A quantum computer realizes operations on qubits to perform calculations by quantum gates. Today's quantum computers are referred to as NISQ (Noisy intermediate-scale quantum) hardware. These devices are limited in qubit numbers and the execution of quantum algorithms is highly prone to errors. The algorithms must therefore be adapted to the NISQ systems in such a way that they can be executed as efficiently as possible. This process, known as compilation, is a complex optimization task.
Researchers at the Fraunhofer Institute for Integrated Circuits IIS will investigate quantum circuit optimization techniques and compilation methods to improve the efficiency of existing quantum computers and algorithms. This requires the development of new classical algorithms and the improvement of existing tools. The scientists at Fraunhofer IIS are contributing their many years of expertise in the fields of optimization, machine learning, and reinforcement learning.
In addition, the researchers analyze industrial use cases and investigate their approximability for quantum computing in order to identify potential advantages.
Numerous quantum algorithms already exist today that could potentially be applied to various industrial use cases in the future. Different algorithms place different requirements on the hardware in terms of the number of gates, execution time, and the number of qubits. Optimized compilation can help reduce the algorithm’s susceptibility to errors on today's hardware.
In close collaboration with associated partners from various fields, the consortium identifies industrial use cases and aims to provide suitable quantum algorithms for future applications.
Fraunhofer Institute for Integrated Circuits IIS