Quantum Machine Learning Scientist


    Website Cambridge Quantum Computing

    Research at Cambridge Quantum Computing

    Quantum machine learning (QML) is one of the most interesting applications of quantum computers. For example, parameterized quantum circuits (PQC) can be trained to perform tasks such as classification, regression, and generative modelling (see our recent Topical Review [1] for an introduction).

    QML algorithms can be successfully implemented using hybrid quantum-classical systems and are amongst the most promising techniques to show a near-term impact on real-world problems. Firstly, it has been empirically shown that they can cope with noise, a limited number of quantum bits, and shallow circuits. Secondly, it has been theoretically shown that some classes of circuits have remarkable expressive power and share many aspects with classical neural network models.  Also, QML borrows many concepts with Deep Learning and other areas of statistical learning and this links have not been thoroughly explored; in this respects, it offers great opportunities to Machine Learning experts interested in quantum computing applications.

    The field is thriving with new ideas and open questions such as proving quantum advantage, assessing computational complexity, designing variational circuits, and efficient state preparation, to name a few.


    Role description

    Cambridge Quantum Computing ltd (CQC) is looking to expand its cutting edge Machine Learning and Quantum Algorithms team with a full-time research lead. The role entails contributing to world-class scientific research on quantum algorithms for supervised [2] and unsupervised [3] learning. We are also looking to expand our research portfolio in directions that can demonstrate the impact of PQC on computational techniques with business applications. The research will be both theoretical and aimed at prototyping and benchmarking new algorithms on the state-of-the-art of quantum computers.

    This position is embedded in a team of world-class academics focusing on hybrid quantum algorithms for near term application, such as combinatorial optimization and Monte Carlo sampling, in partnership with some of the global leaders in the financial, manufacturing, chemical and shipping industries.


    Fundamental requirements

    PhD in Quantum Physics, Computer Science
    Knowledge of quantum information, quantum algorithms and machine learning
    Published research in at least one relevant topic


    Desirable experience

    Peer-reviewed publications on high impact factor journals
    Familiarity with NISQ device and their implementation aspects



    [1] Benedetti et al., Parameterized quantum circuits as machine learning models, https://arxiv.org/abs/1906.07682 (2019)

    [2] Grant, et al., Hierarchical quantum classifiers,https://www.nature.com/articles/s41534-018-0116-9 (2018)

    [3] Benedetti et al., A generative modeling approach for benchmarking and training shallow quantum circuits, https://www.nature.com/articles/s41534-019-0157-8 (2019)


    About Us:

    Established in 2014, Cambridge Quantum Computing (CQC) is a world leading independent quantum computing software company. CQC design solutions that benefit from quantum computing even in its earliest forms and allow the most effective access to these solutions for the widest variety of corporate and government users.

    CQC combines expertise in the product areas of quantum software, specifically a quantum compiler t|ket⟩™⟩, enterprise applications in the area of quantum chemistry, quantum machine learning (“QML”), and quantum encryption.

    The successful applicant will join one of the most exciting young companies in the world with the potential to dominate innovation across all aspects of the economy and society.


    Applicants should send a CV with cover note to careers@cambridgequantum.com



    Please note that employment at CQC is subject to successfully passing our pre-employment screening checks.


    To apply for this job email your details to careers@cambridgequantum.com