The fusion of quantum technology with classical digital signal processing opens up promising avenues for high precision computational system. In this work we propose a novel approach for the design of a quantum enhanced digital differentiator by leveraging the unique quantum properties of quantum dots-based spin qubits in conjunction with Kalman filtering. Quantum dots offer discrete energy levels, high sensitivity and extended coherence time which are utilized to improve the resolution and stability of the differentiation process. To address inherent quantum noise and uncertainties in the signal, we integrate a Kalman filter that adaptively estimates and refines the differentiated signal in the real time. This hybrid quantum classical framework is theoretically developed and validate through comprehensive numerical simulations. The results demonstrate enhanced accuracy, noise resilience and computational efficiency, making the proposed method suitable for advanced application in control system, precision measurement, and quantum-assisted processing.
The fusion of quantum technology with classical digital signal processing opens up promising avenues for high precision computational system. In this work we propose a novel approach for the design of a quantum enhanced digital differentiator by leveraging the unique quantum properties of quantum dots-based spin qubits in conjunction with Kalman filtering. Quantum dots offer discrete energy levels, high sensitivity and extended coherence time which are utilized to improve the resolution and stability of the differentiation process. To address inherent quantum noise and uncertainties in the signal, we integrate a Kalman filter that adaptively estimates and refines the differentiated signal in the real time. This hybrid quantum classical framework is theoretically developed and validate through comprehensive numerical simulations. The results demonstrate enhanced accuracy, noise resilience and computational efficiency, making the proposed method suitable for advanced application in control system, precision measurement, and quantum-assisted processing.
The fusion of quantum computing and robotics represents a frontier in computational intelligence, promising to overcome the limitations of classical sensor fusion methods in precision, scalability, and real-time adaptability. This study proposes a hybrid quantum–classical framework designed to enhance the integration and synchronization of LiDAR, visual, and inertial sensor data, ultimately improving robotic perception and spatial awareness in complex environments. The model employs quantum-assisted optimization techniques to handle high-dimensional uncertainty, noise propagation, and data redundancy challenges inherent in multi-sensor processing. By leveraging variational quantum circuits and classical machine learning optimizers, the hybrid model achieves efficient data correlation and error minimization during sensor alignment. Benchmark experiments were conducted to evaluate the efficiency and precision of the proposed quantum-assisted sensor fusion system relative to conventional data integration algorithms. The findings reveal that hybrid quantum–classical systems yield substantial improvements in localization accuracy, temporal synchronization, and resilience to sensor noise, while maintaining computational feasibility within near-term quantum devices. This work highlights the potential of quantum-enhanced perception frameworks to accelerate the next generation of autonomous robotics, providing a foundation for adaptive control, intelligent navigation, and mission-critical decision-making under uncertainty.
The increasing demand for high-precision real-time data processing in satellite clusters requires efficient algorithms to manage inherent uncertainties in space-based systems. We propose an innovative framework that integrates Quantum Neural Network (QNN) architecture into Kalman filtering processes, specifically tailored for Low Earth Orbit satellite clusters. Our quantum computing-based approach achieves a significant improvement in prediction accuracy and a reduction in mean absolute error compared to classical Kalman filtering techniques. These advances significantly improve computational efficiency and error handling, making the method highly scalable under varying noise levels. A comparative analysis demonstrates the superior performance of the Quantum Kalman Filter in processing speed, resource utilization, and prediction accuracy, all evaluated within the constraints of LEO satellite constellations. These findings highlight the potential of quantum computing to optimize data processing strategies for future missions, including deep space explorations.
Quantum algorithms offer significant speed-ups over their classical counterparts in various applications. In this paper, we develop quantum algorithms for the Kalman filter widely used in classical control engineering using the block encoding method. The entire calculation process is achieved by performing matrix operations on Hamiltonians based on the block encoding framework, including addition, multiplication, and inversion, which can be completed in a unified framework compared to previous quantum algorithms for solving control problems. We demonstrate that the quantum algorithm exponentially accelerates the computation of the Kalman filter compared to traditional methods. The time complexity can be reduced from O(n3) to O(κpolylog(n/ε)log(1/ε′)), where n represents the matrix dimension, κ denotes the condition number for the matrix to be inverted, ε indicates desired precision in block encoding, ε′ signifies desired precision in matrix inversion. This paper provides a comprehensive quantum solution for implementing the Kalman filter and serves as an attempt to broaden the scope of quantum computation applications. Finally, we present an illustrative example implemented in Qiskit (a Python-based open-source toolkit) as a proof-of-concept.
The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.
The extraction of weak signals plays a crucial role in quantum precision measurement, where the estimation results are often limited by low signal-to-noise ratios. Here, we demonstrate a parameter-estimation framework based on the adaptive extended Kalman filter for dynamic magnetic-field estimation in quantum systems using spin-noise measurements -- a challenging regime characterized by weak signals. By modeling the magnetic field as an unknown parameter, the proposed approach alleviates model dependence in state estimation. Furthermore, by introducing an adaptive algorithm with real-time noise estimation, our method overcomes the measurement noise intensity constraints of conventional extended Kalman filtering and enhances its practical applicability. Numerical simulations covering three representative magnetic-field dynamics validate the capability of the proposed framework, while experimental results demonstrate successful tracking of a seismo-magnetic-like signal beyond the intrinsic sensitivity of conventional spin-noise spectroscopy.
In this work we present an algorithm to perform algorithmic differentiation in the context of quantum computing. We present two versions of the algorithm, one which is fully quantum and one which employees a classical step (hybrid approach). Since the implementation of elementary functions is already possible on quantum computers, the scheme that we propose can be easily applied. Moreover, since some steps (such as the CNOT operator) can (or will be) faster on a quantum computer than on a classical one, our procedure may ultimately emonstrate that quantum algorithmic differentiation has an advantage relative to its classical counterpart.