Circuit Fidelity

Circuit fidelity, the accuracy and reliability of a circuit's output, is a critical concern across diverse computing paradigms, from classical to quantum systems. Current research focuses on improving fidelity through methods like optimizing memristor-based neural networks for on-chip learning, mitigating crosstalk errors in noisy intermediate-scale quantum (NISQ) devices using techniques such as instruction barriers, and employing machine learning models, including graph transformers and LSTM networks, to predict and enhance circuit performance. These advancements are crucial for realizing the full potential of emerging computing technologies and improving the reliability of various applications, ranging from edge computing to quantum machine learning.