  This paper investigates a turbo receiver employing a variational quantum
circuit (VQC). The VQC is configured with an ansatz of the quantum approximate
optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework
to optimize the turbo VQC decoder such that high fidelity soft-decision output
is generated. Besides demonstrating the proposed algorithm's computational
complexity, we show that the L2L VQC turbo decoder can achieve an excellent
performance close to the optimal maximum-likelihood performance in a
multiple-input multiple-output system.
