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IEEE Congress on Evolutionary Computation (CEC)

A practical framework to utilize quantum search

Shigeru Yamashita, Masaki Nakanishi

Published Sep 1, 2007• 0 citations
Publisher

Abstract

In this paper we propose a practical framework to utilize quantum computers in the future. To the best of our knowledge, this is the first paper to show a concrete usage of quantum computation in general programming. In our framework, we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer. To do so, we propose several new ideas and techniques, such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.

DOI
10.1109/cec.2007.4425004

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In this paper we propose a practical framework to utilize quantum computers in the future. To the best of our knowledge, this is the first paper to show a concrete usage of quantum computation in general programming. In our framework, we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer. To do so, we propose several new ideas and techniques, such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.
This paper proposes an efficient framework to utilize quantum search practically.To the best of our knowledge,this is the first paper to show a concrete usage of quantum search in general programming.In our framework,we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer.To do so,we propose several new ideas and techniques,such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.
It is widely believed that quantum computers (if realized) will be more powerful than today’s computers from the viewpoint of computational complexity . However, it is not obvious how to utilize quantum computers in practical situations. For practical purposes, a Grover search may be one of the most promising quantum algorithms known so far. Thus, in this paper, we propose an efficient framework where we can use Grover search for general programming. Our framework has the following steps. (1) A programmer writes a program by using standard C++ programming language. (2) Some if expressions within for loops in the C++ source code are chosen as candidates to be performed as a Grover search on a quantum computer. (3) The framework automatically generates a corresponding quantum circuit for each Grover search chosen in (2). Unlike the existing quantum circuit design methods, we can treat large problems in our quantum circuit design. (4) By evaluating the number of primitive quantum gates in the quantum circuit generated in (3), the framework determines whether the processing time of the quantum circuit is faster than the processing time of the corresponding if expression on a classical computer. If the framework determines that the quantum circuit is faster, it generates some interface source codes for a classical computer. Thus, in our framework, a programmer can use a Grover search with almost no effort.
This paper proposes a practical framework for quantum programming. In our framework, the parts of a program to be performed on a quantum computer are almost automatically determined, and the other parts are performed on a classical computer. We only consider Grover Search to be performed on a quantum computer in the framework because the other quantum algorithms known so far cannot be applied to general cases. By considering only Grover Search, we have several advantages that show our framework is really practical.
This paper investigates the potential of accelerating dynamic programming (DP) algorithms by incorporating search strategies derived from quantum mechanics, specifically quantum tunneling and superposition. The core premise is that the inherent exploration capabilities of quantum phenomena can be simulated through quantum-inspired search algorithms, such as quantum annealing or evolutionary algorithms mimicking tunneling behavior, to efficiently navigate the solution space of DP problems. We propose a framework where a classical DP algorithm is augmented with a quantum-inspired search component. The aim is to leverage the strengths of both approaches – the precision and systematic nature of DP combined with the potentially faster exploration offered by quantum-inspired techniques. The effectiveness of this approach is evaluated through theoretical analysis and conceptual design, focusing on scenarios where traditional DP methods struggle with large state spaces or complex decision boundaries. This research contributes to a new paradigm in algorithm design, bridging the gap between classical and quantum computing by utilizing quantum-inspired methods to optimize classical algorithms. The core idea is to treat the DP problem as a tunneling event within a high-dimensional space, significantly reducing the time required to find optimal solutions.