An Exploratory Study on the Usage of Quantum Programming Languages

Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasAs in the classical realm, the usage of quantum programming languages in quantum computing allows one to instruct a quantum computer to perform certain tasks. Although several imperative, declarative, and multi-paradigm quantum programming languages with different features and goals have been developed in the last 25 years, no study has been conducted on who, how, and what for does one use a quantum programming language. In this thesis, we first identified and described several quantum programming languages and then surveyed 251 quantum practitioners to answer several questions related to the usage of quantum programming languages. Further, an analysis of the results obtained is presented and shows that most of the quantum practitioners use the languages for research and that Qiskit (Python) is the most used one. Finally, we make recommendations for further development of quantum programming languages, such as building on top of a classical programming language, running in real quantum computers, supporting language documentation, and consulting developers’ needs

Programming a Gate-based Quantum Computer: a Comparative Analysis of the Software Development Kits for Circuit Design Automation

openThe rapid development of gate-based Quantum Computers has opened new possibilities for solving complex computational problems. However, programming these quantum systems has to deal with new challenges due to the fundamental differences between classical and Quantum Computing paradigms. This thesis presents a comparative analysis of Software Development Kits (SDKs) conceived for circuit design automation in gate-based quantum computers. The objective of this research is to evaluate and compare the capabilities, features, and usability of existing SDKs focusing on the functionalities such as allowing users to define quantum circuits, apply gate operations, and simulate their behaviour.   Apart from the widely adopted frameworks such as Qiskit, TKET, and Cirq, the analysis also includes the recently developed SDK from the University of Padua: Quantum Matcha Tea. The comparative analysis is conducted through a series of experiments and benchmarks performed on each SDK having as central points the programming interfaces usability, the documentation completeness, and the availability of support provided by the vendor or the related developer community. Another goal of this work is to explore the efficiency and flexibility of the various SDKs in handling common quantum computing tasks, such as quantum circuit design, gate operation, and circuit execution both on simulators and real quantum hardware.   The ambition of this comparative analysis is to give useful insights to researchers, developers, and practitioners in order to identify strengths and weaknesses of different SDKs depending on the specific requirements of the algorithms that need to be implemented. Additionally, the research aims to contribute to the advancement of SDKs by identifying areas of improvement and potential future directions in the development of quantum programming tools.The rapid development of gate-based Quantum Computers has opened new possibilities for solving complex computational problems. However, programming these quantum systems has to deal with new challenges due to the fundamental differences between classical and Quantum Computing paradigms. This thesis presents a comparative analysis of Software Development Kits (SDKs) conceived for circuit design automation in gate-based quantum computers. The objective of this research is to evaluate and compare the capabilities, features, and usability of existing SDKs focusing on the functionalities such as allowing users to define quantum circuits, apply gate operations, and simulate their behaviour.   Apart from the widely adopted frameworks such as Qiskit, TKET, and Cirq, the analysis also includes the recently developed SDK from the University of Padua: Quantum Matcha Tea. The comparative analysis is conducted through a series of experiments and benchmarks performed on each SDK having as central points the programming interfaces usability, the documentation completeness, and the availability of support provided by the vendor or the related developer community. Another goal of this work is to explore the efficiency and flexibility of the various SDKs in handling common quantum computing tasks, such as quantum circuit design, gate operation, and circuit execution both on simulators and real quantum hardware.   The ambition of this comparative analysis is to give useful insights to researchers, developers, and practitioners in order to identify strengths and weaknesses of different SDKs depending on the specific requirements of the algorithms that need to be implemented. Additionally, the research aims to contribute to the advancement of SDKs by identifying areas of improvement and potential future directions in the development of quantum programming tools

Searching for New Physics using Classical and Quantum Machine Learning

The development of machine learning (ML) has provided the High Energy Physics (HEP) community with new methods of analysing collider and Monte-Carlo generated data. As experiments are upgraded to generate an increasing number of events, classical techniques can be supplemented with ML to increase our ability to find signs of New Physics in the high-dimensional event data. This thesis presents three methods of performing supervised and unsupervised searches using novel ML methods. The first depends on the use of an autoencoder to perform an unsupervised anomaly detection search. We demonstrate that this method allows you to carry out a data-driven, model-independent search for New Physics. Furthermore, we show that by extending the model with an adversary we can account for systematic errors that may arise from experiments. The second method develops a form of quantum machine learning to be applied to a supervised search. Using a variational quantum classifier (a neural network style model built from quantum information principles) we demonstrate a quantum advantage arises when compared to a classical network. Finally, we make use of the continuous-variable (CV) paradigm of quantum computing to build an unsupervised method of classifying events stored as graph data. Gaussian boson sampling provides an example of a quantum advantage unique to the CV method of quantum computing and allows our events to be used in an anomaly detector model built using the Q-means clustering algorithm

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Notes on Quantum Computation and Information

