We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum Hamiltonian and modulated by a synaptic-efficacy feedback implementing activity-dependent changes in the collective time scale. The framework links collective quantum many-body modes and collective-state structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives long-lived operating regimes, activity-dependent oscillation periods, and size-dependent robustness that position LMG-based architectures as promising blueprints for bio-inspired quantum brains on future quantum hardware.
We present a biologically inspired quantum neural network that encodes neuronal populations as fully connected qubits governed by the Lipkin-Meshkov-Glick (LMG) quantum Hamiltonian and modulated by a synaptic-efficacy feedback implementing activity-dependent changes in the collective time scale. The framework links collective quantum many-body modes and collective-state structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives long-lived operating regimes, activity-dependent oscillation periods, and size-dependent robustness that position LMG-based architectures as promising blueprints for bio-inspired quantum brains on future quantum hardware.
This paper proposes a novel architecture for quantum neural networks (QNNs) inspired by biological neural networks, specifically focusing on adaptive learning capabilities. The core idea is to integrate biological mechanisms such as synaptic plasticity and long-range dependencies into the quantum learning process. The traditional approach to QNN training often relies on gradient-based methods, which can be inefficient and susceptible to local minima. This work introduces a bio-inspired approach that leverages the inherent parallelism and quantum superposition of QNNs to achieve more robust and adaptive learning. We formulate the learning process using a modified Hebbian learning rule combined with a quantum measurement process. The key innovation lies in dynamically adjusting the quantum gates based on the network's internal state, mimicking the neural network's ability to adapt to changing environments. The resulting QNN architecture exhibits enhanced learning speed and improved generalization performance compared to conventional QNNs. The theoretical framework is presented, and the underlying mathematical equations are detailed, providing a robust foundation for further research and development in this field.
Abstract Motivated by recent advances in neuroscience, in this work, we explore the emergent behaviour of quantum systems with a dynamical biologically-inspired qubits interaction. We use a minimal model of two interacting qubits with an activity-dependent dynamic interplay as in classical dynamic synapses that induces the so-called synaptic depression, that is, synapses that present synaptic fatigue after heavy presynaptic stimulation. Our study shows that in absence of synaptic depression the two-qubits quantum system shows typical Rabi oscillations whose frequency decreases when synaptic depression is introduced, so one can trap excitations for a large period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be sustained in time by introducing a small energy shift between the qubits. In addition, we report that long time entanglement between the two qubits raises naturally in the presence of synaptic depression. Moreover, we propose and analyse a plausible experimental setup of our two-qubits system which demonstrates that these results are robust and can be experimentally obtained in a laboratory.
Biological neuronal networks represent a fundamental component of complex systems, including the brain. Simulating these networks accurately and efficiently is a significant challenge, largely due to the intricate, non-linear interactions between neurons. Traditional simulation methods, often relying on numerical integration techniques like the Hodgkin-Huxley model, are computationally intensive, particularly when scaling to large networks. This paper proposes a novel approach to neuronal network simulation inspired by quantum mechanics, specifically leveraging concepts of superposition and entanglement. We introduce a framework that utilizes quantum-inspired algorithms to represent and evolve neuronal states, aiming to dramatically reduce computational overhead. The core idea involves mapping neuronal dynamics onto a quantum system, where neurons are represented by qubits, and synaptic connections are modeled as quantum gates. The simulation process then utilizes quantum algorithms to evolve the system, effectively capturing the parallel and interconnected nature of neuronal networks. We present a mathematical formulation of the simulation process, focusing on the evolution equation for neuronal states. We explore key concepts like wave function collapse and measurement in the context of neuronal network simulation, offering a potential pathway for more realistic and efficient modeling of biological neural systems. The ultimate goal is to provide a computationally tractable method for simulating large-scale neuronal networks, leading to improved understanding of brain function and potentially facilitating the development of advanced neurotechnologies. The presented framework offers a distinct alternative to conventional methods, promising significant advantages in terms of speed and scalability.
Abstract: Contemporary computational intelligence confronts a dual thermodynamic barrier: classical deep learning accelerators are throttled by the von Neumann memory-bus bottleneck and megawatt-scale power consumption, while gate-based quantum processors remain confined to multi-kilowatt cryogenic dilution refrigerators operating at millikelvin temperatures. In contrast, the biological human brain executes high-dimensional perceptual synthesis and continuous learning within a tight thermal envelope of approximately 20 W at physiological temperatures (T ≈ 310 K), leveraging event-driven sparse spiking dynamics and sub-cellular vibronic transport mechanisms. This monograph formalizes the theoretical foundations and solid-state circuit architecture for a Quantum Spiking Neural Network (QSNN) powered by room-temperature polarons and biomimetic phononics: (1) We model artificial quantum neurons using non-linear exciton-polarons in lithium niobate (LiNbO3) and diamond acoustic metamaterial waveguides, demonstrating threshold-driven coherent optical-acoustic pulse emission (< 100 ps) operating at ambient temperature (300 K); (2) We formulate a Non-Markovian Quantum Synaptic Plasticity Model, proving that structured vibronic phonon modes act as an intrinsic temporal memory kernel supporting quantum Spike-Timing-Dependent Plasticity (Q-STDP) with long-term retention (τ_ret > 10^3 s) without requiring cryogenic volatile refresh cycles; and (3) We derive the physical energy dissipation budget, proving that each quantum synaptic event consumes less than 1.8 fJ (femtojoules per spike)—three orders of magnitude below CMOS neuromorphic baselines. We establish non-equilibrium Bose-Hubbard Hamiltonians, synaptic update operators, and planar wafer-scale integration blueprints hosting 10^7 quantum neurons and 10^10 quantum synapses, establishing a non-von Neumann foundation for cryogen-free, brain-scale quantum cognitive computing.
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 proposes a novel approach to quantum entanglement-based information encoding and decoding, drawing inspiration from biological neural networks. The core concept leverages the plasticity of synapses and the quantum entanglement properties between neurons to enhance the reliability of information transmission. Specifically, we simulate synaptic plasticity to dynamically modulate entanglement states, employing a biologically-inspired search algorithm to optimize these states for robust encoding and decoding. The algorithm's key innovation lies in the synergistic combination of biological mechanisms with quantum phenomena, offering a potentially more efficient and resilient framework for quantum information processing compared to traditional approaches. We present a theoretical framework outlining the algorithm's operation, detailing the mathematical formulation of the entanglement manipulation process and the optimization strategy. The proposed method holds promise for applications in secure communication, quantum sensing, and potentially, a deeper understanding of the interplay between quantum mechanics and biological systems.