Hybrid Quantum-Classical Benchmarking - Assessing workflows that combine quantum and classical computation

Although quantum hardware makes significant progress in the direction of both scalability and fault-tolerance, it becomes increasingly clear that quantum computers will also work together with classical computers in the future. Quantum computers will enter as quantum accelerators into a more complex quantum-classical workflow, where they are expected to handle complex computational tasks intractable for classical computers.When looking at the resulting hybrid quantum-classical workflows, it turns out that the quantum and classical parts are very much interconnected - e.g., the appearance of the cost landscape created by a quantum variational algorithm may require dedicated classical optimizers. Given this complexity of quantum-classical workflows, the question arises on how they could be characterized in their performance and benchmarked. This talk will highlight this in two examples: on generic variational algorithms and on a concrete quantum-classical convolutional neural network.