  An experiment is performed to reconstruct an unknown photonic quantum state
with a limited amount of copies. A semi-quantum reinforcement learning approach
is employed to adapt one qubit state, an "agent," to an unknown quantum state,
an "environment," by successive single-shot measurements and feedback, in order
to achieve maximum overlap. The experimental learning device herein, composed
of a quantum photonics setup, can adjust the corresponding parameters to rotate
the agent system based on the measurement outcomes "0" or "1" in the
environment (i.e., reward/punishment signals). The results show that, when
assisted by such a quantum machine learning technique, fidelities of the
deterministic single-photon agent states can achieve over 88% under a proper
reward/punishment ratio within 50 iterations. This protocol offers a tool for
reconstructing an unknown quantum state when only limited copies are provided,
and can also be extended to higher dimensions, multipartite, and mixed quantum
state scenarios.
