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    Completed Consortium

    Quantum-Enhanced Clinical Decision Support for Intensive Care Medicine

    Applied Quantum Computing built a prototype hybrid quantum-classical model to predict risks for intensive care patients.

    Project data last updated 11 October 2026

    About this project

    Intensive care clinicians must spot urgent risks in large volumes of patient data. Applied Quantum Computing led the project with UCL, Evelina London Children's Hospital, King's College London and the NQCC.

    The team tested hybrid quantum-classical risk prediction models alongside classical models on several datasets. The main output was a prototype model for ICU risk prediction. It was one of the NQCC SparQ proof of concept projects for 2025-26.

    Lead organisation

    Applied Quantum Computing

    Company · London

    London firm applying quantum optimisation and machine learning to NHS problems such as theatre scheduling and intensive care risk prediction.

    Partners (4)

    Funders

    • NQCC

      Research organisation · Harwell

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    Sources

    1. NQCC: Quantum-Enhanced Clinical Decision Support for Intensive Care Medicine (nqcc.ac.uk)
    2. NQCC: Collaborative R&D (SparQ proof of concept projects) (nqcc.ac.uk)