The fastest growing Internet of Things (IoT) application field necessitates efficient signal processing frameworks with high computational portions that have an incomparably low power consumption, especially among embedded devices subject to very limited resources. In this paper, we present a hybrid quantum-inspired signal processing paradigm combining both theoretical aspects of a quantum-inspired computation we have used along our theoretical aspects of classical DSP, e.g., moving between amplitude encoding, quantum superposition, quantum optimization of rotation gates, adaptive FIR/IIR filtering and discrete wavelet transforms, and fixed-point optimisation. Such theoretical combination solves some of the most important signal processing issues that involve lowered computational complexity, enhanced convergence characteristics of adaptive filters, and heightened noise resilience in non-stationary settings. The hardware-algorithm co-design in the framework uses a combination of fixed-point arithmetic, loop unrolling and memory blocking to facilitate successful real-time deployment on an ARM Cortex-M4 microcontroller. Up to 43 percent reduction of energy, 31 percent improvement on execution speed and 25 percent better in the classification rate compared to baseline DSP methods are also shown through experimental validation. Demonstration of the generalizability of the strategy by utilizing it in the area of biomedical signal denoising, environmental sound analysis, and structural vibration monitoring creates a scalable methodology of realizing elegant signal processing theory under resource-limited embedded IoT devices.
The fastest growing Internet of Things (IoT) application field necessitates efficient signal processing frameworks with high computational portions that have an incomparably low power consumption, especially among embedded devices subject to very limited resources. In this paper, we present a hybrid quantum-inspired signal processing paradigm combining both theoretical aspects of a quantum-inspired computation we have used along our theoretical aspects of classical DSP, e.g., moving between amplitude encoding, quantum superposition, quantum optimization of rotation gates, adaptive FIR/IIR filtering and discrete wavelet transforms, and fixed-point optimisation. Such theoretical combination solves some of the most important signal processing issues that involve lowered computational complexity, enhanced convergence characteristics of adaptive filters, and heightened noise resilience in non-stationary settings. The hardware-algorithm co-design in the framework uses a combination of fixed-point arithmetic, loop unrolling and memory blocking to facilitate successful real-time deployment on an ARM Cortex-M4 microcontroller. Up to 43 percent reduction of energy, 31 percent improvement on execution speed and 25 percent better in the classification rate compared to baseline DSP methods are also shown through experimental validation. Demonstration of the generalizability of the strategy by utilizing it in the area of biomedical signal denoising, environmental sound analysis, and structural vibration monitoring creates a scalable methodology of realizing elegant signal processing theory under resource-limited embedded IoT devices.
The next-generation wireless sensor networks (WSN) applications related to the 6G communication, smart structural monitoring, and Internet of Things (IoT) require very efficient, low-latency, and energy-efficient signal processing patterns. The current methods of digital signal processing (DSP), although having the capabilities of accomplishing well in terms of resources, fail to achieve the set parameters involving computation and power consumption and resource requirements of autonomous battery-powered sensor nodes that are distributed. In solving this important problem, this project presents a quantum-inspired framework of signal processing (QISP) that implements the key concepts underlying quantum computation, in terms of superposition, amplitude amplification, and entanglement-inspired correlation modeling, into classical embedded systems. The three key algorithmic elements in the proposed QISP framework include the QISP-Denoise, which combines the concepts of amplitude amplification using Grover-like algorithm to clarify the signals; QISP-Transform, which employs the use of sparse-based compressive sensing to reduce the dimensionality of the data, and finally the QISP-Detect which employs quantum walk based filters to detect anomalies and events. They are run on a resource-bounded ARM Cortex-M-style microcontroller simulator, and tested with real biomedical and environmental data sets. The framework brings about impressive gain in the denoising accuracy, compression efficacy, and localization of anomalies with less amount of efficiency, low computation latency and power consumption. This experimentation shows that the QISP framework out buying a ticket online performs traditional DSP and AI-based approaches in terms of signal-to-noise ratio (SNR), mean squared error (MSE), and classification accuracy, all within an energy envelope that can be deployed long-term in a WSN. In addition, QISP is very insensitive to noise and sensing environment variability, which makes it applicable in the brown field fields of dynamic real time sensing like in wearable medical monitoring, smart grids, and structural health monitoring. Bringing the quantum-inspired paradigm into the system in the form of quantum-inspired accelerators is a scalable, affordable strategy to increasing intelligence in embedded edge devices. Such work can be seen as ground zero to the practical application of quantum-classical mixed models to intelligent sensing in resource-limited environments and bring quantum theory and the next generation of wireless sensors together.
The blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.
The impending threat posed by quantum computing to classical cryptographic systems has accelerated the need for post-quantum cryptographic (PQC) solutions, particularly within the rapidly expanding domain of the Internet of Things (IoT). As IoT devices operate under severe constraints in terms of memory, processing power, and energy, integrating quantum-resilient algorithms presents both a critical necessity and a formidable challenge. This chapter provides a comprehensive study of PQC implementations on constrained IoT platforms, exploring algorithmic performance, memory consumption, execution time, and energy efficiency across various benchmarking environments. It offers a comparative analysis of bare-metal and RTOSbased deployments, evaluates the trade-offs between security and real-time responsiveness, and investigates power-aware scheduling and duty cycling strategies for secure task execution. Case studies involving smart sensors, medical wearables, and industrial nodes highlight the practical implications and optimizations required for effective PQC integration. Emphasis was placed on hardware-software co-design, library selection, and communication stack alignment to ensure future-ready, quantum-secure IoT deployments. This chapter bridges theoretical cryptographic resilience and applied system-level engineering, laying a foundational framework for scalable and secure post-quantum IoT ecosystems.
The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
Innovative optimization methods that balance power efficiency, computational complexity, and performance are needed to meet IoT device demand for low-power, high-performance VLSI circuits. Combining quantum computing with traditional optimization methods is necessary due to the exponential development of circuit complexity. This study optimizes low-power VLSI circuits using a hybrid quantum-classical framework that uses quantum circuit optimization, quantum annealing, and hybrid variational methods. The framework reduces gate count, power consumption, and latency by combining quantum-assisted logic optimization with classical heuristics. Simulations and experiments prove the framework can improve energy-efficient hardware designs. Quantum gate reduction (QGR) and quantum state encoding (QSE) optimize Boolean logic, while quantum annealing refines transistor location and reduces leakage power. Classical post-processing methods like heuristic refinement and logic gate remapping fine-tune quantum-optimized circuits for practical application. Compared to classical approaches, the hybrid architecture reduces gate count by 35%, saves 28% power, and improves latency by 25%. Performance validation using IBM Quantum Experience and D-Wave devices shows that quantum-assisted VLSI optimization techniques are possible. Further research on fault-tolerant quantum computing, hybrid co-design, and real-world CMOS integration for next-generation semiconductor fabrication are planned. This study shows how quantum-classical hybrid techniques can alter electronic design automation.
The continuous growth in computational demands has led to the exploration of innovative technologies for enhancing circuit efficiency. Quantum-inspired algorithms have garnered attention due to their potential to improve optimization, processing, and energy consumption in conventional circuit technologies. Traditional circuit designs, primarily based on classical computation, face limitations in handling large-scale problems due to inefficient algorithms and hardware constraints. These challenges prompt the need for alternative solutions capable of providing scalable, high-performance computation in complex environments. This research proposes integrating quantum- inspired algorithms into circuit technologies to address these challenges. Quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, offer substantial benefits over classical counterparts, especially in solving NP-hard problems, simulating quantum systems, and optimizing complex functions. The integration of such algorithms into circuit design can potentially reduce computational complexity, improve data throughput, and optimize energy efficiency, offering a more sustainable approach in circuit development. The study investigates the adaptation of quantum-inspired algorithms into field-effect transistors (FETs) and other circuit components, focusing on optimizing power consumption and operational speed. A hybrid approach combining classical circuit elements with quantum-inspired strategies is implemented to evaluate its impact on both performance and scalability. The results show a notable reduction in energy consumption and improvement in processing speed, validating the promise of quantum-inspired solutions in enhancing computational efficiency.