This paper investigates the performance of frames for the linear, redundant encoding of vectors when consecutive frame coefficients are lost due to the occurrence of random burst errors. We assume that the distribution of bursts is invariant under cyclic shifts and that the burst-length statistics are known. In analogy with rate-distortion theory, we wish to find frames of a given size, which minimize the mean-square reconstruction error for the encoding of vectors in a complex finite-dimensional Hilbert space. We obtain an upper bound for the mean-square reconstruction error for a given Parseval frame and in the case of cyclic Parseval frames, we find a family of frames which minimizes this upper bound. Under certain conditions, these minimizers are identical to complex Bose–Chaudhuri–Hocquenghem codes discussed in the literature. The accuracy of our upper bounds for the mean-square error is substantiated by complementary lower bounds. All estimates are based on convexity arguments and a discrete rearrangement inequality.

The central issue in this paper is to transmit a quantum state in such a way that after some decoherence occurs, most of the information can be restored by a suitable decoding operation. For this purpose, we incorporate redundancy by mapping a given initial quantum state to a messenger state on a larger dimensional Hilbert space via a C* -algebra embedding. Our noise model for the transmission is a phase damping channel which admits a noiseless subsystem or decoherence-free subspace. More precisely, the transmission channel is obtained from convex combinations of a set of lowest rank yes/no measurements that leave a component of the messenger state unchanged. The objective of our encoding is to distribute quantum information optimally across the noise-susceptible component of the transmission when the noiseless component is not large enough to contain all the quantum information to be transmitted. We derive simple geometric conditions for optimal encoding and construct examples of such encodings.