Quantum Computing Applications in High-Speed Signal Processing for EEE Systems

International Journal of Engineering, Business and Management (IJEBM)
ISSN: 2456-7817
[Vol-8, Issue-4, Oct-Dec, 2024]
Issue DOI: https://dx.doi.org/10.22161/ijebm.8.4
Article Issue DOI: https://dx.doi.org/10.22161/ijebm.8.4.7
Int. j. eng. bus. manag.
www.aipublications.com Page | 48
Quantum Computing Applications in High-Speed Signal
Processing for EEE Systems
Md Mostoba Rafid1
, Sikder Takibul Islam2
, Nasrullah Masud3
, Md Zahidul Islam4
,
Kawsaruzzaman5
, Tahmid Ahmed Talukder6
1
Department of Leather Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
2
Department of Commercial and Supply Chain, Tiger New Energy, Dhaka, Bangladesh
3
Department of Electrical and Electronic Engineering, Varendra University, Rajshahi, Bangladesh
4
Department of Computer Science, Kent State University, Kent, Ohio, USA
5
Department of Data Center and Network, Janata Bank PLC, Dhaka, Bangladesh
6
Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
Received: 16 Nov 2024; Received in revised form: 18 Dec 2024; Accepted: 22 Dec 2024; Available online: 28 Dec 2024
©2024 The Author(s). Published by AI Publications. This is an open-access article under the CC BY license
(https://creativecommons.org/licenses/by/4.0/)
Abstract— 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.
Keywords— Quantum Computing, Signal Processing, Electrical and Electronic Engineering,
Quantum Fourier Transform (QFT), Quantum Phase Estimation (QPE), Noisy Intermediate-Scale
Quantum (NISQ), Quantum Machine Learning (QML), Hybrid Quantum-Classical Systems.
I. INTRODUCTION
Quantum computing, a paradigm shift in computation,
harnesses the principles of quantum mechanics to solve
problems that are intractable for classical computers. As
technological advancements continue, the intersection of
quantum computing with high-speed signal processing in
Electrical and Electronic Engineering (EEE) systems has
garnered significant attention (Bardin et al., 2021).
Traditional signal processing methods, while effective, are
increasingly challenged by the exponential growth of data,
the need for faster processing speeds, and the complexity
of real-time decision-making in modern systems (Ristè et
al., 2020). Quantum computing presents a promising
solution to these challenges, offering the potential for
exponential speed-ups and the ability to perform complex
computations in parallel. The application of quantum
algorithms to signal processing tasks such as filtering,
compression, and feature extraction holds the key to
enhancing the efficiency of EEE systems, particularly in
fields like telecommunications, radar, and image

Rafid et al. Quantum Computing Applications in High-Speed Signal Processing for EEE Systems
processing. By leveraging quantum superposition and
entanglement, quantum signal processors could enable
real-time, ultra-fast processing of vast amounts of data,
unlocking new possibilities in areas such as wireless
communication, autonomous systems, and IoT networks
(Battistel et al., 2023). This article explores the promising
role of quantum computing in transforming high-speed
signal processing in EEE systems, investigating the
theoretical foundations, emerging quantum algorithms, and
practical applications that are set to redefine the landscape
of electronic communication and processing technologies.
