  An unexpected breakdown in the existing theory of quantum serial turbo coding
is that a quantum convolutional encoder cannot simultaneously be recursive and
non-catastrophic. These properties are essential for quantum turbo code
families to have a minimum distance growing with blocklength and for their
iterative decoding algorithm to converge, respectively. Here, we show that the
entanglement-assisted paradigm simplifies the theory of quantum turbo codes, in
the sense that an entanglement-assisted quantum (EAQ) convolutional encoder can
possess both of the aforementioned desirable properties. We give several
examples of EAQ convolutional encoders that are both recursive and
non-catastrophic and detail their relevant parameters. We then modify the
quantum turbo decoding algorithm of Poulin et al., in order to have the
constituent decoders pass along only "extrinsic information" to each other
rather than a posteriori probabilities as in the decoder of Poulin et al., and
this leads to a significant improvement in the performance of unassisted
quantum turbo codes. Other simulation results indicate that
entanglement-assisted turbo codes can operate reliably in a noise regime 4.73
dB beyond that of standard quantum turbo codes, when used on a memoryless
depolarizing channel. Furthermore, several of our quantum turbo codes are
within 1 dB or less of their hashing limits, so that the performance of quantum
turbo codes is now on par with that of classical turbo codes. Finally, we prove
that entanglement is the resource that enables a convolutional encoder to be
both non-catastrophic and recursive because an encoder acting on only
information qubits, classical bits, gauge qubits, and ancilla qubits cannot
simultaneously satisfy them.
