Planck Technologies leverages computational chemistry methods and machine learning to find better materials for green energy storage
Signaloid develops a processor that helps computers track uncertainty and dramatically reduces computational effort and costs
Aqarios develops quantum algorithms in-house and provides a platform for classical and quantum algorithms to tackle optimization problems
Qruise leverages machine learning to build a physical model from a quantum computer’s experimental data to help improve them
Gemesys develops analog, brain-like AI chips with the potential to be 20,000 times more energy-efficient than today’s graphics processors.
Anaqor creates PlanQK, an open platform and ecosystem for quantum applications allowing developers to exchange code easily.
Orbital Materials uses advanced machine learning to better understand the world of atoms and develop better and greener materials.
Quobly leverages semiconductor technology to scale universal quantum computers to millions of silicon spin qubits.
Quantum Art develops develops ion trap quantum computers based on cutting-edge research from the Weizmann Institute of Science.
QEDMA Quantum Computing develops methods to mitigate errors to get optimal results from the current generation of quantum computers.