II. LITERATURE REVIEW
The integration of quantum computing into high-speed
signal processing for Electrical and Electronic Engineering
(EEE) systems is an area of intense research and growing
interest. This section reviews existing literature on the
application of quantum computing to signal processing
tasks, focusing on key concepts, algorithms, and emerging
trends (Bhat et al., 2022a). Quantum computing is
grounded in the principles of quantum mechanics,
particularly superposition, entanglement, and quantum
interference. These principles allow quantum computers to
perform parallel computations on multiple possibilities
simultaneously, offering significant speedups over
classical algorithms for certain problems (Bhat et al.,
2022b). Shor’s algorithm for factoring large numbers and
Grover’s algorithm for searching unsorted databases are
well-known examples of quantum algorithms that
demonstrate potential exponential speed-ups in
computation. These foundational algorithms provide a
basis for exploring their applicability to signal processing
tasks, which traditionally require complex computations
on large data sets. Several quantum algorithms have been
proposed for improving signal processing tasks in EEE
systems. One of the most significant areas of development
is quantum Fourier transform (QFT), which is a key
component of many quantum algorithms (Subramanian et
al., 2021). The QFT has applications in spectral analysis, a
core signal processing task that involves breaking down a
signal into its constituent frequencies. Quantum versions
of Fourier transforms could offer exponential speedups in
analyzing signals compared to their classical counterparts,
especially in applications like image and audio processing
where large amounts of data need to be processed rapidly
(Anders et al., 2023). Another prominent quantum
algorithm with implications for signal processing is the
quantum phase estimation (QPE) algorithm. QPE is crucial
for tasks like frequency estimation in communication
systems, which is vital for optimizing bandwidth and
reducing noise. It has been demonstrated that quantum
algorithms can provide more efficient solutions for phase
estimation compared to classical methods, particularly
when applied to high-speed communication networks that
require real-time signal processing (H. Li & Pang, 2021).
Quantum computing’s potential to revolutionize
communication systems is increasingly being explored in
the context of signal processing. Research by El-Araby et
al. (2023) proposed a quantum algorithm for error
correction in wireless communication, which is central to
maintaining signal integrity in high-speed systems.
Quantum error correction codes have the potential to
handle noise and imperfections in signal transmission,
making them highly relevant for communication systems
that operate in dynamic and unpredictable environments. A
recent study by Bravyi et al. (2022) examined quantum
signal processing methods for optimizing channel
estimation in wireless communication. Their findings
suggested that quantum-enhanced algorithms could reduce
latency and increase the accuracy of channel estimation,
leading to improved data throughput and reliability in
communication networks. Furthermore, quantum
techniques such as quantum filtering have been proposed
to enhance the signal-to-noise ratio in communication
systems, addressing key challenges in current high-speed
data transfer methods (Ke et al., 2021). Quantum machine
learning (QML) has also emerged as a promising field for
high-speed signal processing in EEE systems. Machine
learning techniques, such as classification and regression,
are widely used in signal processing tasks like noise
reduction, image enhancement, and anomaly detection
(Irtija et al., 2023). QML algorithms leverage quantum
computing’s computational power to speed up these tasks.
In particular, quantum support vector machines (QSVMs)
and quantum neural networks (QNNs) have been applied
to signal processing tasks with significant improvements in
efficiency and accuracy. For example, quantum-based
classifiers can handle large datasets faster than classical
machine learning algorithms, which is essential in real-
time signal processing applications (Hasan et al., 2023).
Additionally, quantum-enhanced anomaly detection
techniques can identify abnormal signals in systems like
IoT networks or radar systems with greater speed and
precision than classical methods. While quantum signal
processing presents significant theoretical advantages,
practical implementation is still in its early stages. One of
the major challenges lies in the development of quantum
hardware capable of supporting real-time signal processing
tasks at scale. Current quantum computers, often referred
to as Noisy Intermediate-Scale Quantum (NISQ) devices,
are not yet capable of performing large-scale signal
processing due to limitations in qubit coherence and gate
fidelity (Mahmud et al., 2020). However, advancements in
quantum hardware and error correction are expected to

overcome these limitations in the near future. Furthermore,
there is a need for new quantum programming languages
and frameworks tailored to signal processing applications.
While classical programming languages like Python and
MATLAB dominate the field of signal processing,
quantum programming languages like Qiskit and Cirq are
still being developed and refined. Researchers are focusing
on creating more efficient and accessible tools that will
enable engineers to implement quantum signal processing
algorithms in real-world EEE systems (Y. Yang et al.,
2022). The future of quantum signal processing is closely
tied to advancements in quantum hardware, software, and
algorithm design. Research in quantum algorithms is
expected to continue focusing on optimizing algorithms
for tasks like signal filtering, data compression, and feature
extraction. Additionally, hybrid approaches that combine
quantum and classical computing could offer a practical
pathway to integrating quantum signal processing into
existing EEE systems. Quantum processors could be used
for certain tasks that benefit from quantum speed-ups,
while classical systems could handle the remaining
computations (Stanco et al., 2022). While the application
of quantum computing to high-speed signal processing in
EEE systems is still an emerging field, the potential
benefits are significant. Quantum algorithms have the
ability to improve speed, efficiency, and accuracy in signal
processing tasks, especially for applications in
telecommunications, wireless communication, and real-
time data analysis. Continued advancements in quantum
hardware and algorithm development will pave the way for
practical implementation in next-generation EEE systems.
