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arXiv:2312.16312 · International Conference on Communication Systems & Networks (COMSNETS)

A Quantum Approach to Solve N-Queens Problem

G S Santhosh, Piyush Joshi, Ayan Barui, Prasanta Kumar Panigrahi

Published Jan 3, 2024• 1 citation
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Abstract

In this work, we have introduced two innovative quantum algorithms: the Direct Column Algorithm and the Quantum Backtracking Algorithm to solve N-Queens problem, which involves the arrangement of $N$ queens on an $N \times N$ chessboard such that they are not under attack from each other on the same row, column and diagonal. These algorithms utilizes Controlled W-states and dynamic circuits, to efficiently address this NP-Complete computational problem. The Direct Column Algorithm strategically reduces the search space, simplifying the solution process, even with exponential circuit complexity as the problem size grows, while Quantum Backtracking Algorithm emulates classical backtracking techniques within a quantum framework which allows the possibility of solving complex problems like satellite communication, routing and VLSI testing.

DOI
10.1109/comsnets59351.2024.10427458

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In this work, we have introduced two innovative quantum algorithms: the Direct Column Algorithm and the Quantum Backtracking Algorithm to solve N-Queens problem, which involves the arrangement of NNN queens on an N×NN \times NN×N chessboard such that they are not under attack from each other on the same row, column and diagonal. These algorithms utilizes Controlled W-states and dynamic circuits, to efficiently address this NP-Complete computational problem. The Direct Column Algorithm strategically reduces the search space, simplifying the solution process, even with exponential circuit complexity as the problem size grows, while Quantum Backtracking Algorithm emulates classical backtracking techniques within a quantum framework which allows the possibility of solving complex problems like satellite communication, routing and VLSI testing.
Although NP-complete problems have solutions that can be verified in polynomial time, their exponential computational complexity presents significant challenges for classical computing methods. This study focuses on transferring various NP-complete problems into the quantum computing framework. The application of specific quantum computing algorithms is proposed for solving these problems. Within the scope of this study, a reusable quantum component with problem-specific constraints is designed, and a quantum circuit model is introduced to adapt the Quantum Feasibility Labeling algorithm for solving the Maximum Clique problem. Additionally, the Quick Quantum Search algorithm, which performs a search with a single oracle call, is integrated into the solution of the Maximum Independent Set problem. The proposed quantum circuits provide illustrative examples of how the components of an NP-complete problem can be represented in a quantum computing environment and how problem-specific constraints can be implemented within a quantum circuit. The utilization of modular circuit designs effectively demonstrates the applicability of larger and more complex problems in quantum environments. These approaches not only enable more efficient use of computational resources compared to traditional methods but also lead to significant reductions in solution times. This study provides a comprehensive framework that not only demonstrates the practicality of quantum circuits for small-scale NP-complete problems but also lays the groundwork for addressing larger and more complex challenges in future quantum computing applications.
Quantum computing offers an accelerated mechanism for solving combinatorial optimization problems that could be hardly solvable with classical tools, in this context, this paper highlights the utility of quantum computing in solving NP-complete problems efficiently and shows up the importance of employing quantum algorithms achieving parallel calculation in the purpose of reducing the algorithmic complexity and simplifying the development of optimization applications. Within this publication, an optimized version of quantum circuit conception for developing the oracle of the Grover's search algorithm is introduced for solving the vertex cover problem, and it was also tested using the Qiskit library and applied to a set of instances to evaluate its performance.
$ $ We describe a general method to obtain quantum speedups of classical algorithms which are based on the technique of backtracking, a standard approach for solving constraint satisfaction problems (CSPs). Backtracking algorithms explore a tree whose vertices are partial solutions to a CSP in an attempt to find a complete solution. Assume there is a classical backtracking algorithm which finds a solution to a CSP on nnn variables, or outputs that none exists, and whose corresponding tree contains TTT vertices, each vertex corresponding to a test of a partial solution. Then we show that there is a bounded-error quantum algorithm which completes the same task using O(Tn3/2log⁡n)O(\sqrt{T} n^{3/2} \log n)O(T​n3/2logn) tests. In particular, this quantum algorithm can be used to speed up the DPLL algorithm, which is the basis of many of the most efficient SAT solvers used in practice. The quantum algorithm is based on the use of a quantum walk algorithm of Belovs to search in the backtracking tree.
This paper aims to propose solution for a classical and difficult decision problem using quantum computing. At first, it provides experimental samples taking well-known search problems, where it is possible to establish a plausible comparison between classical search algorithms and a similar quantum algorithm. Moreover, a quantum algorithm is construct in order to deliver solution for a sophisticated mathematical problem called N-Queens. The algorithm was implemented in a quantum computing simulator obtaining solutions for problems with up to 128 queens at a reasonable cost.
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Quantum computers can potentially solve problems that are computationally intractable on a classical computer in polynomial time using quantum-mechanical effects such as superposition and entanglement. The N-Queens Problem is a notable example that falls under the class of NP-complete problems. It involves the arrangement of N chess queens on an N x N chessboard such that no queen attacks any other queen, i.e. no two queens are placed along the same row, column or diagonal. The best time complexity that a classical computer has achieved so far in generating all solutions of the N-Queens Problem is of the order O(N!). In this paper, we propose a new algorithm to generate all solutions to the N-Queens Problem for a given N in polynomial time of order O(N^3) and polynomial memory of order O(N^2) on a quantum computer. We simulate the 4-queens problem and demonstrate its application to satellite communication using IBM Quantum Experience platform.