Implementation of quantum compression on IBM quantum computers

Pivoluska, Matej; Plesch, Martin

doi:10.1038/s41598-022-09881-8

Cited by 8 publications

(1 citation statement)

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“…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. An implementation of quantum compression on IBM quantum computers is reported in the work [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

The Compression Optimality of Asymmetric Numeral Systems

Pieprzyk

Duda

Pawlowski

et al. 2023

Entropy

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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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Section: Introductionmentioning

confidence: 99%

The Compression Optimality of Asymmetric Numeral Systems

Pieprzyk

Duda

Pawlowski

et al. 2023

Entropy

View full text Add to dashboard Cite

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Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

et al. 2023

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Improving quantum-to-classical data decoding using optimized quantum wavelet transform

et al. 2023

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One of the challenges facing current Noisy-Intermediate-Scale-Quantum devices (NISQ) is achieving efficient quantum circuit measurement or readout. The process of extracting classical data from the quantum domain, termed in this work as quantum-to-classical (Q2C) data decoding, generally incurs significant overhead, since the quantum circuit needs to be sampled repeatedly to obtain useful data readout. In this paper, we propose and evaluate time-efficient and depth-optimized Q2C methods based on the multidimensional, multileveldecomposable, quantum wavelet transform (QWT) whose packet and pyramidal forms are leveraged and optimized. We also propose a zero-depth technique that uses selective placement of measurement gates to perform the QWT operation. To demonstrate their efficiency, the proposed techniques are quantitatively evaluated in terms of execution time, circuit depth, and accuracy in comparison to existing Q2C techniques. Experimental evaluations of the proposed Q2C methods are performed using real high-resolution multispectral images on a 27-qubit state-of-the-art quantum computing device from IBM Quantum.

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Implementation of quantum compression on IBM quantum computers

Cited by 8 publications

References 50 publications

The Compression Optimality of Asymmetric Numeral Systems

The Compression Optimality of Asymmetric Numeral Systems

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Improving quantum-to-classical data decoding using optimized quantum wavelet transform

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