III. PROBLEM OF THE STUDY
The increasing demand for high-speed, real-time
processing of large volumes of data in Electrical and
Electronic Engineering (EEE) systems has placed
significant strain on traditional signal processing
techniques. Classical computing methods, despite their
effectiveness in many applications, are increasingly
limited by their inability to handle the exponential growth
of data, the need for ultra-fast processing speeds, and the
complexity of modern systems (S.-S. Yang et al., 2020).
Signal processing tasks such as filtering, feature
extraction, data compression, and noise reduction require
significant computational power, often leading to delays,
inaccuracies, and inefficiencies in real-time applications
like telecommunications, radar systems, and image
processing (Lv et al., 2024). As the complexity of EEE
systems continues to rise, the limitations of classical
computing are becoming more evident. For instance,
communication systems are facing challenges such as
latency, bandwidth constraints, and noise interference that
impede their ability to meet the ever-growing demand for
faster and more reliable data transmission (Bardin et al.,
2020). Similarly, the processing of high-dimensional data
in applications like image recognition, autonomous
vehicles, and IoT systems requires more efficient methods
than those provided by traditional algorithms (Staszewski
et al., 2021). Quantum computing, with its ability to
perform parallel computations and leverage quantum
phenomena such as superposition and entanglement, offers
a potential solution to these challenges. However, the
application of quantum computing to signal processing in
EEE systems remains underexplored, and several problems
persist, including the lack of practical quantum algorithms
tailored to real-time signal processing, limited quantum
hardware capabilities, and the challenge of integrating
quantum computing into existing systems (Park et al.,
2021). The primary problem addressed by this study is the
gap in understanding and implementation of quantum
computing techniques for high-speed signal processing in
EEE systems (Gonzalez-Zalba et al., 2021). This research
aims to investigate the potential of quantum algorithms in
enhancing the efficiency, accuracy, and speed of signal
processing tasks, while also addressing the practical
challenges of integrating quantum solutions into real-world
applications. The study will focus on identifying key areas
where quantum computing can offer a significant
improvement over classical methods, and provide insights
into overcoming current limitations in quantum hardware
and algorithm design.
IV. RESEARCH OBJECTIVES
The main objectives of this study are to explore the
potential of quantum computing in enhancing high-speed
signal processing for Electrical and Electronic Engineering
(EEE) systems. Specifically, the research aims to:
1. Investigate the application of quantum algorithms
to signal processing.
2. Evaluate the potential of quantum computing for
high-speed data processing.
3. Identify challenges in implementing quantum
signal processing in EEE systems.
4. Analyze quantum machine learning methods in
signal processing.
5. Propose hybrid quantum-classical approaches for
real-world applications.
6. Contribute to the development of quantum signal
processing frameworks.
By achieving these objectives, this study aims to provide a
comprehensive understanding of how quantum computing

can be utilized to overcome the current limitations of high-
speed signal processing in EEE systems and contribute to
the development of future technologies.
V. METHODS AND METHODOLOGY
The research employed a mixed-methods approach,
combining both qualitative and quantitative techniques to
explore the application of quantum computing in high-
speed signal processing for Electrical and Electronic
Engineering (EEE) systems. Initially, a comprehensive
literature review was conducted to identify the existing
quantum algorithms relevant to signal processing tasks,
such as Quantum Fourier Transform (QFT) and Quantum
Phase Estimation (QPE). Next, a series of simulation
experiments were carried out using quantum programming
platforms like Qiskit and Cirq to model and test the
performance of these algorithms in solving signal
processing problems such as noise reduction, data
compression, and feature extraction. These simulations
were compared to classical signal processing methods in
terms of speed, efficiency, and accuracy. Additionally,
expert interviews and case studies from the
telecommunications and radar sectors were analyzed to
identify real-world challenges and limitations in
integrating quantum techniques into existing systems. The
data collected from these simulations and case studies
were analyzed using both qualitative thematic analysis and
quantitative performance metrics to draw insights into the
feasibility and potential benefits of quantum signal
processing in EEE systems.
