This paper proposes a quantum switching on crossed cube, the diameter of the crossed cube is almost half of that of the hypercube, to execute quantum walks. Quantum walks can be implemented in the hypercube and general graphs. Hypercube is a regular graph. According to quantum walk algorithm, the system can preserve unitary property of quantum walks. In the regular graph, to develop quantum walks algorithm is focused on quantum search algorithm. The proposed circuit is to use quantum state, transferring from source node to destination for find the searching object. The small diameter is the best criterion to simulate the behavior of the proposed mechanism. This work selects crossed cube as the research topics with the following two major clauses. One is the small diameter and the other is the exchanged crossed state. The crossed cube has the half diameter of the hypercube. This cube owns the exchanged crossed state that can be used to present the behavior of the entangled particles. Quantum switching is reversible circuit and parallel computation circuit. By using parallel computation circuit, the system can achieve the better performance in time complexity and space complexity. Based on quantum switching, the system can trace the behavior of quantum walks from input sequence to output sequence, and obtains the benefit in time complexity and space complexity for quantum search algorithm.
This paper proposes a quantum switching on crossed cube, the diameter of the crossed cube is almost half of that of the hypercube, to execute quantum walks. Quantum walks can be implemented in the hypercube and general graphs. Hypercube is a regular graph. According to quantum walk algorithm, the system can preserve unitary property of quantum walks. In the regular graph, to develop quantum walks algorithm is focused on quantum search algorithm. The proposed circuit is to use quantum state, transferring from source node to destination for find the searching object. The small diameter is the best criterion to simulate the behavior of the proposed mechanism. This work selects crossed cube as the research topics with the following two major clauses. One is the small diameter and the other is the exchanged crossed state. The crossed cube has the half diameter of the hypercube. This cube owns the exchanged crossed state that can be used to present the behavior of the entangled particles. Quantum switching is reversible circuit and parallel computation circuit. By using parallel computation circuit, the system can achieve the better performance in time complexity and space complexity. Based on quantum switching, the system can trace the behavior of quantum walks from input sequence to output sequence, and obtains the benefit in time complexity and space complexity for quantum search algorithm.
Quantum walks can be implemented in the hypercube and general graphs, where hypercube is a regular graph. According to quantum walk algorithm, unitary property of quantum walks can be preserved. In the regular graph, to develop quantum walks algorithm is focused on quantum search algorithm. Quantum switching is the reversible and parallel computation circuits, where parallel computation circuit can achieve the better performance in time complexity and space complexity. Especially, this switching can trace the behavior of quantum walks from input sequence to output sequence, looking as quantum search algorithm. Furthermore, application of unicasting and multicasting can be implemented in this circuit.
Quantum walks are a powerful framework for simulating complex quantum systems and designing quantum algorithms, particularly for spatial search on graphs, where the goal is to find a marked vertex efficiently. In this work, we present efficient quantum circuits that implement the evolution operator of continuous-time quantum-walk-based search algorithms for three graph families: complete graphs, complete bipartite graphs, and hypercubes. For complete and complete bipartite graphs, our circuits exactly implement the evolution operator. For hypercubes, we propose an approximate implementation that closely matches the exact evolution operator as the number of vertices increases. Our Qiskit simulations demonstrate that even for low-dimensional hypercubes, the algorithm effectively identifies the marked vertex. Furthermore, the approximate implementation developed for hypercubes can be extended to a broad class of graphs, enabling efficient quantum search in scenarios where exact implementations are impractical.
The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.
As a new quantum computing model, quantum walk has been widely studied in recent years. It consists of discrete time quantum walk and continuous time quantum walk. Discrete time quantum walk includes coin quantum walk and scattering quantum walk. Meanwhile as a quantum search algorithm, Grover algorithm can search an unsorted database in time complexity of O(√N) . Recent years quantum walk has been used to solve search problems and some of them have been proved to be as efficient as Grover algorithm. Making full use of the novel properties of quantum walk, the quantum walk search algorithms on the 2D grid and hypercube have been proposed. Inspired by phase matching condition of Grover algorithm, we propose a new quantum walk search algorithm which is based on coin quantum walk. Firstly we give the graph to the quantum walk, and then describe the algorithm in detail. Algorithm uses different coin operators and shift operators for two different cases and draws the corresponding iteration operators. Then we prove that iteration operators used in the algorithm are unitary operators. Then we analyze the time complexity and probability of success of the algorithm. Analysis indicates that the time complexity of the algorithm is the same as that of Grover algorithm, however when the targets to be searched are more than 1/3 of the total targets, the algorithm probability of success is greater than that of Grover algorithm. Finally we give the circuit implementation of the algorithm.
In this paper, we propose a circuit design for implementing quantum walks on complex networks. Quantum walks are powerful tools for various graph-based applications such as spatial search, community detection, and node classification. Although many quantum-walk-based graph algorithms have been extensively studied, specific quantum circuits for implementing these algorithms have not yet been provided. To address this issue, we present a circuit design for implementing the discrete-time quantum walk on complex networks. We investigate the functionality of our circuit using the small-sized Watts-and-Strogatz model as the complex network model, comparing it with theoretical calculations. This work offers a new approach to constructing quantum circuits for implementing quantum walks on arbitrary complex networks.
Shenvi et al. have proposed a quantum algorithm based on quantum walking called Shenvi–Kempe–Whaley (SKW) algorithm, but this search algorithm can only search one target state and use a specific search target state vector. Therefore, when there are more than two target nodes in the search space, the algorithm has certain limitations. Even though a multi-objective SKW search algorithm was proposed later, when the number of target nodes is more than two, the SKW search algorithm cannot be mapped to the same quotient graph. In addition, the calculation of the optimal target state depends on the number of target states m. In previous studies, quantum computing and testing algorithms were used to solve this problem. But these solutions require more Oracle calls and cannot get a high accuracy rate. Therefore, to solve the above problems, we improve the multi-target quantum walk search algorithm, and construct a controllable quantum walk search algorithm under the condition of unknown number of target states. By dividing the Hilbert space into multiple subspaces, the accuracy of the search algorithm is improved from p c = (1/2) – O(1/n) to p c = 1 – O(1/n). And by adding detection gate phase, the algorithm can stop when the amplitude of the target state becomes the maximum for the first time, and the algorithm can always maintain the optimal number of iterations, so as to reduce the number of unnecessary iterations in the algorithm process and make the number of iterations reach <?CDATA tf=(π/2)2n−2?> t f = ( π / 2 ) 2 n − 2 .