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arXiv:2405.16322 · Scientific Reports

Circuit implementation and analysis of a quantum-walk based search complement algorithm

Allan Wing-Bocanegra, Carlos Efrain Quintero-Narvaez, Salvador Elías Venegas-Andraca

Published Feb 10, 2025• 3 citations
arXivHTMLPDF

Abstract

We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the [Formula: see text]-complete graph with self-loops and a perturbed coin operator based on the Hadamard operator that works as an oracle for the search. The modified evolution operator leads the opposite behavior as in the original algorithm, that is, the probability to measure the target state is reduced. We call this new behavior the search complement. Furthermore, we extend the functioning of the algorithm so that it targets at multiple nodes at the same time, which makes it suitable to be used as an initialization routine for the QAOA algorithm to solve restricted QUBO problems. Taking a multigraph and matrix approach, we were able to explain that the new algorithm decreases the probability of the target states given that there are less paths that lead towards the nodes that are associated to the target states in a Unitary Coined Discrete-Time Quantum Walk. The search complement algorithm was executed experimentally on IBM quantum processor ibmq_manila obtaining statistical distances [Formula: see text] when decreasing the probability of one state out of four.

DOI
10.1038/s41598-025-87304-0

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We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the [Formula: see text]-complete graph with self-loops and a perturbed coin operator based on the Hadamard operator that works as an oracle for the search. The modified evolution operator leads the opposite behavior as in the original algorithm, that is, the probability to measure the target state is reduced. We call this new behavior the search complement. Furthermore, we extend the functioning of the algorithm so that it targets at multiple nodes at the same time, which makes it suitable to be used as an initialization routine for the QAOA algorithm to solve restricted QUBO problems. Taking a multigraph and matrix approach, we were able to explain that the new algorithm decreases the probability of the target states given that there are less paths that lead towards the nodes that are associated to the target states in a Unitary Coined Discrete-Time Quantum Walk. The search complement algorithm was executed experimentally on IBM quantum processor ibmq_manila obtaining statistical distances [Formula: see text] when decreasing the probability of one state out of four.
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}_{{\rm{f}}}=(\pi /2)\sqrt{{2}^{n-2}}tf​=(π/2)2n−2​?> t f = ( π / 2 ) 2 n − 2 .
Quantum walk search on graphs is primarily limited to real evolution operators, although there is no restriction for using the complex unitary matrix as the evolution operator. In the context of quantum search, several studies on the Grover’s search utilize the complex unitary matrix as an evolution operator, which allows for efficient searching of marked elements. However, the generalization of the quantum walk search to complex space has largely been overlooked. In this paper, we extend existing discrete-time coined quantum walk search algorithms on one- and two-dimensional periodic lattices to complex space by introducing two phase parameters, [Formula: see text], and [Formula: see text], into the coin and oracle operators, respectively, for four different discrete-time coined quantum walk search algorithms. One of the advantages of this generalization is that it enables the Grover and SKW coins with specific values of the phase parameters [Formula: see text], and [Formula: see text] to search for the exceptional configurations on a two-dimensional periodic lattice with high success probability, which is otherwise impossible to search using the real Grover and SKW coins operators.
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.
Abstract The evolution operator of the lackadaisical quantum walk on a weighted cycle graph with self-loop weight w>0w>0w>0 w > 0 is examined. With Grover oracle, the effect of different values of www w on the success probability of the search under various hyperparameter combinations is analyzed. The success probability function of the first few steps is provided algebraically, which in turn highlights the role of www w in governing the evolutionary behavior of the quantum walk search. These studies subsequently allow the numerical results to be obtained for various hyperparameter combinations, showing that the highest success probability for the weighted cycle graph with NNN N vertices is achieved using a flip-flop shift operator, a weighted coin superposition initial state, and w=1.26Nw=\frac{1.26}{N}w=N1.26​ w = 1.26 N . A comparison is also made with other known oracle, demonstrating that the proposed configuration provides a better trade-off between success probability and runtime. An extension of the study to the SKW scheme is also included, and it demonstrates that the scheme provides more variability and potential for quantum walk search.
Shenvi, Kempe, and Whaley's quantum random-walk search (SKW) algorithm [Phys. Rev. A 67, 052307 (2003)] is known to require O(N)O(\sqrt{N})O(N​) number of oracle queries to find the marked element, where NNN is the size of the search space. The overall time complexity of the SKW algorithm differs from the best achievable on a quantum computer only by a constant factor. We present improvements to the SKW algorithm which yield a significant increase in success probability, and an improvement on query complexity such that the theoretical limit of a search algorithm succeeding with probability close to one is reached. We point out which improvement can be applied if there is more than one marked element to find.
Lackadaisical quantum walks (LQW) extend discrete-time quantum walks (DTQW) by introducing weighted self-loops, enabling improved spatial-search performance through controlled localization of the walker. Despite substantial theoretical progress, practical gate-level implementations suitable for quantum hardware remain largely unexplored, limiting evaluation under realistic architectural constraints, noise, and resource requirements. In this work, we present a gate-level implementation framework for lackadaisical quantum walk search. The proposed construction encodes the position and coin spaces into qubit registers, and realizes the walk dynamics through oracle, coin, and flip-flop shift operations. We validate the circuit by reproducing the expected search behavior for single and multiple marked vertices and by analyzing the effect of the self-loop weight on the success probability. We further evaluate the implementation under realistic noisy settings using superconducting hardware's noise models. Logical resource analysis shows that, for grids ranging from 8×88\times88×8 to 64×6464\times6464×64, the transpiled gate count increases from 3.63×1053.63\times10^{5}3.63×105 to 4.38×1064.38\times10^{6}4.38×106 and the circuit depth from 2.13×1052.13\times10^{5}2.13×105 to 2.56×1062.56\times10^{6}2.56×106. Finally, fault-tolerant resource estimates based on a surface-code model using the Microsoft Quantum Resource Estimator demonstrate the substantial space-time trade-off associated with magic-state production. The results establish a practical circuit-level pathway for implementing the LQW search and provide a basis for evaluating its performance.