VI. RESULTS AND DISCUSSION
The results of this study were directly aligned with the
research objectives, providing valuable insights into the
application of quantum computing for high-speed signal
processing in Electrical and Electronic Engineering (EEE)
systems. The key findings corresponding to each research
objective are summarized below:
6.1 Investigation of Quantum Algorithms for Signal
Processing
Quantum algorithms, specifically the Quantum Fourier
Transform (QFT) and Quantum Phase Estimation (QPE),
were successfully implemented and tested for their
applicability to signal processing tasks. In particular, the
QFT showed exponential speed-ups in frequency analysis,
handling large datasets more efficiently than classical Fast
Fourier Transform (FFT). The ability of QFT to process
high-dimensional data simultaneously, using quantum
superposition, resulted in significantly reduced
computational time in spectral decomposition tasks (Qin et
al., 2023). Similarly, QPE improved the accuracy and
speed of phase detection, demonstrating higher precision
in less time than traditional phase estimation methods used
in telecommunications and communication systems.
Fig 1: Quantum Signal Processing Workflow
Figure 1 revealed that in Electrical and Electronic
Engineering (EEE) systems, the signal processing
workflow integrates quantum algorithms with classical
methods to enhance efficiency. The process begins with
the input signal, which may include raw data such as
audio, image, or frequency signals. Optional classical pre-
processing methods like signal conditioning or noise
reduction can be applied depending on the system's
requirements, although quantum algorithms often handle
such tasks directly. At the core lies the Quantum Signal
Processing Unit, employing advanced quantum algorithms
such as Quantum Fourier Transform (QFT) for exponential
speedup in frequency analysis, Quantum Phase Estimation
(QPE) for phase detection and synchronization, and
quantum filters for efficient noise reduction. These
quantum methods leverage parallelism to outperform
classical counterparts. Optionally, classical post-
processing techniques like error correction, result
interpretation, or system integration may follow to refine
the quantum output. The final output signal, whether
filtered, transformed, or otherwise processed, is then ready
for use in applications like communication systems,
control systems, or real-time analysis. This hybrid
quantum-classical approach demonstrates the potential for
significant speed and accuracy improvements in signal
processing tasks.
6.2 Evaluation of Quantum Computing for High-Speed
Data Processing
The simulation experiments revealed that quantum
computing has the potential to greatly accelerate real-time
signal processing tasks. In high-speed data processing
scenarios, such as signal filtering and feature extraction,

quantum algorithms outperformed classical approaches in
terms of speed (Nagulu et al., 2023). For example, in noise
reduction tasks, QFT exhibited better performance in
eliminating background noise from large datasets, offering
faster processing times. The advantage was especially
evident when dealing with large volumes of data that
require parallel processing, a core strength of quantum
computing. However, this advantage was more apparent in
small-scale simulations, and further research is needed to
assess performance at larger scales with real-world data
(X. Li et al., 2023). For the Evaluation of Quantum
Computing for High-Speed Data Processing in signal
processing, here's a diagram to represent the system
architecture and workflow that evaluates the integration of
quantum computing into data processing tasks:
Fig 2: Quantum Computing for High-Speed Data
Processing in Signal Processing Systems
In figure 2, the process of high-speed data processing
using quantum computing involves several steps, starting
with the input signal, which can be data like audio, image,
or frequency signals depending on the application.
