The field of quantum machine learning is rapidly advancing, with a focus on developing innovative methods that leverage the power of quantum computing to improve classical machine learning models. Recent research has explored the application of quantum-inspired techniques, such as quantum augmentations and quantum-inspired encoding strategies, to enhance the performance of classical machine learning models. Additionally, there has been a growing interest in using quantum machine learning models, such as Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs), to tackle complex tasks, including time-series forecasting, classification, and clustering. These models have shown promising results, outperforming classical models in some cases.
Noteworthy papers include:
- Quantum Approximate Optimization Algorithm for Spatiotemporal Forecasting of HIV Clusters, which achieved 92% accuracy in cluster detection and 94% accuracy in HIV prevalence forecasting.
- AI-Hybrid TRNG, a deep-learning framework that extracts near-uniform entropy from physical noise, eliminating the need for bulky quantum devices or expensive laboratory-grade RF receivers.
- Quantum Machine Learning in Transportation, which used QSVM and QNN to model complex skin conductance response events that reflect pedestrian stress in a virtual reality road crossing experiment.