Several emerging companies are making claims about their quantum computing capabilities. The Department of Defense needs these claims evaluated against known models to objectively compare available systems. This thesis examines the results of tests of quantum circuits on the ibmqx2 and compares them to our own calculations. This thesis provides a bridge between the quantum circuit model as presented in Quantum Computing for Computer Scientists and the compiled circuits provided by IBM. The study is limited to algorithms that can be run with a maximum of five quantum bits.
Several emerging companies are making claims about their quantum computing capabilities. The Department of Defense needs these claims evaluated against known models to objectively compare available systems. This thesis examines the results of tests of quantum circuits on the ibmqx2 and compares them to our own calculations. This thesis provides a bridge between the quantum circuit model as presented in Quantum Computing for Computer Scientists and the compiled circuits provided by IBM. The study is limited to algorithms that can be run with a maximum of five quantum bits.
In this research notebook on quantum computation and applications for quantum engineers, researchers, and scientists, we will discuss and summarized the core principles and practical application areas of quantum computation. We first discuss the historical prospect from which quantum computing emerged from the early days of computing before the dominance of modern microprocessors. And the re-emergence of that quest with the sunset of Moore's law in the current decade. The mapping of computation onto the behavior of physical systems is a historical challenge vividly illustrate by considering how quantum bits may be realized with a wide variety of physical systems, spanning from atoms to photons, using semiconductors and superconductors. The computing algorithms also change with the underline variety of physical systems and the possibility of encoding the information in the quantum systems compared to the ordinary classical computers because of these new abilities afforded by quantum systems. We will also consider the emerging engineering, science, technology, business, and social implications of these advancements. We will describe a substantial difference between quantum and classical computation paradigm. After we will discuss and understand engineering challenges currently faced by developers of the real quantum computation system. We will evaluate the essential technology required for quantum computers to be able to function correctly. Later on, discuss the potential business application, which can be touch by these new computation capabilities. We utilize the IBM Quantum Experience to run the real-world problem, although on a small scale.
Computer processor speeds double every eighteen months according to Moore's law. This growth will reach a limit by the year 2020. Quantum computation is one proposed alternative to bypass this limitation. This thesis explores the topic of quantum computation. Specifically, we address what is a quantum computer, its various proposed implementations, its technological feasibility, and its military applications. Recent experiments have provided a proof of concept for quantum computation and some researchers believe that a working model could be developed within a reasonable time period. This success has caused a marked increase in the interest in quantum computers and their proposed potential. We attempt to separate fact from fiction to see what possible benefits the Department of Defense could obtain from it.
Context: The main obstacle of quantum computing on its way to scalability is the noisy nature of current hardware. Various types of errors lead to incorrect computational results,and measures such as quantum error mitigation are needed to counteract these errors. Many error mitigation techniques require a thorough understanding of the noise and its impact on computations. Therefore, accurate noise models are indispensable to facilitate meaningful quantum computing in the near future. They also enable realistic simulations of quantum devices. Since these devices are currently limited and quantum computing is expensive, researchers typically rely on simulations for testing their algorithms.Objective: In this thesis, we aim to construct a realistic noise model for quantum computing and optimise its model parameters. The model construction should build on the underlying physical processes, and the number of parameters should scale well with the system size. We also address the evaluation and comparison of different noise models by benchmarks using quantum circuits specific to real-world applications. Finally, we aim to develop useful graphical representations of noise models based on the existing quantum circuit model.Method: We present a benchmarking framework for quantum computing noise models and evaluate it based on benchmarking quality attributes from the literature. Moreover, we construct a quantum computing noise model and develop a training procedure for its parameters. We analyse the parameters of the trained noise model and the impact ofdifferent types of errors on computations. We benchmark the noise model with the above approach and compare it to a noise model provided by IBM’s software development kit Qiskit. Finally, we develop an extension of the quantum circuit model to represent noise channels.Result: The benchmarks performed in this thesis show that our noise model predicts noisy hardware behaviour of IBM’s ibmq_manila quantum device equally well as the Qiskit model, if not better, based on quantum circuits commonly used for variational quantum algorithms. Within the trained noise model, readout error has the most detrimental impact on computations. Our benchmarking approach satisfies relevant quality criteria by choosing suitable quantum circuits and objective functions to compare model predictionsto hardware data. Moreover, the quantum circuit model can be extended meaningfully by noise channels.Conclusion: Our benchmarking approach is suitable for evaluating and comparing quantum computing noise models. In future work, volumetric benchmarks should be performed for various noise models and application contexts. The parameter optimisation based on learning with training circuits yields accurate noise models and shows the importance of mitigating measurement error. Different optimisation algorithms and quantum circuits should be explored, and more types of noise should be incorporated into the noise model.
Quantum physics provides an intriguing basis for achieving computational power to address certain categories of mathematical problems that are completely intractable with machine computation as we know it today. We present a brief overview of the current theoretical and experimental works in the emerging field of quantum computing. The implementation of a functioning quantum computer poses tremendous scientific and technological challenges, but current rates of progress suggest that these challenges will be substantively addressed over the next ten years. We provide a sketch of a quantum computing system based on superconducting circuits, which are the current focus of our research. A realistic vision emerges concerning the form of a future scalable fault-tolerant quantum computer.
Running quantum programs on real hardware requires a compilation process in which qubit operations must be mapped onto physical resources, a task commonly referred to as the mapping problem. The objective is to choose a mapping that minimizes the execution time of the compiled program. This is critical because qubits are susceptible to decoherence, which limits the time available for computation. This thesis introduces an algorithm for addressing the mapping problem on the SpinBus architecture, a shuttling-based platform in which qubits can physically move and where certain types of conflicts must be considered. Because the mapping problem is believed to be computationally hard, our approach relies on heuristic strategies that do not guarantee optimality but produce high-quality solutions within practical time limits. We also develop a method for generating an initial qubit placement. We evaluate the proposed methods on a suite of benchmark circuits and compare them to a baseline mapping strategy. Our evaluation considers several metrics that capture the runtime of the mapping algorithm, the total amount of required shuttling, the likelihood of conflicts, and the achievable parallelism.
Abstract Quantum computing is a new and exciting field with the potential to solve some of the world’s most challenging problems. Currently, with the rise of quantum computers, the main challenge is the creation of quantum algorithms (under the limitations of quantum physics) and making them accessible to scientists who are not physicists. This study presents a parametrized quantum circuit and its implementation in estimating the distribution measures for discrete value vectors. Various applications can be derived from this method, including information analysis, exploratory data analysis, and machine learning algorithms. This method is unique in providing access to quantum computation and enabling users to run it without prior knowledge of quantum physics. The proposed method was implemented and tested over a dataset and five discrete value distributions with different parameters. The results showed a high level of agreement between the classical computation and the proposed method using quantum computing. The maximum error obtained for the dataset was 5.996%, while for the discrete distributions, a maximum error of 5% was obtained.