Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness

summary

Video file (mp4)

The gist

The gist The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future lowenergy carbon IoT systems.

In short

The study tested three quantum neural network algorithms—QNN-P, QNN-Q, and QNN-H—on low-energy carbon IoT datasets for room occupancy detection and GPS tracking. Quantum Neural Network utilizing Pennylane (QNN-P) showed the highest accuracy across both datasets (0.95 for RODD and 0.94 for GPSD). These quantum models proved computationally efficient and robust against six different types of noise, suggesting they are promising for sustainable IoT systems.

Key concepts

Low-energy Carbon IoT Systems
These are smart devices designed to be extremely energy-efficient while operating in the Internet of Things (IoT) environment. Their main goal is to reduce carbon emissions by ensuring that the devices perform their tasks without wasting significant battery power, which is crucial for sustainable development.
Quantum Neural Networks (QNNs)
These are machine learning models inspired by quantum computing principles used here to solve data analysis problems. The paper tested three variations: QNN-P, QNN-Q, and hybrid models (QNN-H). These algorithms use quantum circuits to process data and make predictions, aiming for better performance than classical methods.
Noise Robustness Analysis
This part of the research checks how well the quantum algorithms perform when real-world errors or 'noise' are introduced into the system. The researchers tested six different noise models (like bitflip or amplitude damping). Finding that QNN-P remained accurate even under significant noise shows its reliability for practical, imperfect IoT deployments.
Room Occupancy Detection (RODD) and GPS Tracker (GPSD)
These are the two specific datasets used to test the quantum algorithms. RODD involves determining if a room is occupied or empty, while GPSD deals with tracking movement using a GPS device. These real-world applications serve as benchmarks to prove the accuracy of the proposed quantum models.

Terminology used across episodes

This episode discusses

The paper

Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness · Read on arXiv

Kshitij Dave, Nouhaila Innan, Bikash K. Behera, Shahid Mumtaz, Saif Al-Kuwari, Ahmed Farouk

Institute of Advanced Research Gandhinagar India University of Casablanca Morocco Bikash’s Quantum (OPC) Pvt Ltd Nottingham Trent University Qatar Center for Quantum Computing Hamad Bin Khalifa University Hurghada University

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance.

DOI: 10.1109/JIOT.2025.3540949

Transcript

Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: I'm Kai.

Mira: And I'm Mira, here with Lev.

Kai: We're looking at the paper: "Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms".

Mira: Here's the gist of the paper. The gist The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future lowenergy carbon IoT systems.

Kai: Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance The application encompasses analyzing various agriculture domains

2: , cloud computing

3: , transportation systems

4: , environment monitoring

5: and many more The Internet of Things (IoT) has application domains that enable technologies on energy efficiency techniques for Wireless Sensor Networks (WSNs) and identify gaps for future research on energy preservation measures

1: IIoT-based applications involve operational efficiency, predictive maintenance, and data-driven decision-making, which ultimately improve productivity and cost efficiency in various industries, including manufacturing, energy, and transportation

8: Optimization problems such as energy management and carbon reduction are difficult to handle using traditional approaches However, Quantum Computing (QC), particularly Quantum Machine Learning (QML) algorithms, have the potential to overcome these limitations by providing new techniques to reduce carbon footprints and increase energy efficiency in IIoT systems

14: ,

15: A technique inspired by QC is introduced to optimize temporal space in real-time IIoT applications, focusing on accuracy, energy efficiency, and data similarity analysis

16: A novel GAN-QEHO algorithm, which combines adversarial generative networks (GAN) with the optimization of the quantum elephant herd (QEHO), is presented to improve mobile edge computing (MEC) in big data environments enabled by IIoT

17: Quantum algorithms, such as annealing, genetic algorithms, and particle swarm optimization, solve resource allocation, network routing, and energy efficiency challenges in IIoT networks

18: Proposed Quantum Algorithms The study introduces three quantum algorithms to optimize low-energy carbon IoT systems: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H) These algorithms are applied to two low-energy carbon IoT datasets—room occupancy detection (RODD) and GPS tracker (GPSD) For the RODD dataset, QNNP achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80 Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNNQ 0.74 The framework involves minimizing the loss function L(ˆy i, y i) = min θ i L(f(X; θ i), y i), where yˆ i = f(X; θ i) represents the predicted output The quantum circuit is represented by ψ(θ)⟩ = Yn i=1 U(θi)ψ0⟩, where U(θ) is the parameterized quantum circuit Model Architectures and Optimization The methodology details three distinct QNN architectures: 1.

Mira: QNN-P utilizes angle embedding for feature encoding, followed by RY and RX rotation gates and entanglement operations The data embedding into the quantum state is achieved through angle embedding, defined as Ψdata(x)⟩ = ∏ i=1 Om i=1 RY (xi)0⟩ The loss function used is binary cross entropy or mean square error in classification 2.

Kai: QNN-Q employs a ZFeatureMap for data embedding and a RealAmplitudes ansatz to construct the quantum circuit The feature map representation of n qubits, r repetitions, and xi is given by ϕ(x) = ∏ k=1 Yr k=1 nO−1 i=0 H!

Paper summary: Kai: So, to recap, this paper explores how quantum algorithms can solve the scalability and real-time processing limitations that classical algorithms face in low-energy carbon IoT systems.

Mira: The main thesis is that quantum computation offers a way to enhance both the performance and the sustainability of these systems by providing faster computations and improved optimization >

Kai: They introduced three specific quantum algorithms—QNN-P, QNN-Q, and QNN-H—and applied them to room occupancy detection and GPS tracker datasets.

Lev: The results show QNN-P achieving zero point nine five accuracy on room occupancy detection and zero point nine four on the GPS tracker data > <ref:2503.00888#pg1>

Mira: And they also explored the robustness of these models against six different noise models, showing that QNN-P was quite resilient compared to the other two architectures >

Kai: The paper essentially demonstrates that these quantum approaches can be more efficient and scalable in noisy environments, which is a key finding for future low-energy carbon IoT systems >

Lev: It’s interesting because it directly addresses resource allocation and energy efficiency challenges in IIoT networks using quantum algorithms like annealing or genetic algorithms >

Kai: So, the paper shows that designing these quantum and hybrid architectures with minimal resource requirements aligns well with the principles of sustainable computing and helps reduce the computational load on traditional systems >

Conclusion: Mira: So, looking at the title, "Optimizing Low-Energy Carbon IIoT Systems with Quantum Algorithms: Performance Evaluation and Noise Robustness" it’s really about making these systems more carbon-efficient by using quantum techniques and checking how well they hold up when things get noisy >

Kai: And the authors are Kshitij Dave, Nouhaila Innan, Bikash K. Behera, Shahid Mumtaz, Saif Al-Kuwari and Ahmed Farouk. The main implication is that if we can build these quantum models to be robust against noise like they showed with QNN-P >

Lev: Then it opens up possibilities for things like energy optimization or traffic management in IIoT networks because these systems would run much lighter than the classical counterparts >

Kai: It suggests that using quantum models could significantly reduce the carbon footprint of IoT applications because of the efficiency gains and how well they generalize across different datasets >

Mira: It's about moving towards infrastructures that are fundamentally more sustainable by making sure device performance stays optimal even when those systems encounter real-world noise >

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