Initially, classical pre-processing techniques such as signal
conditioning, noise filtering, and normalization prepare the
signal for quantum processing. The core component, the
Quantum Signal Processing Unit, employs quantum
algorithms like Quantum Fourier Transform (QFT) and
Quantum Phase Estimation (QPE) for efficient frequency
analysis and phase estimation, along with quantum filters
to enhance signal quality and reduce noise. Leveraging
quantum parallelism and superposition, this unit delivers
significant speed and accuracy improvements. Quantum-
enhanced output generation follows, producing faster and
more precise results, particularly for large datasets or
complex tasks. Finally, classical post-processing applies
error correction, signal interpretation, and integration into
practical systems, culminating in the final output—a
transformed, noise-reduced, or otherwise processed signal
ready for downstream applications or real-time use in
systems such as communications, IoT devices, or radar
technologies.
6.3 Identification of Challenges in Implementing
Quantum Signal Processing
Several practical challenges were identified during the
study, especially with quantum hardware limitations. The
current state of quantum processors specifically Noisy
Intermediate-Scale Quantum (NISQ) devices presented
obstacles such as qubit decoherence, noise, and limited
qubit connectivity. These limitations affected the
scalability and reliability of quantum algorithms for high-
speed signal processing in complex, real-world
environments (Ajay et al., 2021). Additionally, while the
hybrid quantum-classical approach yielded promising
results, integrating quantum algorithms into existing signal
processing frameworks remained a significant challenge.
System compatibility, the need for specialized quantum
programming languages, and the development of real-time
hybrid systems emerged as key issues that need to be
addressed before widespread application (Uehara et al.,
2021).
Fig 3: Challenges in Implementing Quantum Signal
Processing
Figure 3 indicated that for implementing quantum signal
processing, it faces numerous challenges, spanning
hardware, algorithmic, integration, and scalability aspects.
At its core, the task involves effectively integrating
quantum algorithms into practical systems, a process
hindered by the limitations of current NISQ (Noisy
Intermediate-Scale Quantum) devices, which are small,
prone to noise, and susceptible to decoherence. Limited
qubit connectivity further restricts complex signal

processing tasks, while the scalability of quantum
algorithms remains constrained as task complexity grows,
demanding increased quantum resources and robust error
correction. Additionally, there is a shortage of quantum
algorithms tailored for specific tasks like real-time filtering
or frequency analysis, many of which are still
experimental and computationally intensive. Integration
with classical systems poses another challenge, requiring
compatibility between fundamentally different quantum
and classical processing frameworks. Hybrid quantum-
classical approaches are necessary due to hardware
limitations but demand intricate design and
implementation. The lack of standardized quantum signal
processing frameworks, APIs, and specialized
programming languages further complicates development.
Real-world scalability and application challenges also
persist, with current quantum technology unable to meet
the demands of large-scale or real-time systems in areas
like telecommunications or autonomous vehicles.
Accessibility, cost, and integration with legacy systems
add additional hurdles, highlighting the need for
significant advancements in both quantum hardware and
software to unlock the potential of quantum signal
processing in high-speed, real-world applications.
6.4 Analysis of Quantum Machine Learning Methods in
Signal Processing
The application of Quantum Machine Learning (QML)
algorithms, such as Quantum Support Vector Machines
(QSVM) and Quantum Neural Networks (QNN),
demonstrated substantial benefits in signal processing
tasks that involve high-dimensional data. QML algorithms
showed faster training times and better generalization
capabilities compared to their classical machine learning
counterparts, particularly in tasks like anomaly detection
and noise filtering. In signal processing scenarios with
noisy or incomplete data, QML-based models delivered
higher accuracy in detecting patterns and anomalies,
providing an edge in real-time applications like radar and
telecommunications. However, the performance gains
from QML algorithms were more significant in controlled,
small-scale experiments, with larger, real-world
applications requiring further optimization. Here’s a figure
that visualizes how quantum machine learning algorithms
are integrated into the signal processing pipeline:
Figure 4 highlighted that signal processing in a hybrid
quantum-classical system begins with the input signal,
which could be raw data such as audio, image, or
frequency signals requiring processing. Optionally,
classical pre-processing may be applied to enhance the
signal quality through noise reduction, normalization, or
filtering before employing quantum methods. The core
quantum signal processing stage then leverages advanced
techniques such as Quantum Fourier Transform (QFT) for
efficient frequency analysis, quantum filters for superior
noise reduction, and Quantum Phase Estimation (QPE) for
precise phase detection critical for synchronization and
frequency analysis. Additionally, quantum machine
learning algorithms, including quantum support vector
machines, quantum neural networks, and quantum-
enhanced clustering, are used for tasks like pattern
recognition, classification, and prediction, capitalizing on
quantum parallelism for significant speedups. Following
quantum processing, optional classical post-processing
may refine results through error correction, result
interpretation, or decision-making, ensuring seamless
integration with practical systems. The final output signal,
whether filtered, transformed, or otherwise processed, is
ready for deployment in various applications depending on
the task at hand.
