In this work, we show that the usage of a quantum gate that gives extra information about the solution searched permits to improve the performance of the search algorithm by switching from quantum to classical search in the appropriated moment. A comparison to the case where only quantum search is used is also realized.
The quantum search algorithm (QSA) is one of the most recent developments in the era of quantum signal processing (QSP) and quantum information theory (QIT). It shows a faster performance in finding the solutions of system such as multiple-input multiple-output (MIMO) communication system, quantum error correction and others with a polynomial order over the classical one. Grover's algorithm is one of the first proposed QSA in identifying the index number of the solution of a system, whose solutions are known to the oracle. A further improved QSA called Boyer-Brassard-Høyer-Tapp (BBHT) is followed by the Durr-Hoyer algorithm (DHA), which proposes to find the solutions, while the exact number of the solutions is not known. In this treatise, we propose two modifications to the existing DHA and BBHT algorithms. In DHA, we propose a semi-quantum framework, where after each iteration in DHA, the non-solution points are removed in classical framework. This makes the data set iteratively shorter. In the case of BBHT algorithm, we propose to choose the Grover search number based on the Gamma distribution instead of the uniform one, which gives more chances to the new larger values to improve probability of success. The performance of the proposed QSAs demonstrates plausible improvement in search success performance as compared to their existing BBHT and DHA counterparts. Numerical simulations are conducted with the proposed QSA algorithms on a MIMO orthogonal frequency division multiplexing (OFDM) system to demonstrate the performance improvement.
Quantum Teleportation is the process of transmitting Quantum information from one place to another by performing Quantum mechanical operations like entanglement, Bell measurement and classical communication.Unlike the commonly used term teleportation, Quantum teleportation involves only transfer of information rather than particles or objects.Quantum teleportation allows transmission of quantum information despite the impossibility of measuring or broadcasting the information to be transmitted.This paper evolves some new simpler Quantum teleportation circuits which use only Quantum entanglement and a set of Hadamard and CNOT gates.Further, each of the circuits performs teleportation even if Alice does not perform measurement on her qubits.Keeping the feasibility of physical realization in mind, circuits using only adjacent CNOT gates along with Hadamard gates have also been constructed.All these circuits have been evolved using Genetic Algorithms which has been designed with appropriate representation and search operations towards faster convergence.
This paper explores the application of quantum-inspired concepts to the field of combinatorial optimization. Traditional search algorithms often struggle with the complexity of problems involving numerous possible solutions. We propose a novel approach that leverages the principles of quantum superposition and interference, albeit within a classical computing framework, to enhance the efficiency of search processes. The core of our method involves a hybrid algorithm that combines established classical search techniques with probabilistic sampling strategies informed by quantum mechanics. We aim to demonstrate that by mimicking these quantum phenomena, we can significantly improve the speed and effectiveness of solving complex combinatorial optimization problems. The algorithm's performance is evaluated through theoretical analysis and conceptual design, focusing on the potential for achieving performance gains without requiring actual quantum hardware. The key contribution lies in the strategic application of probabilistic sampling to explore solution spaces in a manner analogous to quantum superposition, followed by interference-like mechanisms to guide the search towards promising regions. This approach represents a new direction in optimization, bridging the gap between classical and quantum computing paradigms.