Quantum computation is a subject born out of the combination between physics and computer science. It studies how the laws of quantum mechanics can be exploited to perform computations much more efficiently than current computers (termed classical computers as oppose to quantum computers). The thesis starts by introducing ideas from quantum physics and theoretical computer science and based on these ideas, introducing the basic concepts in quantum computing. These introductory discussions are intended for non-specialists to obtain the essential knowledge needed for understanding the new results presented in the subsequent chapters. After introducing the basics of quantum computing, we focus on the recently proposed quantum algorithm for linear systems. The new results include i) special instances of quantum circuits that can be implemented using current experimental resources; ii) detailed quantum algorithms that are suitable for a broader class of linear systems. We show that for some particular problems the quantum algorithm is able to achieve exponential speedup over their classical counterparts.
Quantum computation is a subject born out of the combination between physics and computer science. It studies how the laws of quantum mechanics can be exploited to perform computations much more efficiently than current computers (termed classical computers as oppose to quantum computers). The thesis starts by introducing ideas from quantum physics and theoretical computer science and based on these ideas, introducing the basic concepts in quantum computing. These introductory discussions are intended for non-specialists to obtain the essential knowledge needed for understanding the new results presented in the subsequent chapters. After introducing the basics of quantum computing, we focus on the recently proposed quantum algorithm for linear systems. The new results include i) special instances of quantum circuits that can be implemented using current experimental resources; ii) detailed quantum algorithms that are suitable for a broader class of linear systems. We show that for some particular problems the quantum algorithm is able to achieve exponential speedup over their classical counterparts.
With its potential to completely change computation, quantum computing—a groundbreaking discipline that sprung from computer science and quantum mechanics—has attracted a lot of interest. This essay attempts to cover the foundations of quantum computing and offer a thorough manual for readers who are not specialists in the subject. We start by going over the basic ideas of quantum computing and then take readers through the concepts of qubits, superposition, entanglement, interference, and noise. We study quantum gates, quantum hardware, and fundamental quantum circuits. This paper provides an overview of the present state of quantum computing, focusing on the noisy intermediate-scale quantum (NISQ) era and its possible applications to practical issues. delve into the creation of quantum algorithms and their uses, emphasizing well-known algorithms like as Grover's and Shor's. We also discuss how several fields, like material science, machine learning, encryption, and optimization, are affected by quantum computing. Upon finishing this paper, readers will possess a firm grasp of the fundamentals, practical uses, and procedures of quantum circuit development. Our objective is to offer an invaluable resource for scholars hoping to keep current on this quickly developing topic as well as for those ready to start their adventure with quantum computing
This lecture continues Lectures One and Two, where we introduced the basic concepts of quantum computing and reviewed the essential linear algebra background for undergraduate students. In this lecture, we focus on the basic computational units of quantum computation, known as quantum gates. Quantum gates play a central role in quantum algorithms, as each quantum algorithm can be implemented as a sequence of quantum gate operations. This idea has a close analogy with classical computing, where computations can be represented by Boolean circuits constructed from elementary logic gates. In principle, classical algorithms can be implemented through combinations of such logic gates. To establish this connection, we first review several fundamental logic gates in classical computing, including the AND, OR, and NOT gates. These elementary Boolean gates provide a useful foundation for understanding quantum gates and how classical computational concepts are extended into the quantum domain. In general, a quantum processing unit (QPU), which executes quantum gate operations, can be viewed as the quantum counterpart of a classical central processing unit (CPU), where computations are carried out through logical operations and circuits. This lecture establishes the foundation for understanding the quantum circuit model and for designing and analyzing quantum algorithms in subsequent lectures. To provide a smooth transition to the topics that follow, we also introduce several tiny quantum algorithms and examples in this lecture. These examples illustrate how quantum gates can be combined to perform meaningful computational tasks and prepare students for the more advanced quantum algorithms discussed later.
Linear matrix systems represent a fundamental class of problems in mathematics, physics, and engineering, and their efficient solution is crucial for various applications. Traditional methods often rely on computationally intensive algorithms, limiting scalability. This paper explores the potential of leveraging the inherent non-linear properties of linear matrices to design novel quantum algorithms for solving linear systems. We propose a quantum algorithm predicated on the principle that by mapping a linear system into a non-linear representation via a specific matrix transformation, we can achieve speedups that surpass classical approaches. The core mechanism involves constructing a quantum circuit that systematically explores this non-linear space, leveraging quantum phenomena like superposition and entanglement to enhance the search process. The paper details the design of a quantum algorithm for solving linear systems, emphasizing the benefits of this approach and its potential to unlock new possibilities in quantum computation.
This manuscript is intended for readers who have a general interest in the subject of quantum computation and provides an overview of the most significant developments in the field. It begins by introducing foundational concepts from quantum mechanics—such as superposition, entanglement, and the no-cloning theorem—that underpin quantum computation. The primary computational models are discussed, including gate-based (circuit) quantum computing, adiabatic quantum computing, measurement-based quantum computing and the quantum Turing machine. A selection of significant quantum algorithms are reviewed, notably Grover’s search algorithm, Shor’s factoring algorithm, and Quantum Singular Value Transformation (QSVT), which enables efficient solutions to linear algebra problems on quantum devices. To assess practical performance, we compare quantum and classical implementations of support vector machines (SVMs) using several synthetic datasets. These experiments offer insight into the capabilities and limitations of near-term quantum classifiers relative to classical counterparts. Finally, we review leading quantum programming platforms—including Qiskit, PennyLane, and Cirq—and discuss their roles in bridging theoretical models with real-world quantum hardware. The paper aims to provide a concise yet comprehensive guide for those looking to understand both the theoretical foundations and applied aspects of quantum computing.
Computational capabilities are reaching their limits with the current technology. Quantum computing is the answer to this limitation. It has unraveled new possibilities of solving the unsolvable and computationally hard problems within a feasible time frame. In this chapter, the authors have discussed the basics of quantum computing, which include the need for quantum computers and how it internally works without getting into too much mathematics for an introductory understanding. The authors have also discussed different technologies in which the scientific communities and industries are working to make these computers feasibly work and the different programming techniques and tools available to implement the algorithms in these computers and the testbeds available to common people for testing the feasibilities of their programs.
A quantum computer is a machine that exploits quantum phenomena to store information and perform computations.\nThe chief goal of this article is to provide a brief but comprehensive introduction to quantum computing. It overviews some mathematical underpinnings of quantum computation for readers with only a basic knowledge of linear algebra and probability. However, it does not attempt to be exhaustive nor make an exposition of quantum physics. It further does not attempt to stay current with physical implementations of quantum computers.\nAfter providing a brief historical background, the article introduces the notion of a quantum bit (qubit) and the linear operators (gates) that act on these. It then addresses systems of multiple qubits and their corresponding gates. Along the way, the article covers the essential concepts of separable systems, as well quantum interference and decoherence. It also describes how to represent gates as quantum circuits. To conclude, it explains the underpinnings of two simple but insightful quantum algorithms. A final section suggests further readings for those who wish to delve deeper into quantum computing.