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Majorana fermions promise potential applications in quantum computing, superconductivity, and related fields. In this thesis, an analysis of A. Y. Kitaev's “Kitaev Chain”, a quasi-one-dimensional quantum wire in contact with a p-wave superconductor, designed as a model exhibiting unpaired Majoranas, is performed. Described by tunneling of spinless fermions between quantum dots, and formation of Cooper pairs on neighboring dots, Kitaev's chain Hamiltonian serves as a basis for emergent Majorana Zero Modes (zero energy Majorana fermions localized at either end of the chain) and artificial gauges (phases) to appear. By exact diagonalization, energy spectra and wavefunctions of a chain of spinless fermions on discrete quantum dots described by Kitaev's Hamiltonian are generated. By transforming the system into a basis of Majorana fermions and "bond fermions", where Majoranas on neighboring dots are paired, emergent Majorana Zero Modes (MZMs) are found at the ends of the chain. These emergent MZMs are paired in a non-local, zero energy bond fermion, which is found to allow degenerate energy states of the system to occur. Joining the ends of the chain by allowing tunneling and pairing of fermions on end sites, a ring topology is considered, where an "artificial gauge" emerges. This artificial gauge, or phase, causes a phase change on tunneling and Cooper pairing Hamiltonian matrix elements as a result of operator ordering within the Hamiltonian's ring terms. These required operator orderings are derived by comparison of energy spectra of the Kitaev ring in the fermion and bond fermion bases. Matching of calculated energy spectra in the Majorana and fermionic bases is used to confirm the presence of the artificial gauge, where this phase is found to be necessary in order to maintain a consistent energy spectra across the transformation between bases. This analysis is performed in order to understand the concept of Majorana Zero Modes and the emergence of Majorana fermions in 1D chains. By doing so, it is determined what Majorana fermions are, where they come from, and why Majorana Zero Modes are considered to be zero energy. These results contribute to the understanding of Kitaev chains and rings, as well as serve as a starting point for discussions regarding physical implications of the artificial gauge's effect, fermion statistics, and the emergence of Majorana Zero Modes in quasi-one-dimensional systems.

Quantum computers have seen rapid development over the last two decades. Despite this, they are not yet scalable or fault-tolerant (i.e. we cannot address arbitrarily many error-corrected qubits). Therefore, improvements that include consideration of the underlying physics are paramount. To do this, we must reduce existing errors and understand how algorithms perform without error correction. In this dissertation, we provide contributions toward these goals. We organize these efforts into three groups. Firstly, we focus on quantum control. We introduce a novel scheme for performing entangling gates on superconducting qubits. We create fast, high-fidelity entangling operations and single-qubit gates to implement arbitrary quantum operations. Then, we implement entangling gates on real transmon qubits. Finally, we develop new techniques for entangling gates on spin qubits. In total, we improve low-level device performance with high-fidelity entangling operations. Secondly, we focus on quantum simulation algorithms. First, we apply error mitigation techniques to a quantum simulation algorithm while simultaneously performing device characterization. Then we take advantage of known symmetries of the input Hamiltonian to improve the same algorithm. Then, we demonstrate that this reduces resources compared to other approaches in the presence of noise. Then we compare this technique with state-of-the-art approaches. Then, we improve this algorithm with approaches from quantum control. Finally, we develop a novel algorithm to simulate spin chains on a quantum processor with improved resources compared to other techniques. In total, we improve quantum simulation algorithms, with the aim of better utilizing current devices. Thirdly, we consider the ADAPT-VQE algorithm, which is used to construct quantum circuits for preparing trial states in quantum simulation. In total, we improve gate counts for the algorithm, improve a separate algorithm by utilizing the gradient criterion, and leverage the repeating structure of an input Hamiltonian to improve performance. Finally, we provide a deeper understanding of ADAPT-VQE and demonstrate its robustness to scaling issues of competing algorithms. In total, we improve the algorithm and its applicability. Thus, we improve quantum simulation algorithms that can be run in the near term. / Doctor of Philosophy / The computers that we interact with every day rely on the processing of bits, represented as 1's or 0's. The rules that govern how they operate mostly rely on classical physics (i.e. discovered before quantum physics), which does not include any quantum effects. If we instead allow for quantum rules and quantum bits ("qubits"'), new types of algorithms are possible. That is to say, quantum computers can do some things more efficiently than classical computers. As such, there is a massive effort to build these devices. Because these devices are so delicate and in their early stages, this requires an understanding of the algorithm and the physical device performing it. Therefore, improving the overall performance requires taking high and low-level aspects of this design into consideration. In this dissertation, we provide three groups of contributions to achieving this goal. In the first group, we improve the device performance by considering how operations are performed on qubits, primarily in terms of producing quantum operations that have no classical analog. In the second group, we improve the simulation of quantum systems on quantum devices with a focus on how existing imperfections in the device impact the results. In the third group, we make improvements to an algorithm used to simulate quantum systems like molecules, while also developing a deeper understanding of how the algorithm functions. In each of these parts, we develop novel techniques to improve device and algorithm performance, contributing to the applicability and utility of current and future quantum devices.

