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Advanced Science

Quantum‐Inspired Fourier Transforms Based on Circuits

Hanxu Zhang, Yifan Sun, Xiangdong Zhang

Published Aug 21, 2025• 1 citation
Publisher

Abstract

Fourier transform (FT) is ubiquitous in modern society due to their broad applications in many branches of science and engineering. Improving the speed of FT is a common interest in the fields of signal processing. The quantum FT is generally believed to be superior to classical algorithms, but it requires a special quantum environment to perform, which has not yet been widely used. Inspired by quantum FT, here a new FT scheme is demonstrated based on circuits. In the circuit scheme, a new type of classical correlation, which its mathematical form corresponds to those of quantum entanglement, has been constructed. The calculation speed using the designed circuit scheme is equivalent to those based on the quantum FT algorithms, which is faster than those based on the classical fast FT algorithms. Furthermore, some basic gates have been designed and experimentally fabricated using classical circuit networks, which can emulate the functions of quantum gates. Fast calculation efficiencies for the FT based on the designed classical circuit networks have been demonstrated. Extensive applications of the FT scheme in the signal processing are anticipated.

DOI
10.1002/advs.202510261

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•Jan 1, 2021•25 citations
Fourier transform (FT) is ubiquitous in modern society due to their broad applications in many branches of science and engineering. Improving the speed of FT is a common interest in the fields of signal processing. The quantum FT is generally believed to be superior to classical algorithms, but it requires a special quantum environment to perform, which has not yet been widely used. Inspired by quantum FT, here a new FT scheme is demonstrated based on circuits. In the circuit scheme, a new type of classical correlation, which its mathematical form corresponds to those of quantum entanglement, has been constructed. The calculation speed using the designed circuit scheme is equivalent to those based on the quantum FT algorithms, which is faster than those based on the classical fast FT algorithms. Furthermore, some basic gates have been designed and experimentally fabricated using classical circuit networks, which can emulate the functions of quantum gates. Fast calculation efficiencies for the FT based on the designed classical circuit networks have been demonstrated. Extensive applications of the FT scheme in the signal processing are anticipated.
signals into frequency-domain forms is the Fourier Transform (FT). It is extensively utilized in several fields, such as spectrum analysis, audio and video processing, and 5G networks. Effective frequency analysis is essential for channel optimization, signal reliability, and noise reduction in 5G networks. The FFT is appropriate for real-time applications since it greatly increases processing speed. However, the Quantum Fourier Transform makes use of quantum entanglement and superposition, providing exciting new developments for secure data transfer and high-speed communication in the future. Furthermore, both the Inverse Fast Fourier Transform (IFFT) and the Inverse Quantum Fourier Transform are used to correctly recreate the signal. Implementation of Quantum's real valued computing of quantum-based signal processing is also covered in the survey.
ABSTRACT Universal quantum computation that consists of producing arbitrary input states and executing universal unitary gates is an important goal for the future information field. However, realizing a device that performs practical universal quantum computation is tricky. This is because it requires a scalable qubit system with long coherence time and good controls, which is harsh for most current platforms. Here, we provide a novel scheme to emulate the universal quantum computer by designing a classical circuit network. In the circuit network, we encode information using correlated electrical signals. The information processing elements that correspond to the universal quantum gate set are designed and embedded into the circuit network, which gives the classical circuit networks processing functions similar to that of universal quantum computation. Taking the analogy of two‐qubit processor as an example, we experimentally verify that our proposal works well. Because classical circuit networks possess good scalability and stability, the present scheme is expected to avoid some problems faced by the quantum schemes. Thus, our findings are advantageous for information processing in the era of big data.
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.
Summary The fast Fourier transform (FFT) is one of the most successful numerical algorithms of the 20th century and has found numerous applications in many branches of computational science and engineering. The FFT algorithm can be derived from a particular matrix decomposition of the discrete Fourier transform (DFT) matrix. In this paper, we show that the quantum Fourier transform (QFT) can be derived by further decomposing the diagonal factors of the FFT matrix decomposition into products of matrices with Kronecker product structure. We analyze the implication of this Kronecker product structure on the discrete Fourier transform of rank‐1 tensors on a classical computer. We also explain why such a structure can take advantage of an important quantum computer feature that enables the QFT algorithm to attain an exponential speedup on a quantum computer over the FFT algorithm on a classical computer. Further, the connection between the matrix decomposition of the DFT matrix and a quantum circuit is made. We also discuss a natural extension of a radix‐2 QFT decomposition to a radix‐d QFT decomposition. No prior knowledge of quantum computing is required to understand what is presented in this paper. Yet, we believe this paper may help readers to gain some rudimentary understanding of the nature of quantum computing from a matrix computation point of view.
This survey presents a comprehensive analysis of the quantum Fourier transform (QFT), a fundamental tool in quantum computing, in comparison to its classical counterpart, the fast Fourier transform (FFT). The study begins with an introduction to the classical Fourier transform, which is widely used in different disciplines such as signal and image processing. The article introduces the QFT as a quantum version of the Fourier transform, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the operation, and highlighting its crucial role in quantum algorithms like Shor’s algorithm for integer factorization. The analysis also addresses the mathematical foundations of the QFT, its implementation in quantum circuits, and the key advantages and challenges associated with its use, such as measurement precision and quantum decoherence. Finally, it concludes with an exploration of the current and potential applications of QFT in quantum computing.
The fast Fourier transform (FFT) is one of the most successful numerical algorithms of the 20th century and has found numerous applications in many branches of computational science and engineering. The FFT algorithm can be derived from a particular matrix decomposition of the discrete Fourier transform (DFT) matrix. In this paper, we show that the quantum Fourier transform (QFT) can be derived by further decomposing the diagonal factors of the FFT matrix decomposition into products of matrices with Kronecker product structure. We analyze the implication of this Kronecker product structure on the discrete Fourier transform of rank-1 tensors on a classical computer. We also explain why such a structure can take advantage of an important quantum computer feature that enables the QFT algorithm to attain an exponential speedup on a quantum computer over the FFT algorithm on a classical computer. Further, the connection between the matrix decomposition of the DFT matrix and a quantum circuit is made. We also discuss a natural extension of a radix-2 QFT decomposition to a radix-d QFT decomposition. No prior knowledge of quantum computing is required to understand what is presented in this paper. Yet, we believe this paper may help readers to gain some rudimentary understanding of the nature of quantum computing from a matrix computation point of view.