This work describes and assesses fundamental quantum computing concepts from the perspective of the social sciences. Quantum computers differ fundamentally from classical computers in that their elementary units, qubits, can exist in superpositions of multiple states simultaneously. Thisproperty makes them particularly suited for simulating systems with a large number of interacting components, a promising perspective across disciplines.In this tutorial, we introduce (i) what properties a social dynamic model should have to be easily translatable into quantum computer mechanics, (ii) eleven steps that have to be completed to achieve that translation and (iii) a practical example in which we illustrate the process.
This work describes and assesses fundamental quantum computing concepts from the perspective of the social sciences. Quantum computers differ fundamentally from classical computers in that their elementary units, qubits, can exist in superpositions of multiple states simultaneously. Thisproperty makes them particularly suited for simulating systems with a large number of interacting components, a promising perspective across disciplines.In this tutorial, we introduce (i) what properties a social dynamic model should have to be easily translatable into quantum computer mechanics, (ii) eleven steps that have to be completed to achieve that translation and (iii) a practical example in which we illustrate the process.
Over the last decade a new way to compute has been defined which, unlike conventional methods, is based on quantum mechanics rather than classical physics. This fundamental change in paradigm can, in principle, solve some important and hitherto intractable problems such as prime factorization of large numbers. This tutorial presentation will review the basics of quantum computation and the design of circuits to implement quantum algorithms. The starting point is the notion of a quantum bit or qubit. Because of the superposition property of quantum states, n qubits can store 2n numbers simultaneously, implying a type of massive parallelism. Furthermore, quantum states allow powerful forms of interaction such as entanglement that have no classical counterparts. Qubits are fragile, however, and are altered by measurement; hence quantum circuits must follow very different rules from classical ones. The differences between quantum and classical logic circuits will be discussed and illustrated. Finally, the physical implementation of quantum devices will be considered, along with the prospects for practical quantum computers.
Over the last decade a new way to compute has been defined which, unlike conventional methods, is based on quantum mechanics rather than classical physics. This fundamental change in paradigm can, in principle, solve some important and hitherto intractable problems such as prime factorization of large numbers. This tutorial presentation will review the basics of quantum computation and the design of circuits to implement quantum algorithms. The starting point is the notion of a quantum bit or qubit. Because of the superposition property of quantum states, n qubits can store 2n numbers simultaneously, implying a type of massive parallelism. Furthermore, quantum states allow powerful forms of interaction such as entanglement that have no classical counterparts. Qubits are fragile, however, and are altered by measurement; hence quantum circuits must follow very different rules from classical ones. The differences between quantum and classical logic circuits will be discussed and illustrated. Finally, the physical implementation of quantum devices will be considered, along with the prospects for practical quantum computers.
Quantum computing leverages quantum-mechanical phenomena such as superposition, entanglement and interference to solve classes of problems that are intractable on classical machines.This paper unifies three complementary perspectives: (i) the central role of complex matrices in modelling quantum states, gates and error-correction protocols; (ii) the pervasive use of trigonometric functions in qubit representation, phase manipulation and the quantum Fourier transform (QFT); and (iii) a systematic comparison of classical and quantum computing paradigms with illustrative circuits and real-world applications.By integrating these dimensions, the study provides a consolidated reference that elucidates the mathematical foundations, circuit constructions, algorithmic advantages and practical challenges that define contemporary quantum information processing.
In quantum computers a unitary transformation is applied to a given initial state of a set of qubits through several quantum gates. Tests were conducted using Grover's algorithm and implemented in a variety of different ways on the publically available IBM quantum computers. Essentially, quantum computers manipulate quantum states instead of classical bits. Quantum information can be represented by the elementary units called quantum bits abbreviated as qubits or qbits. This chapter discusses the fundamental properties of quantum systems that are relevant to information processing. Quantum computation needs something much more than that quantum parallelism to be useful. This is achieved in Deutsch's algorithm which combines quantum parallelism with interference. One can simulate a classical circuit using a quantum circuit. That is, it is possible to perform classical computations on quantum computers. But the advantage of quantum computing is that powerful functions may be computed by making use of quantum parallelism, the fundamental feature of many quantum algorithms.
Abstract: Quantum computing, an interdisciplinary field blending quantum mechanics and computer science, holds promise to revolutionize computational capabilities. Unlike classical computers, which utilize bits to process information in a binary fashion, quantum computers deploy qubits, capable of existing in multiple states simultaneously due to superposition. This unique property, paired with entanglement, offers unparalleled processing power, positioning quantum computing to tackle problems deemed unsolvable by classical means. While heralded for potential applications in cryptography, drug discovery, and complex modeling, quantum computing faces challenges including decoherence, scalability, and error correction. This paper delves into the foundational principles of quantum computing, its current advancements, the vast applications it could revolutionize, and the significant challenges and societal implications it brings. Through comprehensive exploration, we aim to shed light on both the transformative potential of quantum computing and the hurdles that lay in its path.
Quantum computing represents a transformative approach to computation by leveraging the fundamental principles of quantum mechanics. Unlike classical computing, which operates on bits, quantum computing uses quantum bits or qubits that exist in superposition, enabling parallel processing of information. This paper introduces the foundational concepts of quantum computing—such as superposition, entanglement, and quantum gates—and explores how these principles differ from classical models. Further, it highlights the implications of quantum computing in modern physics, especially in areas like quantum simulation, cryptography, and quantum field theory. By avoiding complex mathematical derivations, this paper aims to make quantum computing accessible to a broader academic audience, emphasizing both theoretical insights and practical impacts.