Quantum data compression is of great significance to both quantum computation and quantum communication due to the limited resources for quantum storage. Here in this study, we present an efficient multi-qubit quantum data compression protocol in which N identically prepared qubits can be perfectly compressed into qubits. Moreover, the feasible implementation of the quantum data compression is also designed based on the IBM Quantum Experience. The experiment reveals efficient information compression performance using the current scheme. We believe this method might be useful in quantum information which one could make the encoding of information on the qubits more efficiently, and also reducing the resources consumption for quantum storage.Quantum data compression is of great significance to both quantum computation and quantum communication due to the limited resources for quantum storage. Here in this study, we present an efficient multi-qubit quantum data compression protocol in which N identically prepared qubits can be perfectly compressed into qubits. Moreover, the feasible implementation of the quantum data compression is also designed based on the IBM Quantum Experience. The experiment reveals efficient information compression performance using the current scheme. We believe this method might be useful in quantum information which one could make the encoding of information on the qubits more efficiently, and also reducing the resources consumption for quantum storage.

Quantum EngineeringMay 5, 202115 citations

Quantum Information Compression

François Chapeau‐Blondeau

Springer eBooksJan 1, 20260 citations

Efficient quantum compression for identically prepared states with arbitrary dimensio

Zeyu Chen, Chunhe Xiong, Kamil Ravilevich Khadiev +1 more

Identical preparation creates permutation symmetry that can be used for lossless quantum compression. For $n$ copies of an unknown $d$ -dimensional pure state, the tensor-power input lies in the fully symmetric subspace, whose dimension is polynomial in $n$ for fixed local dimension. Schur--Weyl duality isolates this subspace coherently, allowing the fixed representation labels to be discarded while retaining all information needed for recovery. The resulting encoder and decoder are exact on the input family. Because tensor-power states span the symmetric subspace, the achieved memory dimension is also necessary for reversible coherent compression, so the scheme is space-optimal. The encoder is implemented recursively with Clebsch--Gordan transforms, and only the branches reached by symmetric inputs need to be reproduced. Standard efficient synthesis of these transforms yields polynomial-size circuits for fixed local dimension and arbitrary target accuracy. Thus identically prepared pure states in arbitrary dimension admit lossless, space-optimal compression with an efficient circuit implementation.Identical preparation creates permutation symmetry that can be used for lossless quantum compression. For $n$ copies of an unknown $d$-dimensional pure state, the tensor-power input lies in the fully symmetric subspace, whose dimension is polynomial in $n$ for fixed local dimension. Schur--Weyl duality isolates this subspace coherently, allowing the fixed representation labels to be discarded while retaining all information needed for recovery. The resulting encoder and decoder are exact on the input family. Because tensor-power states span the symmetric subspace, the achieved memory dimension is also necessary for reversible coherent compression, so the scheme is space-optimal. The encoder is implemented recursively with Clebsch--Gordan transforms, and only the branches reached by symmetric inputs need to be reproduced. Standard efficient synthesis of these transforms yields polynomial-size circuits for fixed local dimension and arbitrary target accuracy. Thus identically prepared pure states in arbitrary dimension admit lossless, space-optimal compression with an efficient circuit implementation.

arXiv:2307.12024Jul 22, 20230 citations

Quantum Data Compression Algorithm

Jincheng Zhang

Quantum Data Compression Algorithm is a novel algorithm designed to enhance data transmission efficiency through adaptive compression based on the inherent properties of the data itself. This research explores a method that dynamically adjusts compression parameters based on the data's characteristics, moving beyond traditional compression techniques. The core mechanism relies on leveraging quantum data compression principles to achieve a more robust and efficient compression process. This paper presents a detailed overview of the proposed algorithm, its underlying principles, and preliminary experimental results demonstrating its effectiveness.Quantum Data Compression Algorithm is a novel algorithm designed to enhance data transmission efficiency through adaptive compression based on the inherent properties of the data itself. This research explores a method that dynamically adjusts compression parameters based on the data's characteristics, moving beyond traditional compression techniques. The core mechanism relies on leveraging quantum data compression principles to achieve a more robust and efficient compression process. This paper presents a detailed overview of the proposed algorithm, its underlying principles, and preliminary experimental results demonstrating its effectiveness.

Sep 3, 20260 citations

Efficient compression of unknown quantum information

Martin Plesch, Vladimír Bužek

Jan 1, 20100 citations

Identical preparation creates permutation symmetry that can be used for lossless quantum compression. For

n

copies of an unknown

d

-dimensional pure state, the tensor-power input lies in the fully symmetric subspace, whose dimension is polynomial in

n

for fixed local dimension. Schur--Weyl duality isolates this subspace coherently, allowing the fixed representation labels to be discarded while retaining all information needed for recovery. The resulting encoder and decoder are exact on the input family. Because tensor-power states span the symmetric subspace, the achieved memory dimension is also necessary for reversible coherent compression, so the scheme is space-optimal. The encoder is implemented recursively with Clebsch--Gordan transforms, and only the branches reached by symmetric inputs need to be reproduced. Standard efficient synthesis of these transforms yields polynomial-size circuits for fixed local dimension and arbitrary target accuracy. Thus identically prepared pure states in arbitrary dimension admit lossless, space-optimal compression with an efficient circuit implementation.