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arXiv:1401.6030

The Effective Solving of the Tasks from NP by a Quantum Computer

S. S. Sysoev

Published Jan 23, 2014• 1 citation
arXivHTMLPDF

Abstract

The new model of quantum computation is proposed, for which an effective algorithm of solving any task in NP is described. The work is based and inspired be the Grover's algorithm for solving NP-tasks with quadratic speedup compared to the classical computation model. The provided model and algorithm exhibit the exponential speedup over that described by Grover.

DOI
10.48550/arxiv.1401.6030

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The new model of quantum computation is proposed, for which an effective algorithm of solving any task in NP is described. The work is based and inspired be the Grover's algorithm for solving NP-tasks with quadratic speedup compared to the classical computation model. The provided model and algorithm exhibit the exponential speedup over that described by Grover.
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.
Quantum computers have the potential to out-perform classical computers—certain quantum algorithms run much faster than any known alternative classical algorithm. For example, Grover showed that a quantum computer can search an unordered list of N items in time O ( √ N), providing a quadratic speed-up over the classical algorithm. In this paper, we show that we can modify Grover’s search algorithm to give an algorithm that finds stable models of an Answer Set Program with a similar quadratic improvement over the classical algorithm. Marek and Remmel showed that Answer Set Programming (ASP) programs can uniformly solve all NP-search problems, so our quantum algorithm to find stable models of ASP programs also solves all NP-search problems. It follows that Answer Set Programming could provide a programming language for quantum computation.
In this work, we present a multi-layer quantum search method that generates an exponential speedup of the standard Grover's algorithm. As direct applications, any NP problems can be solved efficiently on a quantum circuit with only polynomial gate complexity. In particular, such multi-layer search can solve the factoring problem with an exponential speedup, providing an alternative to Shor's algorithm. Our results show that the exponential speedup of quantum circuits is ubiquitous, and Grover's search is much more powerful than that has been demonstrated. With no contradiction to the quadratic optimality of single-layer query complexity, the great potential of Grover's search is fully released by such multi-layer search design.
In 1982 Feynman noted that the simulation of a quantum process on a classical computer appears to generally involve an exponential slowdown in running time compared to the evolution of the process itself. More recently in quantum computation this effect has been turned around and exploited for computational advantage by formulating quantum processes (quantum algorithms) whose evolution corresponds to the performance of useful computational tasks. Thus the quantum algorithm will engender an exponential speedup in computing time. The most famous such quantum algorithm is Shor's algorithm for factorising whole numbers. It factorises a number having d digits in a time of order less than d whereas no known classical algorithm can perform factorisation in a time bounded by any polynomial in d. In theoretical computer science, computational complexity is generally characterised by classifying computational tasks into complexity classes such as P, BPP and NP. It therefore appears that quantum computers will be able to transgress some of the boundaries set by these classes. (2 pages)
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discriminative machine learning have been found based on efficient solving of linear algebraic problems, with potential exponential speedup in runtime under the assumption of effective input from a quantum random access memory. In machine learning, generative models represent another large class which is widely used for both supervised and unsupervised learning. Here, we propose an efficient quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is exponentially more powerful to represent probability distributions compared with classical generative models and has exponential speedup in training and inference at least for some instances under a reasonable assumption in computational complexity theory. Our result opens a new direction for quantum machine learning and offers a remarkable example in which a quantum algorithm shows exponential improvement over any classical algorithm in an important application field.
Quantum computing represents the leading edge of computational technology, leveraging the principles of quantum mechanics to execute targeted computations much faster than classical computers. In contrast to classical bits, which are limited to representing either 0 or 1, qubits, or quantum bits, exhibit the extraordinary property of superposition. This distinctive characteristic enables qubits to simultaneously occupy multiple states, empowering quantum computers to explore numerous potential solutions to a problem concurrently. This feature makes quantum computing particularly potent for specific tasks. Recent research endeavors have been sparked by the potential of advanced quantum computing technology, leading to the creation of simulations of quantum computers using classical hardware. Grover's quantum search algorithm serves as a notable illustration of quantum computing application, enabling quantum computers to conduct a database search within an unsorted array with a quadratic speedup in time efficiency compared to classical computers. This document presents the quantum Grover search algorithm and its application through 5-qubit quantum circuits, as well as a design framework to simplify the creation of an oracle for a greater number of qubits.