QuTeNet

The capabilities of quantum computers promise a paradigm shift for extremely computationally intensive applications such as optimisation problems, quantum systems simulation and artificial intelligence. In the QuTeNet project, we are investigating a specific architecture of quantum algorithms based on tensor networks, an efficient representation of quantum states. To this end, we are further developing tensor network methods on classical computers and transferring tensor networks to quantum computers in order to realise simulations and AI. Using a use case, we are investigating whether quantum simulations and quantum machine learning can be coupled.

Motivation

For quantum AI systems, tasks involving classical data are currently being investigated. However, the quantum coding required for this is often so inefficient that no quantum advantages can be achieved in the overall algorithm. On the other hand, information contained in the quantum states is inevitably lost at the end of a quantum simulation due to the measurement process. The combination of quantum simulation and quantum AI for their evaluation directly on the quantum computer bypasses the step via the classical world — and thus both problems.

We are also developing evaluation approaches and implementations for both applications of tensor networks. By comparing classical and quantum approaches, differences and similarities can be identified and their capability ranges delineated. In this way, we show perspectives for application-oriented industrial and academic use, including scaling for more powerful quantum hardware.

Challenge

Quantum computers already enable simulations of quantum systems on a small scale. However, for the development of future quantum technologies, more complex simulations are needed, requiring new methods on the QC hardware. Both aspects of the QuTeNet project – simulations and artificial intelligence (AI) – use tensor networks, a method of representing complex (quantum) states as a network of smaller tensors. Machine learning structures can also be represented in this extremely efficient structure.

We develop concepts and methods to implement and analyse quantum tensor networks, evaluate the development possibilities of tensor network methods on classical computers and deal with concrete use cases in the simulation of quantum systems.