The variational quantum eigensolver (VQE) approach is currently one of the most promising strategies for simulating chemical systems on quantum hardware. In this work, I will describe a new quantum algorithm and a new set of classical algorithms based on VQE. The quantum algorithm, ADAPT-VQE, shows promise in mitigating many of the known limitations of VQEs: Ansatz ambiguity, local minima, and barren plateaus are all addressed to varying degrees by ADAPT-VQE. The classical algorithm family, O2DX-UCCSD, draws inspiration from VQEs, but is classically solvable in polynomial time. This group of algorithms yields equations similar to those of the linearized coupled cluster theory (LCCSD) but is more systematically improvable and, for X = 3 or X = ∞, can break single bonds, which LCCSD cannot do. The overall aim of this work is to showcase the richness of the VQE algorithm and the breadth of its derivative applications. / Doctor of Philosophy / A core goal of quantum chemistry is to compute accurate ground-state energies for molecules. Quantum computers promise to simulate quantum systems in ways that classical computers cannot. It is believed that quantum computers may be able to characterize molecules that are too large for classical computers to treat accurately. One approach to this is the variational quantum eigensolver, or VQE. The idea of a VQE is to use a quantum computer to measure the molecular energy associated with a quantum state which is parametrized by some classical set of parameters. A classical computer will use a classical optimization scheme to update those parameters before the quantum computer measures the energy again. This loop is expected to minimize the quantum resources needed for a quantum computer to be useful, since much of the work is outsourced to classical computers. In this work, I describe two novel algorithms based on the VQE which solve some of its problems.

This PhD work took place in the framework of theoretical research aimed at implementation of quantum computing schemes and algorithms in solid state devices. The electron and nuclear spins of dopant atoms implanted in silicon crystals, that already lie at the core of commercial diodes and the photovoltaic industry, are able to store quantum information longer than anything else in the solid state. Controlled manipulations of silicon qubits depend on the ability to tune the nanoscopic donor electron state: we provide a complete theoretical picture that includes, within the insightful and analytic framework of effective mass theory, the effects of the non-trivial silicon conduction band and the different lattice distortions caused by the implantation of the donor species. Calibration of the multi-valley bulk theory to account for binding energies and electron-nuclear hyperfine couplings allows improved estimates of the exchange splittings between two neighbouring donors, that provide the simplest handle for tuning two-qubit operations. Further refinements to our approach lead to exceptional agreement with experimental measurements of Stark effects, where an external electric field is used to enable local single qubit manipulations within global driving fields: we set reliable thresholds on such gating speeds across all group V donors. Finally, we propose a scalable scheme for silicon quantum computing that relies on the coherent transfer of information from Si:Bi donors, that are established as excellent memory qubits, to surface quantum dots that are easier to manipulate, within a topological surface code which enables outstanding tolerance to errors. Analysis of the optimal working regimes and inclusion of the leading sources of decoherence allow us to set out a robust design of the basic building block of future realizations.

