlation between the level of noise interference on a circuit and the probability of getting the correct measurement. We also found a greater impact of readout errors on the success rates, one-qubit depolarising errors on runtimes and that two-qubit depolarising errors greatly affected both, which was also discussed in the study. Our findings are in line with previous research and help to highlight the importance of minimising errors on critical quantum logic gates in an algorithm. / Tillräckligt avancerade kvantdatorer lovar att revolutionera områden så som kryptografi, utveckling av nya läkemedel och simulering av komplexa system. Kvantdatorer är uppbyggda av qubits vilka är ömtåliga och mottagliga mot felinducerande interferens, vilket kallas brus. Målet med denna studie var att utforska effekten av varierande brusnivåers interferens på lyckade försök samt körtiden av en kvantdatorkrets designad för att implementera Shors algoritm. Detta gjordes med Qiskits ramverk för kvantdatorsimulering och anpassningsbara brusmodeller. Våra resultat visar en korrelation mellan nivån av brusinterferens på en krets och sannolikheten av att få den korrekt mätningen. Vi fann även en större påverkan av avläsningsfel på kvoten lyckade försök, en-qubit depolariserande fel på körtid och att två-qubit depolariserande fel hade en stor påverkan på båda, vilket vi även diskuterat i studien. Våra resultat är i linje med tidigare studier och hjälper till att lyfta fram vikten av att minimera inducerade fel på kritiska logiska grindar i en kvantdatoralgoritm.

Quantum annealing (QA) is an optimization process in quantum computing similar to the probabilistic metaheuristic simulated annealing (SA). The QA process involves encoding an optimization problem into an energy landscape, which it then traverses in search for the point of minimal energy representing the global optimal state. In this thesis two different implementations of QA are examined, one run on a binary quadratic model (BQM) and one on a discrete quadratic model (DQM). These are then compared to their traditional counterpart: SA, in terms of performance and accuracy when solving the graph coloring problem (GCP). Regarding performance, the results illustrate how SA outperforms both QA implementations. However, it is apparent that these slower execution times are mostly due to various overhead costs that appear because of limited hardware. When only looking at the quantum annealing part of the process, it is about a hundred times faster than the SA process. When it comes to accuracy, both the DQM-implementation of QA and SA provided results of high quality, whereas the BQM-implementation performed notably worse, both by often not finding the optimal values and by sometimes returning invalid results. / Quantum annealing (QA) är en kvantbaserad optimeringsprocess som liknar den probabilistiska metaheuristiken simulated annealing (SA). QA går ut på att konvertera ett optimeringsproblem till ett energilandskap, som sedan navigeras för att hitta punkten med lägst energi, vilket då motsvarar den optimala lösningen på problemet. I denna uppsats undersöks två olika implementationer av QA: en som använder en binary quadratic model (BQM) och en som använder en discrete quadratic model (DQM). Dessa två implementationerna jämförs med deras traditionella motsvarighet: SA, utifrån både prestanda och korrekthet vid lösning av graffärgningsproblemet (GCP). När det gäller prestanda visar resultaten att SA är snabbare än båda QA implementationerna. Samtidigt är det tydligt att denna prestandaskillnad framförallt beror på diverse förberedelser innan exkueringen startar på kvantdatorn, vilka är krävande på grund av olika hårdvarubegränsningar. Om man endast betraktar kvantprocesserna visar vår studie att QA implementationerna är ungefär hundra gånger snabbare än SA. Gällande korrekthet gav både DQM-implementationen av QA och SA resultat av hög kvalitet medan BQM-implementationen presterade betydligt sämre. Den gjorde detta dels genom att inte skapa optimala resultat och genom att returnera otillåtna lösningar.