A quantum algorithm for Viterbi decoding of classical convolutional codes

TL;DR

This paper introduces a quantum Viterbi algorithm that accelerates decoding of classical convolutional codes by leveraging quantum superpositions and the structure of the decoding trellis, outperforming classical methods under certain conditions.

Contribution

The paper presents a novel quantum algorithm for Viterbi decoding that exploits the structure of the decoding trellis for potential speedup in classical convolutional code decoding.

Findings

Quantum speedup depends on the fanout of the trellis.

Superposition of all legal paths is efficiently prepared.

Amplitude amplification enhances the probability of the most likely path.

Abstract

We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than $Q$. The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.