Superconducting Quantum Interference Devices (SQUIDs) are currently being used as flux qubits and read-out detectors in a variety of solid-state quantum computer architectures. The main limitation of SQUID qubits is that they have a coherence time of the order of 10 us, due to the presence of intrinsic flux noise that is not yet fully understood. The origin of flux noise is currently believed to be related to spin impurities present in the materials and interfaces that form the device. Here we present a novel numerical method that enables calculations of the flux produced by spin impurities even when they are located quite close to the SQUID wire. We show that the SQUID will be particularly sensitive to spins located at its wire edges, generating flux shifts of up to 4 nano flux quanta, much higher than previous calculations based on the software package FastHenry. This shows that spin impurities in a particular region along the wire's surface play a much more important role in producing flux noise than other spin impurities located elsewhere in the device. / Graduate / 0611 / 0607 / 0753 / laforest@uvic.ca

One of the current hot topics in physics is quantum information, which, broadly speaking, is concerned with exploring the information-processing and storing tasks that can be performed in quantum mechanical systems. Besides driving forward our experimental control and understanding of quantum systems, the field is also in the early stages of developing revolutionary new technology of far reaching implication. As part of these endeavors, this thesis presents some results in experimental quantum information. Specifically, we develop several new tools for performing quantum information processing in optical quantum systems, and use them to explore a number of applications and novel physical phenomena. A central theme, and one of the most sought after applications of quantum information, is the pursuit of a programmable quantum computer. This thesis is divided into 3 parts. In Part I we develop some new optical quantum logic gates, which are tools for manipulating quantum information and the fundamental building blocks of a quantum computer. We also develop a new technique for simplifying the construction of quantum logic circuits, by exploiting multi-level quantum systems, that has the potential for application in any physical encoding of quantum information. In Part II we use these tools to perform some of the first demonstrations of quantum algorithms. Each of these could, in principle, efficiently solve an important problem that is thought to be fundamentally intractable using conventional `classical' techniques. Firstly we implement a simplified version of the quantum algorithm for factoring numbers, and demonstrate the core processes, coherent control, and resultant entangled states required for a full-scale implementation. Secondly we implement an algorithm for calculating the energy of many-body quantum systems. Specifically, we calculate the energy spectrum of the Hydrogen molecule, in a minimal basis. Finally we demonstrate an algorithm for a novel model of quantum computing that uses mixed states. Here we perform the first characterisation of intrinsically non-classical correlations between fully separable quantum systems, captured by the 'discord'---a measure of quantum correlations in mixed states that goes beyond entanglement. Part III presents a technique that extends experimental control over biphotons---the novel quantum information carriers formed by the polarisation of two photons in the same spatial and temporal mode. We also generate and explore new forms of entanglement: producing the first instance of qubit-qutrit entanglement, by entangling the polarisation of a photon and a biphoton, and developing a technique that enables full control over the level of `W-class' of multi-partite entanglement between the polarisation of three photons.

Many important tasks in finance often rely on complex and time-consuming computations. The rapid development of quantum technology has raised the question of whether quantum computing can be used to solve these tasks more efficiently than classical computing. This thesis studies the potential use of quantum computing in finance by solving differently-sized problem instances of the mean-variance portfolio selection model using commercially available quantum resources. The experiments employ gate-based quantum computers and quantum annealing, the two main technologies for realizing a quantum computer. To solve the mean-variance optimization problem on gate-based quantum computers, the model was formulated as a quadratic unconstrained binary optimization (QUBO) problem, which was then used as input to quantum resources available on the largest quantum computing as a service (QCaaS) platforms, IBM Quantum Lab, Microsoft Azure Quantum and Amazon Braket. To solve the problem using quantum annealing, a hybrid quantum-classical solver available on the service D-Wave Leap was employed, which takes as input the mean-variance model’s constrained quadratic form. The problem instances were also solved classically on the model’s QUBO form, where the results acted as benchmarks for the performances of the quantum resources. The results were evaluated based on three performance metrics: time-to-solve, solution quality, and cost-to-solve. The findings indicate that gate-based quantum computers are not yet mature enough to consistently find optimal solutions, with the computation times being long and costly as well. Moreover, the use of gate-based quantum computers was not trouble-free, with the majority of quantum computers failing to even complete the jobs. Quantum annealing, on the other hand, demonstrated greater maturity, with the hybrid solver being capable of fast and accurate optimization, even for very large problem instances. The results from using the hybrid solver justify further research into quantum annealing, to better understand the capabilities and limitations of the technology. The results also indicate that quantum annealing has reached a level of maturity where it has the potential to make a significant impact on financial institutions, creating value that cannot be obtained by using classical computing.

