Example use cases for quantum computing

Quantum computing techniques are being applied in the fields of optimisation, quantum simulation and quantum machine learning.


Maximisation and optimisation problems can be found everywhere in business and almost any problem can be cast into an optimisation framework.

It’s here that quantum computation can provide theoretical advantage over classical systems. Example applications include factory workflow optimisation, aircraft scheduling and traffic-flow optimisation for land-based transportation. In a finance context, early applications have been to portfolio optimisation and option pricing.


By its nature quantum computation operates at the quantum level and so can be used to model physical and chemical processes at their most fundamental level, a task which is particularly challenging for classical computing.

Quantum simulation is being used to model new compounds in Materials Science, for example to develop more efficient batteries, and to enhance existing best practices in computational chemistry.  An additional exciting area of application is in the identification of promising molecules that can result in faster drug discovery for the pharmaceutical industry.

Quantum machine learning

Quantum computing techniques can be used to enhance the performance of classical machine learning.  Researchers are working to develop algorithms to combine unique “learning” properties of quantum methods with conventional approaches to offer enhanced machine learning techniques using reduced volumes of data.  This area offers great potential and is a prime focus of quantum computing development.  Concrete applications can be expected across the full range of current machine learning use cases.