Farbod Khoshnoud, Marco B. Quadrelli, Enrique J. Galvez, Clarence Wilfred de Silva, Shayan Javaherian, Behnam Bahr, Maziar Ghazinejad, Anas Salah Eddin, Mohamed El-Hadedy
As the importance of quantum technologies is rapidly rising, e.g., in cyber security domain, further surge applications of such systems in various domains are expected in near term.In fact, the integration of quantum technologies with classical systems is inevitable in many disciplines, particularly as quantum devices are becoming more accessible.Among the various areas in which quantum technologies can potentially provide important complementary contributions, the area of Brain Computer Interface (BCI) has yet to be explored.The two areas of experimental photon quantum mechanics and BCI are brought together in this paper, for the first time.The applications of EEG signals to the control of mechanical systems, for example controlling drones remotely with brain wave signals, have been studied by various researchers.This paper presents the mechanism in which an EEG device is used to control the quantum properties in quantum experimental setups.In particular, a brain wave signal is used in changing the polarization of photons using a motorized half-waveplate in pre-built quantum entanglement and quantum cryptography experiments.This, in fact shows the mechanism on how quantum-BCI can be integrated as a system, before BCIbased control can be applied to hybrid classical-quantum systems, such as quantum experiments mounted on robotic platforms.The mechatronics of such systems is explained in this paper.This paper also outlines the necessary educational aspects of quantum related topics for students who have not traditionally exposed to quantum mechanics in their discipline, such as mechanical, aerospace, and electromechanical engineering students, which indeed is an essential step in preparing the next generation of engineering workforce for the fast-changing industry, the R&D sectors and academia.The quantum mechanics education and training steps in the mechatronics course and senior design projects are particularly promoted and discussed here.
As the importance of quantum technologies is rapidly rising, e.g., in cyber security domain, further surge applications of such systems in various domains are expected in near term.In fact, the integration of quantum technologies with classical systems is inevitable in many disciplines, particularly as quantum devices are becoming more accessible.Among the various areas in which quantum technologies can potentially provide important complementary contributions, the area of Brain Computer Interface (BCI) has yet to be explored.The two areas of experimental photon quantum mechanics and BCI are brought together in this paper, for the first time.The applications of EEG signals to the control of mechanical systems, for example controlling drones remotely with brain wave signals, have been studied by various researchers.This paper presents the mechanism in which an EEG device is used to control the quantum properties in quantum experimental setups.In particular, a brain wave signal is used in changing the polarization of photons using a motorized half-waveplate in pre-built quantum entanglement and quantum cryptography experiments.This, in fact shows the mechanism on how quantum-BCI can be integrated as a system, before BCIbased control can be applied to hybrid classical-quantum systems, such as quantum experiments mounted on robotic platforms.The mechatronics of such systems is explained in this paper.This paper also outlines the necessary educational aspects of quantum related topics for students who have not traditionally exposed to quantum mechanics in their discipline, such as mechanical, aerospace, and electromechanical engineering students, which indeed is an essential step in preparing the next generation of engineering workforce for the fast-changing industry, the R&D sectors and academia.The quantum mechanics education and training steps in the mechatronics course and senior design projects are particularly promoted and discussed here.
This chapter presents a quantum computing-based approach to study and harness neuronal correlates of mental activity for the development of Brain-Computer Interface (BCI) systems. It introduces the notion of a logic of the mind, where neurophysiological data are encoded as logical expressions representing mental activity. Effective logical expressions are likely to be extensive, involving dozens of variables. Large expressions require considerable computational power to be processed. This is problematic for BCI applications because they require fast reaction times to execute sequences of commands. Quantum computers hold much promise in terms of processing speed for some problems, including those involving logical expressions. Hence, we propose to use quantum computers to process the logic of the mind. The chapter begins with an introduction to BCI and the electroencephalogram, which is the neurophysiological signal that is normally used in BCI. Then, it briefly discusses how the EEG corresponds to mental states, followed by an introduction to the logic of the mind. After that, there is an overview of quantum computing, focusing on the basics deemed necessary to understand how it processes logical expressions. An example of a BCI system is presented. In a nutshell, the system reads the EEG and builds logical expressions, which are sent to a quantum computer to solve them. In turn, the system converts the results into sounds by means of a bespoke synthesiser. Essentially, the BCI here is a musical instrument controlled by the mind of the player. Our BCI is a proof-of-concept aimed at demonstrating how quantum computing may support the development of sophisticated BCI systems. The remaining of the chapter is devoted to technical and practical considerations on the limitations of current quantum computing hardware technology and scalability of the system.
