In November 2018, the first workshop on Quantum Computing in HEP was organised at CERN to assess the state of the art and share ideas and proposals for possible joint R&D activities between HEP institutes and industry. Over the past two years — as a direct outcome of these best-effort initiatives — events and collaborative pilot projects have been set up at CERN to explore the community’s interest in quantum technologies (in particular, quantum computing), as well as possible synergies with other scientific research domains.
Some of the projects are:
- Quantum optimisation for grid computing,
- Quantum computing for simulation: investigating quantum generative adversarial networks and quantum random number generators,
- Quantum graph neural networks,
- Quantum support vector machines for Higgs boson classification,
- Quantum machine-learning for SuperSymmetry searches.
Several collaborations already created across the HEP community include the German-Canadian network for quantum computing, hosted by DESY and TRIUMF. Apart from that, CERN has a few pilot investigation projects with leading academic and research centres, as well as industry in place. It also supports the creation of national quantum initiatives with statements of interest and direct advisory roles — for example, the US National Quantum Institutes programme.
Companies such as IBM, Google, Microsoft, D-Wave, and ATOS set up industry-academia networks to promote and speed up research and applications of quantum technologies. Emerging start-ups bring innovation in all aspects of quantum technologies, from quantum algorithms and software to sensors, cryogenic technologies, and quantum repeaters.
Today, CERN is in a unique position to establish a comprehensive R&D, academic and knowledge-sharing initiative, capitalising on existing activities and building the necessary expertise for the future. The Laboratory’s challenging scientific research programme could benefit significantly from the application of quantum technologies. For instance, in supporting the design of new sophisticated types of detectors or in accelerating computing workloads of physics experiments.