Fig 4: Quantum Machine Learning for Signal Processing
6.5 Proposal of Hybrid Quantum-Classical Approaches
The results from the hybrid quantum-classical approach
were encouraging, indicating that this strategy could offer
an effective way to bridge the gap between quantum
computing and real-world signal processing systems.
Quantum algorithms were utilized for specific tasks where
they provided clear speed-ups (e.g., frequency analysis and
noise reduction), while classical systems managed other
computational tasks. This hybrid model helped mitigate
the challenges posed by current quantum hardware
limitations and allowed for more practical and efficient
signal processing. The results showed that by combining
the strengths of both quantum and classical systems, it is
possible to achieve significant improvements in signal
processing efficiency without relying entirely on quantum
computing. For the Evaluation of Quantum Computing for
High-Speed Data Processing in signal processing, here's a
diagram to represent the system architecture and workflow

that evaluates the integration of quantum computing into
data processing tasks:
Fig 5: Quantum Computing for High-Speed Data
Processing in Signal Processing Systems
Figure 5 revealed that high-speed data processing using
quantum computing involves multiple stages, beginning
with the input signal, which can be raw data such as audio,
image, or frequency signals. Classical pre-processing may
then be applied to prepare the signal through techniques
like conditioning, noise filtering, and normalization,
depending on its quality. At the core lies the Quantum
Signal Processing Unit, where quantum algorithms such as
Quantum Fourier Transform (QFT) for frequency analysis,
Quantum Phase Estimation (QPE) for phase detection, and
quantum filters for noise reduction are employed. These
methods leverage quantum parallelism and superposition
to achieve significant speed and accuracy improvements.
After quantum processing, the quantum-enhanced output is
generated, providing faster and more precise results,
particularly for large datasets or complex tasks. Classical
post-processing can refine these results through error
correction, signal interpretation, or integration into existing
systems. The final output, which may include transformed
or noise-reduced signals, is ready for downstream
applications like communications, IoT devices, or real-
time analysis in radar systems.
6.6 Development of Quantum Signal Processing
Frameworks
The study also highlighted the need for developing
specialized frameworks for quantum signal processing to
facilitate the integration of quantum algorithms into
practical applications. While existing quantum
programming platforms like Qiskit and Cirq provided a
foundation for quantum algorithm implementation, they
are not yet optimized for signal processing tasks. The
results suggest that creating tailored quantum signal
processing languages and frameworks will be essential for
making quantum signal processing accessible to engineers
and practitioners in the field of EEE.
The study successfully demonstrated the potential of
quantum computing in enhancing high-speed signal
processing, providing clear benefits in specific tasks such
as frequency analysis, noise reduction, and anomaly
detection. While quantum algorithms showed impressive
results in simulations, the scalability of these algorithms
and their real-world applicability remain constrained by
hardware limitations. The adoption of hybrid quantum-
classical approaches and further development of quantum
signal processing frameworks are essential steps toward
overcoming these challenges and realizing the full
potential of quantum computing in EEE systems.
VII. FINDINGS
1. Quantum Algorithms Improve Signal Processing
Efficiency: The Quantum Fourier Transform (QFT) and
Quantum Phase Estimation (QPE) algorithms provided
notable improvements in speed and accuracy compared to
classical signal processing methods. QFT demonstrated
exponential speed-ups in frequency analysis tasks,
particularly in processing high-dimensional data, such as
images and audio signals (Ur Rasool et al., 2023). QPE
outperformed classical phase detection techniques by
offering higher precision in less time, making it ideal for
applications in communication systems requiring real-time
signal synchronization.
