This proposal is at the interface of Quantum-Computing (QC) and Algorithms (QA) and Machine Learning (ML) on one hand, and particle physics on the other. It aims at applying and developing QC/QA and ML to the tremendous challenges of particle physics at the Large Hadron Collider (LHC), both for event classification & reconstruction of particles from detector signals.For classification, the benchmark signal to separate from background processes is the supersymmetric top quark (stop), a phenomenologically well motivated particle, which is actively searched at the LHC. The proposal builds on and goes beyond earlier published results by the PI, which introduced the usage of both classical and quantum based ML tools in the stop search.An advanced neural-network will be tuned as classical ML, while quantum support vector machine and quantum variational classifier willbe used as quantum ML.For reconstruction, we aim to meet the challenges of the High-Luminosity (HL) phase of the LHC, where the very high number of produced charged particles represent a huge challenge for computation, thus for event reconstruction: the time for reconstructing tracks of charged particles is, with present algorithms, so slow that it could not cope with the number of pile-up events at the HL-LHC, which is expected to be around 200. Therefore, the goal is to build QA/QC based algorithms which are faster than classical ones, while maintaining their efficiency for reconstructing tracks. The challenge of track reconstruction at the HL-LHC is so immense that it will certainly help the QA/QC fields to grow.We plan to explore the application of quantum algorithms such as quantum annealing, and quantum walk to the challenging problem of pattern recognition in tracking detectors. The lower complexity of such algorithms is expected to result in a lower CPU time needed for track reconstruction.