We discuss fundamentals of quantum computing and information - quantum gates, circuits, algorithms, theorems, error correction, and provide collection of QISKIT programs and exercises for the interested reader.Comment: v2: 86 pages, 97 references. Refined the text, fixed several typos, added some text on continuous variables, and added few solved example problems. v1: 72 pages, 76 references. Suggestions, comments, and corrections are very welcome

Quantum Chemistry in the Age of Quantum Computing

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging complexity landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry such as the electronic structure of molecules. In the past two decades significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This article is an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing, and quantum computing researchers who would like to explore applications in quantum chemistry

Many-body physics meets quantum computation

Quantum computers are built directly from units that follow the laws of quantum mechanics. This allows them to perform computational tasks that are intractable on classical computers. There is an intricate interplay between the fields of quantum computing and many-body physics. First of all, we may gain a better understanding of quantum many-body systems by simulating them on quantum computers. We contribute towards this goal by designing and testing an explicit method for the quantum simulation of the Heisenberg anti-ferromagnet on the kagome lattice. Conversely, as quantum computers are scaled up, they themselves become many-body systems. Hence, we may gain a better understanding of quantum computers by using analytical techniques from many-body physics. We do so by studying the system-size dependence of the decoherence rate in the single-reservoir pure dephasing model. We determine the conditions under which this dependence scales quadratically, rather than linearly with the number of qubits. This difference in system-size staling is especially important for quantum computers with many qubits. Additionally, we study the effects that perturbations of the register-bath interaction have on decoherence-free subspaces. We find that a linear response to these perturbations is not a property of decoherence-free subspaces but is in fact generic for any register subspace. We derive a concise formula for the quadratic, leading order response. This formula can be used to identify the situations where decoherence-free subspaces work well in practice

A study of Quantum ALgorithms with Ion-trap Quantum Computers

Quantum computing will be one of the most incredible breakthroughs in science and technology of our generation. Although the ultimate goal of building quantum computers that hold thousands of error-corrected qubits is still beyond our reach, we have made substantial progress. Compared with the first-generation prototypes, holding a few qubits with gate errors of several percent, the latest generation systems can apply more than a hundred gates (with fidelities above $99\%$) to tens of fully connected qubits. This thesis focuses on the applications of such state-of-the-art ion-trap quantum computers. The latest generation ion-trap quantum computers have become complex enough that automation is necessary for optimal operations. We present a full-stack automation scheme implemented on a system at the University of Maryland. With the automation scheme, the system can operate without human interference for a few days. With automation, such systems can efficiently demonstrate different categories of applications. We present the experimental study of several hybrid algorithms aiming for generation modeling and efficient quantum state preparation. We also present a gate-based digital quantum simulation with the trotterization method. Our result accurately reproduced all the features expected from running the algorithms. Verifying quantum computations with classical simulation is getting increasingly challenging as quantum computers evolve. We present two approaches to validate quantum computations. First, we demonstrate a method based on random measurement for comparing the results from different quantum computers. Our comparison captures the similarities between quantum computers made with the same technology. We then present experimental works in verifying quantum advantage classically with interactive protocols. We show that our results, at scale with real-time interaction, can demonstrate quantum advantages

On the performance and programming of reversible molecular computers

If the 20th century was known for the computational revolution, what will the 21st be known for? Perhaps the recent strides in the nascent fields of molecular programming and biological computation will help bring about the ‘Coming Era of Nanotechnology’ promised in Drexler’s ‘Engines of Creation’. Though there is still far to go, there is much reason for optimism. This thesis examines the underlying principles needed to realise the computational aspects of such ‘engines’ in a performant way. Its main body focusses on the ways in which thermodynamics constrains the operation and design of such systems, and it ends with the proposal of a model of computation appropriate for exploiting these constraints. These thermodynamic constraints are approached from three different directions. The first considers the maximum possible aggregate performance of a system of computers of given volume, V, with a given supply of free energy. From this perspective, reversible computing is imperative in order to circumvent the Landauer limit. A result of Frank is refined and strengthened, showing that the adiabatic regime reversible computer performance is the best possible for any computer—quantum or classical. This therefore shows a universal scaling law governing the performance of compact computers of ~V^(5/6), compared to ~V^(2/3) for conventional computers. For the case of molecular computers, it is shown how to attain this bound. The second direction extends this performance analysis to the case where individual computational particles or sub-units can interact with one another. The third extends it to interactions with shared, non-computational parts of the system. It is found that accommodating these interactions in molecular computers imposes a performance penalty that undermines the earlier scaling result. Nonetheless, scaling superior to that of irreversible computers can be preserved, and appropriate mitigations and considerations are discussed. These analyses are framed in a context of molecular computation, but where possible more general computational systems are considered. The proposed model, the א-calculus, is appropriate for programming reversible molecular computers taking into account these constraints. A variety of examples and mathematical analyses accompany it. Moreover, abstract sketches of potential molecular implementations are provided. Developing these into viable schemes suitable for experimental validation will be a focus of future work