2. Hybrid Quantum-Classical Approach is Effective:
Due to current limitations in quantum hardware, a hybrid
approach, combining quantum and classical systems,
proved to be the most practical solution. Quantum
computing was used for specific tasks that benefit from
quantum speed-ups (e.g., frequency analysis and noise
reduction), while classical methods handled other
computations. This approach resulted in faster processing
times and enhanced accuracy, showcasing a pathway to
integrate quantum techniques into existing signal
processing systems without overhauling classical
infrastructure (Ajay et al., 2021).
3. Quantum Machine Learning Enhances Signal
Processing: Quantum machine learning algorithms,
particularly Quantum Support Vector Machines (QSVM)
and Quantum Neural Networks (QNN), showed significant
promise in tasks like noise filtering, feature extraction, and
anomaly detection. These algorithms performed faster
training and exhibited better generalization for high-
dimensional datasets, leading to improved accuracy in

real-time signal processing applications like radar systems
and IoT networks.
4. Hardware Limitations Affect Scalability: Despite the
promising results, current quantum hardware limitations—
such as qubit decoherence, noise, and connectivity—
hindered the scalability of the quantum algorithms. While
the QFT algorithm showed speed-ups in small-scale
simulations, larger datasets required substantial error
correction, limiting its real-time applicability. These issues
highlight the need for more advanced quantum processors
and error correction techniques to handle large-scale signal
processing tasks effectively (Ajay et al., 2021).
5. Practical Integration Challenges: The integration of
quantum computing into existing Electrical and Electronic
Engineering (EEE) systems posed several challenges.
Quantum signal processing algorithms require specialized
quantum programming languages, and compatibility with
classical systems remains an obstacle. A hybrid quantum-
classical model appears to be the most feasible solution for
now, although developing standard frameworks for such
integrations will be crucial for future widespread adoption.
While quantum computing shows great potential for
improving high-speed signal processing in EEE systems,
its full integration is still hindered by hardware limitations.
Hybrid approaches and ongoing advancements in quantum
technology will likely pave the way for more practical
applications in the near future.
VIII. RECOMMENDATIONS
1. Invest in Quantum Hardware Development: To fully
leverage the potential of quantum computing for signal
processing, it is recommended that significant investments
be made in the development of more stable, noise-
resistant, and scalable quantum hardware. Advances in
qubit coherence times, error correction methods, and qubit
connectivity will be essential for enabling real-time
quantum signal processing at a large scale.
2. Focus on Hybrid Quantum-Classical Systems: Given
the current limitations of quantum hardware, it is advisable
to pursue hybrid quantum-classical systems that combine
the strengths of both approaches. Quantum algorithms can
be utilized for specific tasks where they provide clear
advantages, such as frequency analysis and noise
reduction, while classical systems handle other
computational workloads. This strategy will enable the
integration of quantum computing into existing
infrastructures and allow for more efficient signal
processing in the short term.
3. Enhance Quantum Machine Learning (QML)
Integration: Quantum machine learning (QML)
algorithms, particularly Quantum Support Vector
Machines (QSVM) and Quantum Neural Networks
(QNN), have shown promise in improving signal
processing tasks such as anomaly detection and feature
extraction. Further research into optimizing these
algorithms for practical applications in real-time systems
like radar, telecommunications, and IoT is recommended.
Additionally, developing hybrid QML models that
integrate quantum and classical components could offer
practical benefits for industries requiring high-dimensional
data analysis.
4. Develop Standard Frameworks for Quantum Signal
Processing: The establishment of a standardized
framework for quantum signal processing will be crucial
for widespread adoption. This includes creating quantum
programming languages and development tools
specifically tailored for signal processing applications.
Collaboration between academia, industry, and quantum
hardware manufacturers will be essential for establishing
these standards and ensuring their compatibility with
existing signal processing systems.
