Aiding paralyzed people through using technology to transmit signals from brain to paralized part of a body has been a matter of great interest in recent times. Classical approaches in this regard still experience several limitations and sometimes become hazardous to living bodies. Besides, existing literature points out that there are many nerve signals that are not amenable to the classical approaches, however, can be amenable to quantum approaches. By addressing these two points, we propose a new system to propagate signal of a damaged nerve using quantum networking. We name our proposed system Q-Nerve. Q-Nerve exploits quantum network based artificial connection between brain and other organs to bypass a damaged nerve. Subsequently, we propose a more sophisticated version of Q-Nerve that aims to exploiting a synergy between the ability of quantum computing to accumulate neural signal and the ability of quantum networking to pass the signal instantaneously. Further, we extend the proposed system for other brain and nerve related problems that require numerous logical computations.
Aiding paralyzed people through using technology to transmit signals from brain to paralized part of a body has been a matter of great interest in recent times. Classical approaches in this regard still experience several limitations and sometimes become hazardous to living bodies. Besides, existing literature points out that there are many nerve signals that are not amenable to the classical approaches, however, can be amenable to quantum approaches. By addressing these two points, we propose a new system to propagate signal of a damaged nerve using quantum networking. We name our proposed system Q-Nerve. Q-Nerve exploits quantum network based artificial connection between brain and other organs to bypass a damaged nerve. Subsequently, we propose a more sophisticated version of Q-Nerve that aims to exploiting a synergy between the ability of quantum computing to accumulate neural signal and the ability of quantum networking to pass the signal instantaneously. Further, we extend the proposed system for other brain and nerve related problems that require numerous logical computations.
The tantalizing hypothesis that quantum phenomena underpin the brain’s remarkable computational abilities has sparked intense interdisciplinary interest. This study delves into quantum neuroscience by computationally exploring quantum coherence in neural systems, aiming to uncover whether quantum effects enhance information processing. We developed a sophisticated model simulating a 1,000-qubit neural network, with each qubit representing a neuron entangled under biologically relevant conditions (310 K, 0.15 µs coherence time). Using IBM’s Qiskit framework, we tested signal propagation efficiency, latency, and coherence duration across four conditions: quantum models with full, moderate, and high decoherence, and a classical benchmark. Our results reveal a striking 19.4% improvement in signal propagation efficiency in the full-coherence quantum model (95.8% ± 2.9%) compared to the classical model (76.4% ± 5.1%; p < 0.001). Latency was reduced by 31%, with the quantum model achieving 0.68 µs versus 0.98 µs for the classical model. Coherence persisted for up to 1.5 µs, sufficient for short-range neural signaling. Extensive sensitivity analyses, varying temperature (300–325 K), noise (0.01–0.12 µs^-1), and network size (500–1,500 qubits), confirmed robustness, with efficiency remaining above 90% under moderate perturbations. These findings suggest quantum coherence could complement classical neural mechanisms, potentially enhancing processes like sensory integration or consciousness. However, biological complexity, including biochemical interactions, warrants further exploration. We advocate for experimental validation using advanced quantum sensors, such as nitrogen-vacancy centers, to detect coherence in neural tissue. This study bridges quantum physics and neuroscience, offering a robust computational framework to probe the brain’s quantum potential and inspiring future interdisciplinary research into cognition’s mechanistic underpinnings.
The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computation applies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Neural Network (QNN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It is argued that the study of QNN may explain the brain functionality in a better way and create new systems for information processing including solving some classically intractable problems. In this paper we have given an introductory representation of quantum artificial neural network to show how it can be modelled on the basis of double-slit experiment. Also an attempt is made to show the quantum mechanical representation of a classical neuron to implement Hadamard transformation.
This paper proposes a novel approach to information processing by leveraging quantum entanglement within a neural network architecture. The core idea centers on encoding information within entangled quantum bits (qubits) and utilizing their correlated states for enhanced computational capabilities. Traditional neural networks rely on classical bits, limiting their potential for parallel processing and complex pattern recognition. By employing quantum entanglement, we aim to overcome these limitations, creating a network that can perform computations in a fundamentally different way. The proposed network structure consists of interconnected qubits, where entanglement facilitates the instantaneous transfer of information, theoretically bypassing the constraints of signal propagation speed. This architecture promises significant improvements in information encoding density, parallel processing efficiency, and potentially, the ability to solve problems intractable for classical neural networks. The theoretical framework presented here lays the groundwork for future research into quantum neural networks and their potential applications across diverse fields. The key mathematical concepts underpinning this work are presented below.
We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.
The human brain acts as a very efficient parallel classical-quantum communications system relating responses to a multitude of input signals from sensors. However, the mechanisms of the quantum communication channel in the brain remain a mystery. Since matter and energy cannot be teleported, in the sense of transferring from one place to another without passing through intermediate locations, teleportation of quantum states, as the ultimate structure of objects, is possible. Only the structure, information about conformation state, is teleported - the matter stays at the source side and has to be already present at the final location. From this point of view, we consider a new type of network in the brain, based on a unity of classical and quantum neural-astrocyte networks. Matter and energy (classical channel) and classical quantum information travel through neurons, while through the astrocyte-neuron loop travel classical quantum and non-classical quantum information. The new type of network is a synergetic one with quite new energy-information properties compared to classical neuron networks.
Recently significant effort has been invested in studying commonalities of human brain operation and advanced algorithms for machine learning to answer the question: Can the learning mechanisms, identified in the operation of the brain, be mimicked in artificial neural networks to enhance the learning efficiency with simultaneous reduction in complexity and power consumption. At the same time, machine learning algorithms, on their own, become increasingly complex, resulting in complex neural networks. To speed up the machine learning algorithms, research on 7G networks will be looking for new computing technologies, like quantum (q-) computing (QC), and new models for complex networks that will enable us to efficiently control/optimize the processes run on them. In this paper, under the umbrella of well-established complex networks theory, we provide a unified presentation of how quantum computing, implemented on near-future computers, can enable solving various problems in the above disciplines, otherwise difficult to solve by using classical (c-) approaches. The emphasis is on the commonalities in QC applications and modeling for the different systems listed above. For 7G network designers, the survey is expected to provide an insight into how much the research results in natural, QC based sciences can be integrated into new network paradigms to support above initiatives.