Quantum Machine Learning Scientist
Quantum Machine Learning Scientist
Location: London, Victoria
Salary: Competitive Salary
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  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 these links have not been thoroughly explored; in this respect, 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.
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  and unsupervised  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.
- PhD in Quantum Physics, Computer Science
- Knowledge of quantum information, quantum algorithms and machine learning
- Published research in at least one relevant topic
- Peer-reviewed publications on high impact factor journals
- Familiarity with NISQ device and their implementation aspects
Employment with CQC is subject to satisfactory background checks being completed
Benefits We Offer
- Competitive compensation package
- Collaboration with top level academics
- Cycle to Work Scheme
- Company Pension
 Benedetti et al., Parameterized quantum circuits as machine learning models,
 Grant, et al., Hierarchical quantum classifiers,https://www.nature.com/articles/s41534-018-
 Benedetti et al., A generative modeling approach for benchmarking and training shallow
quantum circuits, https://www.nature.com/articles/s41534-019-0157-8 (2019)
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 firstname.lastname@example.org