5. Explore Quantum Error Correction Methods:
Quantum error correction is a critical area of research to
address the limitations of current quantum hardware.
Developing efficient error-correction algorithms and fault-
tolerant quantum computing techniques will be necessary
for scaling quantum algorithms to larger, more complex
signal processing tasks. Future research should focus on
creating error-resistant quantum hardware that can handle
real-time signal processing tasks reliably.
IX. LIMITATIONS
1. Quantum Hardware Constraints: One of the primary
limitations of this study is the current state of quantum
hardware. Noisy Intermediate-Scale Quantum (NISQ)
devices still face issues with qubit coherence times, error
rates, and limited connectivity, which affect the
performance and scalability of quantum algorithms. As a
result, real-time, large-scale quantum signal processing
remains impractical for most applications.
2. Limited Availability of Quantum Processing Power:
Although quantum computing shows great potential for
specific signal processing tasks, the availability of
quantum processors capable of handling complex, real-
time applications is still limited. The lack of sufficient
quantum resources restricts the ability to test and
implement large-scale quantum signal processing systems
in real-world environments.
3. Complexity of Hybrid Systems: While hybrid
quantum-classical systems offer a viable solution in the

short term, the integration of quantum algorithms with
classical systems introduces complexity in terms of system
architecture, programming, and real-time coordination.
Developing seamless hybrid systems that can efficiently
combine quantum and classical components remains a
challenge and requires further research in system
integration.
4. Software and Tooling Limitations: The lack of
dedicated quantum signal processing software and
programming languages hampers the widespread use of
quantum computing in signal processing applications.
While platforms like Qiskit and Cirq provide quantum
computing frameworks, they are primarily geared towards
general-purpose quantum algorithms rather than specific
signal processing tasks, limiting their utility for
practitioners in the field.
5. Data and Algorithm Constraints: While quantum
algorithms such as QFT and QPE demonstrated significant
improvements in specific signal processing tasks, their
application to larger datasets and more complex problems
still faces limitations. The computational overhead
associated with quantum operations, particularly when
applied to high-dimensional data, requires further
optimization to become feasible for real-time applications.
X. CONCLUSION
This study has explored the potential of quantum
computing to enhance high-speed signal processing in
Electrical and Electronic Engineering (EEE) systems,
identifying both the promising advantages and the current
challenges associated with its integration. Quantum
algorithms, such as Quantum Fourier Transform (QFT)
and Quantum Phase Estimation (QPE), demonstrated
significant improvements in speed and accuracy for
specific signal processing tasks, including frequency
analysis, noise reduction, and phase detection. These
results suggest that quantum computing could offer
substantial benefits in applications requiring rapid
processing of large datasets, such as telecommunications,
radar systems, and real-time image processing. However,
the study also revealed several limitations, particularly
with regard to quantum hardware. The current state of
Noisy Intermediate-Scale Quantum (NISQ) devices
presents challenges, including qubit coherence, error rates,
and limited scalability, which hinder the practical
application of quantum signal processing for large-scale,
real-time tasks. To address these limitations, the research
suggests a hybrid quantum-classical approach as a feasible
solution, enabling the integration of quantum algorithms
into existing systems while overcoming the constraints of
current hardware. Moreover, the study highlights the
growing importance of quantum machine learning (QML)
algorithms, which showed promise in enhancing signal
processing tasks like anomaly detection and feature
extraction. The combination of quantum computing with
classical systems in a hybrid architecture offers a pathway
toward improving signal processing in dynamic
environments, such as autonomous vehicles, wireless
communication, and IoT networks.
In conclusion, while quantum computing holds great
potential for revolutionizing signal processing in EEE
systems, its full realization will require continued
advancements in quantum hardware, error correction, and
algorithm optimization. The development of standardized
frameworks for quantum signal processing, alongside the
integration of quantum and classical systems, will be
critical in overcoming current limitations and facilitating
the widespread adoption of quantum computing in real-
world applications. As quantum technology progresses, its
integration into signal processing systems is expected to
play a pivotal role in meeting the demands of modern,
high-speed data processing tasks.
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