Quantum computing has generated a lot of interested in the past two decades. Research into powerful models of quantum computation has yielded important and elegant results like an efficient algorithm for factoring and a quadratic speed-up for unordered search. At the same time, given the current difficulty in the physical implementation of these general purpose models, considerable effort has also been made in estimating the power of weaker models of quantum computation: models that have a small quantum component. The first part of this thesis is an investigation into the power of interference in quantum computing. While interference in probability amplitudes is a central feature even in powerful models, it is the only extra resource available to quantum finite automata. Of particular interest is interference in automata that have both classical and quantum states (2QCFA) as proposed by Ambainis and Watrous, since it inquires into the power of a classical deterministic finite automaton when augmented with a quantum component of constant size. Our contributions in this part are as follows: • To abstract out the phenomenon of interference in quantum computing, we propose a model called the 2-way Optical Interference Automata (2OIA). The model consists of a 2DFA augmented with a simple optical arrangement. We show different ways of harnessing the power of interference in the form of algorithms on this model to recognize some non-trivial languages. We then go on to show a language recognizable by a Turing machine using O(n2) space but by no 2OIA. • A natural classical model for comparison with 2QCFA is the weighted automaton, since it has the potential to capture interference in sum of path weights. Using the Cortes-Mohri definition of language recognition, we give an efficient simulation of 2QCFAwith algebraic amplitudes by weighted automata over the complex semi ring. • We introduce quantum non-determinism to the Measure-Once 1-way Quantum Finite Automata of Moore and Crutchfield and Kondacs and Watrous and show that even then, the model can recognize only regular languages with bounded error. • We propose a group theoretic generalization of counter automata that allows a notion of counter reversal complexity. To obtain this generalization, we combine concepts from classical counter automata theory with results in 2QCFA. We examine specific instances of this generalization and compare their ii iii powers. We also show an instance recognizing a language that is not recognized by conventional 2-way counter automata. Finally, we show a strict hierarchy among the 1-way versions of the instances Discussed. The second part of the thesis deals with Quantum Adiabatic Optimization algorithms. A common trick for designing faster quantum adiabatic algorithms is to apply the adiabatic condition locally at every instant. However it is often difficult to determine the instantaneous gap between the lowest two eigen values, which is an essential ingredient in the adiabatic condition. We present a simple linear algebraic technique for obtaining a lower bound on the instantaneous gap even in such a situation. As an illustration, we investigate the adiabatic unordered search of van Dam et al. and Roland and Cerf when the non-zero entries of the diagonal final Hamiltonian are perturbed by a polynomial (in logN, where N is the length of the unordered list) amount. We use our technique to derive a bound on the running time of a local adiabatic schedule in terms of the minimum gap between the lowest two eigenvalues.

The development of integrated quantum photonics is integral to many areas of quantum information science, in particular linear optical quantum computing. In this context, a diversity of physical systems is being explored and thus versatility and adaptability are important prerequisites for any candidate platform. Silicon oxynitride is a promising material because its refractive index can be varied over a wide range. This dissertation describes the development of silicon oxynitride waveguides for applications in the field of integrated quantum photonics. The project consisted of three stages: design, characterisation, and application. First, the parameter space was studied through simulations. The structures were optimised to achieve low-loss devices with a small footprint at a wavelength of 900 nm. Buried channel waveguides with a cross-section of 1.6 ?m x 1.6 ?m and a core (cladding) refractive index of 1.545 (1.505) were chosen. Second, following their fabrication with plasma-enhanced chemical vapour deposition, electron beam lithography, and reactive ion etching, the waveguides were characterised. The refractive index was shown to be tunable from the silica to the silicon nitride regime. Optimised tapers significantly improved the coupling efficiency. The minimum bend radius was measured to be less than 2 mm. Propagation losses as low as 1.45 dB cm-1 were achieved. Directional couplers with coupling ratios ranging from 0 to 1 were realised. Third, building blocks for linear optical quantum computing were demonstrated. Reconfigurable quantum circuits consisting of Mach-Zehnder interferometers with near perfect visibilities were fabricated along with a four-port switch. The potential of quantum speedup was illustrated by carrying out the Deutsch-Jozsa algorithm with a fidelity of 100 % using on-demand single photons from a quantum dot. This dissertation presents the first implementation of tunable Mach-Zehnder interferometers, which act on single photons, based on silicon oxynitride waveguides. Furthermore, for the first time silicon oxynitride photonic quantum circuits were operated with on-demand single photons. Accordingly, this work has created a platform for the development of integrated quantum photonics.