Aker BP and Cambridge Quantum Computing to Develop Quantum Machine Learning for Energy
(HPCWire) Cambridge Quantum Computing (“CQC”) has announced the results of their work with one of Europe’s largest independent energy companies, Aker BP.
The collaboration between Oslo based, Aker BP and CQC saw the design and demonstration of a cutting-edge Quantum Machine Learning (“QML”) algorithm to tackle a multiphase flow classification problem. The team’s solution consisted of an instantaneous quantum polynomial-time circuit trained as a three-class classifier, implemented on an IBM quantum processor using CQC’s quantum software development platform – t|ket⟩. Tested on Aker BP data, the QML classifier only required a handful of qubits to match the performance of a classical Support Vector Machine (“SVM”) with nonlinear kernels.
The results of this collaboration demonstrate one of the earliest applications of QML to the energy sector – providing a further step forward in the utility of today’s Noisy Intermediate Scale Quantum (“NISQ”) processors.
Dr Mattia Fiorentini, Head of Quantum Machine Learning at CQC said “We are pleased by the nature and results of our collaboration with Aker BP, demonstrating the early application of NISQ solutions to the energy sector. As both hardware and software continue to show significant developments, the impact of quantum technologies on many industry verticals is becoming increasingly clear”.
Aker BP is a fully-fledged E&P company with exploration, development and production activities on the Norwegian Continental Shelf (NCS). Measured in production, Aker BP is one of the largest independent oil companies in Europe.