Quantum computing emulation using neuromorphic artificial spiking neurons designed with CMOS electronics is introduced in this paper. By extending the Axon-Hillock neuron model, the proposed circuit can emulate a universal set of quantum gates-Phase Shift, Hadamard, and CNOT-at room temperature, with ultra-low power consumption. Simulations and layout using 180 nm CMOS technology demonstrate successful emulation of quantum logic operations and entanglement, including GHZ states, with small area and significant reduction in power compared to previous oscillator-based approaches. This work paves the way for scalable, energy-efficient quantuminspired computation on standard CMOS hardware, suitable for mobile and edge applications.
Quantum computing emulation using neuromorphic artificial spiking neurons designed with CMOS electronics is introduced in this paper. By extending the Axon-Hillock neuron model, the proposed circuit can emulate a universal set of quantum gates-Phase Shift, Hadamard, and CNOT-at room temperature, with ultra-low power consumption. Simulations and layout using 180 nm CMOS technology demonstrate successful emulation of quantum logic operations and entanglement, including GHZ states, with small area and significant reduction in power compared to previous oscillator-based approaches. This work paves the way for scalable, energy-efficient quantuminspired computation on standard CMOS hardware, suitable for mobile and edge applications.
This paper explores a novel approach to neuromorphic computing by leveraging the unique properties of quantum entanglement. The core idea is to utilize multi-particle quantum entanglement states as the physical connections between artificial neurons, enabling efficient parallel information processing and mimicking biological neural networks. We propose a quantum neuromorphic chip architecture employing multiple superconducting quantum bits (Qubits) engineered to exhibit entanglement. The design focuses on manipulating the coherence time, topological structure, and combined operation of quantum gates to emulate synaptic transmission and learning within biological neural networks. Specifically, we investigate encoding information through entanglement and explore parallel computation facilitated by this quantum phenomenon. Furthermore, we address the challenges of quantum computation by integrating quantum decoherence and quantum error correction techniques. The potential for achieving high-performance, energy-efficient, and biologically inspired computing is discussed, representing a significant departure from traditional approaches and classical neural networks. The proposed framework offers a pathway towards scalable quantum neuromorphic systems with potential applications in areas such as pattern recognition, machine learning, and complex system modeling.
Digital neuromorphic systems seek to replicate the organizational principles and dynamics of biological neural networks in silicon, offering massive parallelism, event‐driven processing, and orders-of-magnitude lower energy consumption compared with conventional von Neumann computers. Concurrently, quantum artificial intelligence (AI) methods exploit quantum superposition and entanglement to accelerate optimization and pattern-recognition tasks. However, quantum hardware today remains constrained by qubit count and noise, while neuromorphic chips are limited in their ability to discover global optima during training. In this work, we propose and evaluate a hybrid framework—“quantum-optimized neuromorphic inference”—in which a variational quantum circuit (VQC) tunes synaptic weight matrices for a spiking neural network (SNN), and the optimized SNN is deployed on a digital neuromorphic core for inference. We first survey the relevant neuromorphic platforms and quantum machine learning algorithms, then describe our methodology for weight optimization and mapping to neuromorphic hardware. Through extensive simulations on a spoken-digit classification benchmark, our hybrid approach achieves a 3.3% absolute improvement in accuracy over a conventionally trained SNN, while reducing energy per inference by 3.5%. We discuss implications for scalable brain emulation, energy-efficient AI, and future neuromorphic-quantum co-design, and outline next steps toward experimental validation on emerging quantum processors and multi-core neuromorphic systems.
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters, highlighting its potential for event-driven applications such as neuromorphic integrated sensing and communications (N-ISAC).
Abstract Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.
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.
As the field of artificial intelligence experiences the emergence of various large-scale models, the computing power requirements are increasing. Simultaneously, Moore’s Law is approaching its limits, which intensifies the need to address how to perform high-dimensional complex data computations efficiently and with minimal energy consumption. Quantum computing and neuromorphic computing, with their unique features, offer potential solutions to this contemporary challenge. In this study, we aim to integrate quantum computing with neuromorphic computing, and develop a quantum spiking neural networks architecture. We proposed three measurement strategy to reducing operational costs, making it more friendly to near-term quantum hardware. We evaluated the performance of these three models on the standard MNIST dataset for classification tasks and found that they maintained a high level of accuracy despite a reduced number of parameters and a substantial decrease in the number of quantum circuit shots.