Reinforcement Learning-Guided Graph State Generation in Photonic Quantum Computers

The photonic quantum computer (PQC) is an emerging and promising quantum computing paradigm that has gained momentum in recent years.In PQC, computations are executed by performing measurements on photons in graph states (i.e., a collection of entangled photons).The graph state generation process is fulfilled by applying a sequence of quantum gates to quantum emitters, referred to as the "generation sequence".In a generation sequence, i) the time required to complete the generation sequence, ii) the number of quantum emitters used, and iii) the number of CZ gates performed between emitters greatly affect the fidelity of the generated graph state.In this paper, we propose RLGS (Reinforcement Learningguided Graph State generation), a novel compilation framework to identify optimal generation sequences that optimize the three fidelity metrics.Experimental results show that RLGS achieves an average reduction in generation time of 31.1%,49.6%, and 57.5% for small, medium, and large graph states compared to the baseline.Additionally, the reductions in the number of quantum emitters are 13.9%, 16.7%, and 17.5%, whereas the reductions in the number of CZ gates are 37.7%, 53.4%, and 57.8%, respectively.