Optimal quantum learning of a unitary transformation

We address the problem of learning an unknown unitary transformation from a finite number of examples. The problem consists in finding the learning machine that optimally emulates the examples, thus reproducing the unknown unitary with maximum fidelity. Learning a unitary is equivalent to storing it in the state of a quantum memory (the memory of the learning machine) and subsequently retrieving it. We prove that, whenever the unknown unitary is drawn from a group, the optimal strategy consists in a parallel call of the available uses followed by a “measure-and-rotate” retrieving. Differing from the case of quantum cloning, where the incoherent “measure-and-prepare” strategies are typically suboptimal, in the case of learning the “measure-and-rotate” strategy is optimal even when the learning machine is asked to reproduce a single copy of the unknown unitary. We finally address the problem of the optimal inversion of an unknown unitary evolution, showing also in this case the optimality of the “measure-and-rotate” strategies and applying our result to the optimal approximate realignment of reference frames for quantum communication.

Figure 1

The learning process is described by a quantum comb (in white) representing the storing board, in which the $N$ uses of a unitary $U$ are plugged, along with the state $|\psi \rangle$ (in gray). The wires represent the input-output Hilbert spaces. The output of the first comb is stored in a quantum memory, later used by the retrieving channel $\mathcal{R}$.