Abstract: Quantum walks are a potent technique for building quantum algorithms. This paper examines the quantum walk search algorithm on complete multipartite graphs with multiple marked vertices, which has not been explored before. We employ the coined quantum walk model and achieve quadratic speedup with a constant probability of finding a marked vertex in two […]
Abstract: Modern communication environments ranging from smart mobility infrastructures to digitally integrated healthcare systems demand adaptable and trustworthy network security. In this situation, Software-Defined Networks (SDNs) play a vital role by providing programmable control and global visibility, enabling rapid policy updates and fine-grained traffic management. As a result, the need for efficient and interpretable intrusion […]
Abstract: Randomized algorithms are crucial subroutines in quantum computing, but the requirement to execute many types of circuits on a real quantum device has been challenging to their extensive implementation. In this study, we propose an engineering method to reduce the executing time for randomized algorithms using dynamic circuits, i.e., quantum circuits involving intermediate measurement […]
Abstract: Inspired by the Fleming–Viot stochastic process, we propose a parallel implementation with restart of variational quantum algorithms, with the aim of reducing the time spent by the algorithm in barren plateaus where the optimization direction is unclear. In the Fleming–Viot tradition, parallel searches are called particles. In the proposed approach, the search by a […]
Abstract: We present an information-theoretic approach to quantum state classification based on sequential Bayesian inference. In each measurement step, the algorithm updates a probability distribution over candidate states by applying Bayes’ rule to the observed outcome. For each measurement shot on an unknown quantum state, the algorithm selects the observable with the highest expected information […]
Abstract: With the rapid advancement of quantum computing, quantum compilation has become a crucial layer connecting high-level algorithms with physical hardware. In quantum cloud computing, compilation is performed on the cloud platforms, which expose user circuits to potential risks, such as structural leakage and output predictability. To address these issues, we propose the encrypted-state quantum […]
Abstract: Recently, feedback-based quantum algorithms have been introduced to calculate the ground states of Hamiltonians, inspired by quantum Lyapunov control theory. This article aims to generalize these algorithms to the problem of calculating an eigenstate of a given Hamiltonian, assuming that the lower energy eigenstates are known. To this aim, we propose a new design […]
Abstract: Constructing effective mixer Hamiltonians is essential for enhancing the performance of the quantum approximate optimization algorithm (QAOA) in solving combinatorial optimization problems. In this work, we develop a systematic methodology for designing QAOA mixers that align with the symmetries of the classical objective function, with the goal of achieving values (mean, median, and minimum […]
Abstract: Currently, the development of quantum computers is active; however, large-scale machines remain limited and noisy. Furthermore, such quantum computers do not allow direct access to state vectors, posing challenges for quantum algorithm development. Quantum circuit simulators on classical computers offer a solution, with decision diagram (DD)-based simulators being particularly memory-efficient for representing quantum states. […]
Abstract: Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on current and near future quantum devices. The typical input preprocessing pipeline for quanvolutional layers comprises of four steps: optional input binary quantization, encoding classical data into quantum states, […]