Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.
Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.
This article investigates the transformative potential of quantum computing in high-speed signal processing for Electrical and Electronic Engineering (EEE) systems. By examining quantum algorithms such as Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE), the study identifies significant advancements in speed and accuracy for applications like frequency analysis, noise reduction, and phase detection. These advancements could greatly benefit industries requiring rapid processing of large datasets, including telecommunications, radar systems, and real-time image processing. Despite the promising benefits, challenges posed by Noisy Intermediate-Scale Quantum (NISQ) devices such as qubit coherence, error rates, and scalability currently limit practical applications. A hybrid quantum-classical approach is proposed to address these limitations, integrating quantum algorithms into existing systems. Additionally, quantum machine learning (QML) algorithms show promise in enhancing tasks like anomaly detection and feature extraction. The findings emphasize the importance of continued progress in quantum hardware, error correction, and algorithm optimization to unlock the full potential of quantum computing in EEE systems. This study highlights the need for standardized frameworks and hybrid architectures to drive future advancements in quantum signal processing and its real-world adoption.
In this paper, we introduce quantum fourier transform as a key ingredient for many useful algorithms. These algorithms make a solution for problems which is considered to be intractable problems on a classical computer. Quantum Fourier transform is propounded as a key for quantum phase estimation algorithm. In this paper our aim is the implementation of period-finding algorithm.Quantum computer solves this problem, exponentially faster than classical one. Quantum phase estimation algorithm is the key for the period-finding problem .Therefore, by means of simulating quantum Fourier transform, we are able to implement the period-finding algorithm. In this paper, the simulation of quantum Fourier transform is carried out by Matlab software.
The Quantum Fourier Transform (QFT)andQuantumPhaseEstimation (QPE)are twofundamental algorithmic frameworks in quantum computation, and they provide the mathematical basis of many exponential-speedup quan tum algorithms, such as Shor’s factoring algorithm and quantum simulation techniques. In this work we present a detailed theoretical and computational study of the Quantum Fourier Transform and its integration in the Quan tum Phase Estimation. The discussion begins with the linear algebraic structure of the Hilbert spaces, the unitary transformations, and the complex vector representations that enable quantum interference and superposition. The QFT is studied as the unitary transform of computational basis states in the amplitudes in the frequency domain by phase rotations and tensor-product decompositions. The efficient circuit implementation in terms of Hadamard and controlled phase gates of this transform is discussed. The advantage of the transform in terms of polynomial time complexity over the classical discrete Fourier transform in some quantum scenarios is discussed.The QFT is implemented and the Quantum Phase Estimation algorithm is proposed to obtain the eigenphase information of the unitary operators. In the report the mathematical representation of QPE is derived by using the controlled unitary evolution, the inverse Fourier transforms and the measurement theory in finite dimensional Hilbert spaces. We discuss the probabilistic interpretation of phase extraction in detail and the convergence properties of the bi nary phase approximation. We also discuss the relation of QPE to eigenvalue problems in quantum mechanics and its applications to Hamiltonian simulation, quantum chemistry, cryptography, and hidden subgroup prob lems.Implementation aspects of these algorithms are studied using quantum circuit representations and oracle based computation models. We also discuss error sensitivity, qubit precision requirements, decoherence limita tions and practical constraints of Noisy Intermediate-Scale Quantum (NISQ) hardware. The work synthesizes the mathematical foundations, computational complexity analysis and physical realizations of the QFT and the QPE, demonstrating how the Fourier-based quantum transformations provide the essential mechanism underlying modern quantum algorithmic speedups and scalable quantum information processing.
This work explores the practical application of the Quantum Fourier Transform (QFT) for image alignment, focusing on the estimation of rotation angles in grayscale images containing structured line patterns and text. Motivated by the need to demonstrate quantum computing’s potential in addressing real-world image processing tasks, we develop a hybrid approach that combines classical pre-processing with quantum computation. We demonstrate that QFT can be applied to a concrete image processing task, illustrating the practical utility of quantum computing beyond theoretical examples. A classical baseline using the Fast Fourier Transform (FFT) is implemented via the ToolIP framework, achieving fast and accurate angle estimation. In parallel, a quantum version replaces the FFT with a simulated QFT on IBM’s Qiskit platform, using the Quantum Image Encoding Probability scheme to reduce qubit requirements. The classical method delivers results in milliseconds, while the quantum implementation, constrained by simulation and encoding overhead, demands significantly greater computational effort. Nonetheless, our findings show that QFT-based angle estimation is feasible and can yield results comparable to classical techniques. This study demonstrates the applicability of quantum algorithms to real-world image processing and underscores both the theoretical promise of QFT and the practical limitations faced in the current Noisy Intermediate-Scale Quantum (NISQ) era.
Fourier Transform (FT) is an important signal processing technique that translates time-domain signals to frequency-domain forms. It is widely used in several applications, including 5G networks, image processing, & spectrum analysis. In 5G networks, efficient frequency analysis is critical for noise reduction, channel optimisation, and signal dependability.This study explores the implementation of Classical Fourier Transform, Fast Fourier Transform, and Quantum Fourier Transform using Python (NumPy, Pandas, Matplotlib) and Qiskit. The FFT significantly improves processing speed, making it suitable for real-time applications. On the other hand, QFT leverages quantum superposition and entanglement, offering promising advancements for future high-speed communication and secure data transmission. Additionally, the signal is successfully reconstructed using both the Inverse Fast Fourier Transform (IFFT) and the Inverse Quantum Fourier Transform (IQFT). While IFFT efficiently restores the original signal with minimal numerical artifacts, IQFT ensures exact reconstruction without errors. The study also includes execution on IBM Quantum’s real hardware, showcasing the practical feasibility of quantum-based signal processing.
Quantum computing over the past few decades has experienced a huge boost, due in part to the discovery of algorithms such as the Shor's algorithm which promises a significant reduction in complexity compared to its analogues of classical computing. The main objective of this thesis is to introduce improvements in some algorithms based on the quantum Fourier transform, more specifically in the phase estimation algorithm and the order-finding algorithm. In addition to this, the problem of efficient generation of quantum states is also studied, where as a result a method has been developed for the efficient generation of quantum states.