Brain-computer interface (BCI) technology, as a key means of human-computer interaction, has a wide range of applications in the fields of neurorehabilitation, brain-control devices, and intelligent medicine. However, existing BCI systems suffer from high computational complexity, low decoding accuracy, and limited real-time performance during neural signal processing, which restricts their practical applications. In this study, a quantum-enhanced BCI optimization algorithm is proposed to address this technical bottleneck by combining the parallel computing capability and nonlinear optimization characteristics of quantum computing. The study constructs a quantum-classical hybrid computing architecture, uses quantum Fourier transform (QFT) to enhance the efficiency of time-frequency analysis of EEG signals, introduces quantum principal component analysis (QPCA) to optimize the dimensionality reduction of high-dimensional data, and enhances the generalization ability of motion intent classification by combining with quantum support vector machines (QSVMs), and verifies the effectiveness of the method in experiments. The results show that the method outperforms the traditional BCI algorithm in terms of decoding accuracy, computational efficiency and real-time performance, and the system end-to-end latency is controlled within 200ms, which meets the demands of online brain-computer interaction. This study provides theoretical support and experimental validation for the application of quantum computing in the field of brain-computer interface, which can be further expanded to closed-loop neuromodulation, intelligent prosthesis control and other application scenarios in the future, providing new ideas for the development of high-efficiency brain-computer interaction technology.
This paper explores the potential of quantum computing to revolutionize brain-computer interfaces (BCIs) and accelerate complex computations within the brain. We propose a novel architecture for a quantum-enhanced BCI, leveraging quantum algorithms to efficiently decode brain signals and translate them into optimized quantum operations. The core mechanism centers around a quantum-accelerated signal processing pipeline, aiming to significantly increase the speed and accuracy of neural control tasks. The paper details the design of the BCI system, outlining the quantum algorithms employed, the hardware components involved, and a preliminary analysis of the expected performance improvements. We conclude by highlighting the novelty and potential impact of this approach, emphasizing its promise for advancements in neuroscience, cognitive computing, and control systems.
Abstract Brain–computer interfaces (BCIs) can revolutionize how humans interact with technology, but several scientific and technological challenges must be addressed to realize their full potential. Recent developments in quantum‐based sensing methods offer promising solutions to some of these challenges. This review provides an overview of the progress, challenges, and prospects of BCIs research and discuss the feasibility of integrating quantum sensor technology in BCI systems. The applications of quantum sensing in BCIs research are reviewed and the solution based on quantum sensor technology to overcome some of the challenges associated with BCI systems is proposed. The potential of quantum sensor technology for the future development of BCIs is emphasized. Overall, this review highlights quantum sensor technology's significant potential for future development of BCI.
A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.
This research explores the potential of quantum computing to revolutionize brain-computer interfaces (BCIs) by providing a fundamentally different approach to simulating neuronal activity and information transfer. The core claim posits that quantum algorithms can accurately model the intricate quantum effects within neural networks, leading to more precise brain interface design and therapeutic interventions. This study will detail the challenges and opportunities inherent in harnessing quantum mechanics for BCI applications, focusing on the unique capabilities of quantum bits (qubits) and their potential to overcome limitations of classical computing. We will examine current research directions, including the development of specialized quantum algorithms for neural network modeling, and discuss the technical and theoretical hurdles that must be addressed before practical implementation becomes feasible.