This article presents an algorithm for fast factorization on quantum computer. Quantum mechanic introduces to information processing new effects that can be used to solve efficiently problems classical known as hard. Simon algorithm was one of the first proofs that quantum computer has some advantages over classical model of computation. Shor generalized ideas introduce by Simon and show that real world problems are in range of quantum computation. Article presents also general description of quantum Fourier transform (QFT), which is without doubt the most powerful tool used in algorithms described in this paper. Generalization of algorithms for Simon problem and fast factorization gives new class of problems for which fast quantum algorithms can be construct.
This article presents an algorithm for fast factorization on quantum computer. Quantum mechanic introduces to information processing new effects that can be used to solve efficiently problems classical known as hard. Simon algorithm was one of the first proofs that quantum computer has some advantages over classical model of computation. Shor generalized ideas introduce by Simon and show that real world problems are in range of quantum computation. Article presents also general description of quantum Fourier transform (QFT), which is without doubt the most powerful tool used in algorithms described in this paper. Generalization of algorithms for Simon problem and fast factorization gives new class of problems for which fast quantum algorithms can be construct.
We are now ready to assemble quantum subroutines into quantum algorithms. A reasonable first question is: Are there problems which can be solved efficiently on a quantum computer? In his analysis of a universal quantum computer [25], Deutsch illustrated quantum parallelism by constructing a procedure to compute the parity f (0) ⊕ f (1) of a function on one bit. In a generalization of this context, Deutsch and Jozsa [27] defined a quantum algorithm that could solve a problem more efficiently than is possible on a classical computer. This problem helped motivate the search for algorithms that could solve “real” problems, and Simon [67] provided an example that inspired Shor’s subsequent work on the factoring problem and on the discrete logarithm problem. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Quantum Fourier transform (QFT) plays an eminent role in quantum computation.It creates a superposition of different quantum states, allowing simultaneous calculation, which would take many steps were the same program implemented on a classical computer.The computational speed of a quantum computer is thus boosted dramatically.In this article, we explain the application of QFT in Shor's algorithm, which was proposed by Peter Shor to factor large integers on quantum computers.Specifically, the principle and design of quantum Fourier transform are explained.We stress the subtle distinction between QFT and its inverse.Since former articles did not emphasize it, we hope it could be a supplement to former articles.Our next work is to verify the existing articles on executing Shor's algorithm by conducting two experiments on IBMQ of factoring N = 15 in two ways (a = 7 and a = 11).We find that the effect of quantum entanglement might be crucial to the speed boost of factoring large integers in Shor's algorithm.
The power of quantum mechanics, that is too complex for conventional computers, can be solved by an innovative model of computing known as quantum computing. Quantum algorithms can provide exponential speedups for some types of problems, such as many difficult mathematical ones. In this paper, we review some of the most important quantum algorithms for hard mathematical problems. When factoring large numbers, Shor's algorithm is orders of magnitude faster than any other known classical algorithm. The Grover's algorithm, which searches unsorted databases much more quickly than conventional algorithms, is then discussed.
This survey presents a comprehensive analysis of the quantum Fourier transform (QFT), a fundamental tool in quantum computing, in comparison to its classical counterpart, the fast Fourier transform (FFT). The study begins with an introduction to the classical Fourier transform, which is widely used in different disciplines such as signal and image processing. The article introduces the QFT as a quantum version of the Fourier transform, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the operation, and highlighting its crucial role in quantum algorithms like Shor’s algorithm for integer factorization. The analysis also addresses the mathematical foundations of the QFT, its implementation in quantum circuits, and the key advantages and challenges associated with its use, such as measurement precision and quantum decoherence. Finally, it concludes with an exploration of the current and potential applications of QFT in quantum computing.
Any computation is necessarily a physical process. The current drive towards increasing speed and miniaturization of computers lead modern technology towards the subatomic domain - quantum computing, where strange quantum behavior takes over from familiar classical notions. The quantum computers have the ability to solve problems with varied computational complexities and more importantly hard problems. A problem is said to be hard, if the best possible algorithm requires exponential resources. This exponential quantum parallelism is the basis for the quantum speedup of many algorithms, and has made the construction of fast algorithms for quantum computers possible. Various proposals exist for modeling and devising procedures on computational problems related to factoring, searching, quantum counting, generalization of quantum algorithms etc. (Shor, 1994). In this paper, we have discussed the importance of algorithms such as order-finding, factoring and quantum searching which require exorbitant resources for their solution which otherwise is impossible on a classical computer
A challenging question in the overlap of computer science, mathematics, and physics, is the exploration of potential capabilities of quantum computers. Milestones were the factoring algorithm of Shor (1994) and the search algorithm of Grover (1996). So far, major research was concentrated on discrete and algebraic problems. Much less was known about computational problems of analysis, including such a prominent example as high dimensional numerical integration, which is well-studied in the classical settings. We seek to understand how efficiently this and related problems can be solved in the quantum model of computation (i.e., on a quantum computer) and how the outcome compares to the efficiency of deterministic or randomized algorithms on a classical (i.e. non-quantum) computer. In this paper we give a survey of the state of the art in this field, including also a brief introduction to the general ideas of quantum computing. Introduction A quantum computer is a computing device based on quantum mechanical laws of the (sub)atomic world. The idea of such a computer was developed by Feynman [8] in 1982. He emphasized that simulating quantum mechanics on a classical computer is extremely hard, probably infeasible. So why not try to simulate quantum mechanics using quantum devices themselves. (Thoughts in this direction were also expressed by Manin [21] in 1980, see also [22].) In 1985 Deutsch [6] presented a formal model of computation for quantum computing.