As a step in the architectural design of a quantum processing or sensing system with control and signaling, an attempt is made at putting in parallel functional properties of the random flows between neurons through electrical synapses, and quantum particle flows inside a quantum processing system mimicking biological processes. Based on a simplified dynamic electrical synapse model, a quantum synapse circuit design is proposed. This is extended to the case of bidirectional flows through a synapse, highlighting the possible role of quantum synapse circuits as highly parallel controlled interfaces crucial in sensing and sensor fusion systems. A short status of the quantum simulation is provided.
As a step in the architectural design of a quantum processing or sensing system with control and signaling, an attempt is made at putting in parallel functional properties of the random flows between neurons through electrical synapses, and quantum particle flows inside a quantum processing system mimicking biological processes. Based on a simplified dynamic electrical synapse model, a quantum synapse circuit design is proposed. This is extended to the case of bidirectional flows through a synapse, highlighting the possible role of quantum synapse circuits as highly parallel controlled interfaces crucial in sensing and sensor fusion systems. A short status of the quantum simulation is provided.
Earlier reports have described a quantum computing architecture, in which key elements are derived from control functions in biology. In this further continuing research, focus is on the signaling and control of a flow of qubits in that architecture, mimicking synapse signals and neurological controls. After a short description of that architecture, and of quantum sensing elements, it is first shown how the coloring of quantum particle flows, implemented as in mathematical colored algebras, can reduce decoherence and enhance the decidability of quantum processing elements. Next, after reviewing specific human biology functions, and exploiting experimental results on excitation modes in live animals, it is shown how to achieve separation of the quantum control & signaling signals. Technologies and designs from particle physics are discussed as well as open research issues towards a realization of a quantum computing architecture with decidable signaling.
This paper proposes a novel quantum computing architecture inspired by the structure and function of biological neural networks. The core claim is to enhance quantum computing efficiency and fault tolerance by leveraging the inherent properties of biological systems. The mechanism involves modeling quantum bits (qubits) using biologically inspired neuron and synapse models, mimicking their interactions, and utilizing the self-organizing capabilities of neural networks for quantum computation. Unlike traditional quantum computing architectures rooted in classical physics, this approach aims to capitalize on the flexibility and adaptability found within biological systems. The architecture explores concepts such as quantum entanglement and superposition through simulated neural network dynamics, offering a potentially more robust and scalable solution for quantum processing. This work lays the foundation for a biologically-inspired quantum computing paradigm.
A probabilistic model of Synapse is proposed. In concrete, synapse is proposed as a Bayesian probability that requires the quantum probabilities for both: the interaction of Calcium 2+ in axon area, and the releasing of neurotransmitters. In this way, all available interactions that are carry out at axon zone, are dictated by quantum events. Once neurotransmitters are released from synaptic vesicles, their dynamics might be dictated by quantum probabilities based in a Hamiltonian. The diverse scenarios and their respective distribution of probabilities are simulated. From proposed theory, it is suggested that synapse would have a notable dependence on the accumulation of electric charges before and after the pass of neurotransmitters from a neuron to adjacent one. From the results, it is possible to argue that synapse is more than a biological phenomenon, a multidisciplinary event, that emphasizes the importance of transporting of electric charges with a potential dependence on probabilities, more than trivial determinism.
This paper proposes a novel quantum computing model that leverages the dynamics of neuronal behavior as its foundational building blocks. Unlike conventional quantum computing approaches reliant on purely classical qubits, this model utilizes quantum systems designed to mimic the dynamic changes observed in biological neural networks, specifically at the level of synaptic connections. The core principle involves employing quantum superposition and interference to construct quantum bits (qubits) representing the state of these simulated synapses. Algorithm execution is achieved through precise manipulation, or "quantum tuning," of these qubits. The proposed architecture represents a biologically-inspired paradigm shift in quantum computation, potentially offering advantages in terms of computational efficiency and resilience, particularly for problems exhibiting inherent biological complexity. This work outlines the theoretical framework, key equations, and the fundamental principles driving this new approach to quantum information processing.
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper.The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results.The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse.A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
This paper proposes a novel neuron model leveraging the principles of quantum state superposition. Traditional neuron models, based on classical probability, struggle to accurately represent the complex information processing within biological neurons. This model introduces a fundamentally different approach by modeling synaptic connections as quantum states in superposition. The propagation of neural signals is then governed by the quantum mechanical interaction of these superimposed states. We define a mathematical framework incorporating key concepts from quantum mechanics, including superposition, entanglement, and quantum measurement, to describe the dynamics of this model. The core of the model lies in the equation: $ \frac{d| \Psi(t) \rangle}{dt} = \mathcal{H} | \Psi(t) \rangle $ where ∣Ψ(t)⟩ represents the quantum state of the neuron at time *t*, and H is the Hamiltonian operator describing the neuron's dynamics. We explore scenarios involving multiple inputs and their influence on the superposition state. The model demonstrates the potential for enhanced information processing compared to conventional models, particularly in scenarios requiring parallel computation and robust information storage. Furthermore, we discuss the implications of this approach for understanding and potentially mimicking biological neural computation. The model presented offers a theoretical foundation for exploring quantum computation within biological systems.