An improved quantum network communication model based on compressed tensor network states

Zhang, Qiang; Lai, Hong; Pieprzyk, Josef; Pan, Lei

doi:10.1007/s11128-022-03609-3

Cited by 1 publication

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“…Recent developments in quantum technology bring us closer to the construction of real-life quantum computers [ 17 ]. A natural question is whether or not compression of quantum data is feasible [ 18 , 19 ]. The question has been answered in the affirmative in the work [ 20 ], which shows how to compress an ensemble of qubits into exponentially fewer qubits.…”

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confidence: 99%

The Compression Optimality of Asymmetric Numeral Systems

Pieprzyk

Duda

Pawlowski

et al. 2023

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Source coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming, for instance) renews interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from having a beautiful mathematical structure, it is very efficient and offers compression with a very low coding redundancy. ANS works well for any symbol source statistics, and it has become a preferred compression algorithm in the IT industry. However, designing an ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression ratio. The paper investigates the compression optimality of ANS. It shows that ANS is optimal for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to precisely determine the ANS compression rate. We present two algorithms for finding ANS instances with a high compression ratio. The first explores state probability approximations in order to choose ANS instances with better compression ratios. The second algorithm is a probabilistic one. It finds ANS instances whose compression ratios can be made as close to the best ratio as required. This is done at the expense of the number θ of internal random “coin” tosses. The algorithm complexity is O(θL3), where L is the number of ANS states. The complexity can be reduced to O(θLlog2L) if we use a fast matrix inversion. If the algorithm is implemented on a quantum computer, its complexity becomes O(θ(log2L)3).

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