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Silicon-based spin qubit platform is a promising candidate for the hardware realization of quantum computing. Charge noise, however, plays a critical role in limiting the fidelity and scalability of silicon-based quantum computing technologies. This work presents Green’s transfer function approach to simulate the correlated noise power spectral density (PSD) in silicon spin qubit devices. The simulation approach relates the dynamics of the charge noise source of two-level fluctuators (TLFs) to the correlated noise of spin qubit device characteristics through a transfer function. It allows the noise auto-correlation and cross correlation between any pairs of physical quantities of interest to be systematically computed and analyzed. Because each spin qubit device involves only a small number of TLFs due to its nanoscale device size, the distribution of TLFs impacts the noise correlation significantly. In both a two-qubit quantum gate and a spin qubit array device, the charge noise shows strong cross correlation between neighboring qubits. The simulation results also reveal a phase-flipping feature of the noise cross-PSD between neighboring spin qubits, consistent with a recent experiment. 

The platform of silicon-based spin qubits holds significant potential for the hardware implementation of quantum computing. Charge noise, however, notably hinders the performance and scalability of silicon-spin-based quantum computing technologies. Here we computationally investigated correlated charge noise in silicon spin quantum computing devices by developing and applying a Green’s transfer function approach. The approach allows for the systematic simulation and analysis of both the noise's auto-correlation and cross-correlation spectrums in a physics-based manner. We simulate the correlated noise's power spectral density (PSD) in silicon spin qubit devices. The results indicate strong cross-correlation and show phase-flipping features in neighboring silicon spin qubits, in agreement with a recent experiment. Given that each spin qubit device is small and influenced by a limited number of two-level fluctuators (TLFs), the arrangement of these TLFs plays a crucial role in the correlation of noise. The simulation study highlights the need to consider noise correlation and its related spectral features in developing robust quantum computing technologies based on silicon spin qubits. 

We introduce a hybrid model that synergistically combines machine learning (ML) with semiconductor device physics to simulate nanoscale transistors. This approach integrates a physics-based ballistic transistor model with an ML model that predicts ballisticity, enabling flexibility to interface the model with device data. The inclusion of device physics not only enhances the interpretability of the ML model but also streamlines its training process, reducing the necessity for extensive training data. The model's effectiveness is validated on both silicon nanotransistors and carbon nanotube FETs, demonstrating high model accuracy with a simplified ML component. We assess the impacts of various ML models—Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and RandomForestRegressor (RFR)—on predictive accuracy and training data requirements. Notably, hybrid models incorporating these components can maintain high accuracy with a small training dataset, with the RNN-based model exhibiting better accuracy compared to the MLP and RFR models. The trained hybrid model provides significant speedup compared to device simulations, and can be applied to predict circuit characteristics based on the modeled nanotransistors. 

Silicon-based spin qubits represent a promising technology for scalable quantum computing. However, the complex nature of this field, which requires a deep understanding of quantum mechanics, materials science, and nanoelectronics, poses a significant challenge in making it accessible to future engineers and scientists. Spin Quantum Gate Lab, a spin qubit simulation tool, is proposed in this paper to address this obstacle. This tool is designed to introduce key concepts of spin qubit to undergraduate students, enabling the simulation of single-qubit rotational gates and two-qubit controlled-phase gates. By providing hands-on experience with quantum gate operations, it effectively links theoretical quantum concepts to practical experience, fostering a deeper understanding of silicon-based quantum computing. 

Edge devices face challenges when implementing deep neural networks due to constraints on their computational resources and power consumption. Fuzzy logic systems can potentially provide more efficient edge implementations due to their compactness and capacity to manage uncertain data. However, their hardware realization remains difficult, primarily because implementing reconfigurable membership function generators using conventional technologies requires high circuit complexity and power consumption. Here we report a multigate van der Waals interfacial junction transistor based on a molybdenum disulfide/graphene heterostructure that can generate tunable Gaussian-like and π-shaped membership functions. By integrating these generators with peripheral circuits, we create a reconfigurable fuzzy controller hardware capable of nonlinear system control. This fuzzy logic system can also be integrated with a few-layer convolution neural network to form a fuzzy neural network with enhanced performance in image segmentation.