What you will need to succeed
PhD in Quantum Physics, Mathematics, Computer Science, or related field.
Knowledge of research topics in quantum information, quantum algorithms and machine learning.
Published research in at least one of the aforementioned topics.
Peer-reviewed publications on high impact factor journals.
Familiarity with NISQ device and their implementation aspects.
What’s in it for you
Working alongside a highly talented team, with leading names in the quantum computing industry. We offer a highly competitive package including equity participation, 28 day’s paid annual leave, a workplace pension and a positive approach to flexible working.
The quantum computers available today have proven to be a viable tool to investigate scientific, engineering, and industrial problems. Yet, the full potential of quantum computation has not been unlocked. In fact, hardware limitations typical of the Noisy Intermediate-Scale (NISQ) era, such as noise, a small number of qubits, and inefficient Input/Output operations have hindered the implementation of well known, powerful algorithms. On one hand, Variational Quantum Algorithms (VQAs) can circumvent NISQ obstacles but closer scientific scrutiny is necessary to understand the trade off between robustness and efficiency. On the other hand, quantum algorithms that have quantum advantage guarantees and are suitable for NISQ devices are mostly unexplored territory.
At Cambridge Quantum Q, we have studied machine learning (ML) problems and used parameterised quantum circuits as quantum models for supervised learning, generative modelling, and Bayesian inference. CQ scientists have explored the reach of VQAs beyond ML, to attack nonlinear partial differential equations, combinatorial optimisation, quantum simulation and Monte Carlo sampling. Whilst VQAs have proven to be practical and flexible tools for current NISQ computers, we believe that developing algorithms that go beyond variational approaches is crucial. We are committed to researching ways of bringing the advantage guarantees of algorithms for fault-tolerant quantum computers to NISQ hardware more systematically, which is a key goal for the whole quantum computing sector in the medium term.
Cambridge Quantum is looking to expand its cutting edge Machine Learning and Quantum Algorithms division with a full-time researcher. The successful candidate will join a world-class scientific team focused on quantum algorithms for machine learning, and other key computational problems, with a particular focus on variational and differentiable approaches. The new member of the team will participate in ongoing research projects, and are expected to pioneer new research directions in quantum algorithms to address some of the more compelling open questions.
The research will be both theoretical and aimed at benchmarking new algorithms on the state of the art of quantum computers. Significant results will be published in peer-reviewed journals and/or patented. Aside from the pure research focus, this role will support client engagements by contributing to the definition of the scientific scope, the problem formulation, and the solution using quantum algorithms.
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