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Table I summarizes the performance metrics over the test set.

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Quantum Machine Learning (QML) seeks to exploit quantum superposition and entanglement for data processing. Farhi et al. proposed the Quantum Approximate Optimization Algorithm (QAOA), which has been adapted for portfolio optimization. Recent studies have explored "Quantum Neural Networks" (QNN), suggesting that parameterized quantum circuits can approximate complex functions with fewer parameters than classical networks, offering a potential "quantum advantage" in generalization. Table I summarizes the performance metrics over the test set

I.FAST gained significant visibility at the 13th International Particle Accelerator Conference (IPAC'22), held in Bangkok, Thailand, in June 2022. At this conference, which brought together around 800 people from around the world, I.FAST was the source of four oral presentations. These presentations showcased cutting-edge research, including Frank Zimmermann's (CERN) considerations on the impact of longitudinal gradient dipoles on storage ring performance, Mike Seidel's (PSI) presentation on improving the efficiency of particle accelerators, and Toms Torims' (Riga TU) presentation on the world's first full RFQ (Radio Frequency Quadrupole) prototype built using additive manufacturing. Additionally, I.FAST was highlighted in the industry session as a prime example of co-innovation between academia and industry, emphasizing that projects like I.FAST are essential for establishing a broad open innovation ecosystem for accelerator-based research infrastructures. Farhi et al