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 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 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.
Networks built to sixth-generation (6G) targets will have to hold sub-millisecond latency, push spectral efficiency past what today's massive MIMO deployments achieve, and manage resources across highly heterogeneous nodes with little human supervision. Those targets place a hard demand on the optimizer. Channel estimation, adaptive modulation, beamforming and decoding all involve searching large, non-convex parameter spaces, and the search has to finish inside a coherence interval that may last only a few hundred microseconds. This paper surveys quantum-inspired signal processing (QISP), a family of classical algorithms that borrow structure from quantum computation, including amplitude-weighted superposition sampling, quantum-walk-style exploration, Ising-model formulations and tensor-network compression, but that run entirely on conventional processors. We group the literature under four headings: algorithmic foundations, communication-system architectures, optimization strategies, and implementation constraints. We then propose a reference architecture in which a quantum-inspired optimization engine sits beside a conventional OFDM/MIMO transmit-receive chain and reconfigures modulation, beam-selection and decoding parameters from channel-state feedback. An illustrative simulation grounds the discussion: a representative QISP optimizer is compared against a conventional adaptive baseline over a 4×4 MIMO, QPSK, AWGN-channel OFDM link at signal-to-noise ratios (SNR) from 0 to 20 dB. These numbers demonstrate methodology and are not measurements taken from physical hardware. Within that caveat, the QISP optimizer achieves a lower biterror rate (BER) at low and moderate SNR, and the throughput and latency trends are consistent with the parameter-search efficiency reported in the wider literature. The paper concludes with open problems such as scalability, algorithmic complexity, explainability and standardized benchmarking, and research directions on hybrid quantum-classical processors, reconfigurable intelligent surfaces (RIS), semantic communications, and edge-native QISP inference.
On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user.In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework.Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration.In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance.The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity.Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc.Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers.The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption.This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security.As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field's capability.
Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. This work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. The quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. These results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
Artificial Intelligence techniques have significantly improved medical image diagnostics in Wireless Sensor Network-assisted Internet of Things healthcare systems by enabling real-time acquisition, transmission, and analysis of MRI, CT, and X-ray images. However, the increasing volume of high-dimensional medical data creates major challenges related to computational complexity, energy consumption, and image quality preservation. Traditional convolutional neural networks provide high diagnostic accuracy but require extensive computational resources, making them less suitable for resource-constrained IoT healthcare environments. To address these limitations, Quantum Convolutional Neural Networks have emerged as promising alternatives by utilizing quantum principles such as superposition and entanglement to improve computational efficiency while reducing resource requirements. These models demonstrate lower computational complexity, improved robustness, and enhanced energy efficiency compared to conventional CNN architectures. Attention-based models further enhance diagnostic performance by enabling selective feature extraction and focusing on clinically relevant regions within medical images. Quantized and self-attention frameworks significantly reduce computational cost and energy consumption, making them suitable for IoT-based healthcare applications. Hybrid quantum-classical architectures integrating attention mechanisms combine the strengths of quantum computing and deep learning to improve both accuracy and efficiency. Overall, energy-efficient QCNNs with attention-based learning provide a promising framework for scalable, intelligent, and high-quality medical image diagnostics in future IoT healthcare systems.