Realising and compressing quantum circuits with quantum reservoir computing

Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation. Building quantum computers typically requires substantial engineering efforts to achieve precise control on qubits and quantum gates. Here, the authors introduce an architecture based on reservoir computing and machine learning to realize efficient quantum operations without resorting to full optimization of the control parameters.

Automated summary

The study demonstrates the use of a reservoir computing architecture with random quantum nodes as robust hardware for quantum computing. By optimizing a single layer of quantum nodes, the network can induce different quantum gates and compress sequences of multiple gates in quantum circuits, potentially reducing operation time and complexity.