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Register a new userOptimal quantum source coding with quantum information at the encoder and decoder
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Consider many instances of an arbitrary quadripartite pure state of four quantum systems ABCD. Alice holds the AC part of each state, Bob holds B, while D represents all other parties correlated with ABC. Alice is required to redistribute the C systems to Bob while asymptotically preserving the overall purity. We prove that this is possible using Q qubits of communication and E ebits of shared entanglement between Alice and Bob, provided that Q geq I(C;D|B)/2 and Q+E geq H(C|B), proving the optimality of the Luo-Devetak outer bound. The optimal qubit rate provides the first known operational interpretation of quantum conditional mutual information. We also show how our protocol leads to a fully operational proof of strong subadditivity and uncover a general organizing principle, in analogy to thermodynamics, that underlies the optimal rates.
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We report an experimental demonstration of Schumachers quantum noiseless coding theorem. Our experiment employs a sequence of single photons each of which represents three qubits. We initially prepare each photon in one of a set of 8 non-orthogonal codeword states corresponding to the value of a block of three binary letters. We use quantum coding to compress this quantum data into a two-qubit quantum channel and then uncompress the two-qubit channel to restore the original data with a fidelity approaching the theoretical limit.
We provide several formulas that determine the optimal number of entangled bits (ebits) that a general entanglement-assisted quantum code requires. Our first theorem gives a formula that applies to an arbitrary entanglement-assisted block code. Corollaries of this theorem give formulas that apply to a code imported from two classical binary block codes, to a code imported from a classical quaternary block code, and to a continuous-variable entanglement-assisted quantum block code. Finally, we conjecture two formulas that apply to entanglement-assisted quantum convolutional codes.
Quantum error-correcting codes will be the ultimate enabler of a future quantum computing or quantum communication device. This theory forms the cornerstone of practical quantum information theory. We provide several contributions to the theory of quantum error correction--mainly to the theory of entanglement-assisted quantum error correction where the sender and receiver share entanglement in the form of entangled bits (ebits) before quantum communication begins. Our first contribution is an algorithm for encoding and decoding an entanglement-assisted quantum block code. We then give several formulas that determine the optimal number of ebits for an entanglement-assisted code. The major contribution of this thesis is the development of the theory of entanglement-assisted quantum convolutional coding. A convolutional code is one that has memory and acts on an incoming stream of qubits. We explicitly show how to encode and decode a stream of information qubits with the help of ancilla qubits and ebits. Our entanglement-assisted convolutional codes include those with a Calderbank-Shor-Steane structure and those with a more general structure. We then formulate convolutional protocols that correct errors in noisy entanglement. Our final contribution is a unification of the theory of quantum error correction--these unified convolutional codes exploit all of the known resources for quantum redundancy.
This paper focuses on the structural properties of test channels, of Wyners operational information rate distortion function (RDF), $overline{R}(Delta_X)$, of a tuple of multivariate correlated, jointly independent and identically distributed Gaussian random variables (RVs), ${X_t, Y_t}_{t=1}^infty$, $X_t: Omega rightarrow {mathbb R}^{n_x}$, $Y_t: Omega rightarrow {mathbb R}^{n_y}$, with average mean-square error at the decoder, $frac{1}{n} {bf E}sum_{t=1}^n||X_t - widehat{X}_t||^2leq Delta_X$, when ${Y_t}_{t=1}^infty$ is the side information available to the decoder only. We construct optimal test channel realizations, which achieve the informational RDF, $overline{R}(Delta_X) triangleqinf_{{cal M}(Delta_X)} I(X;Z|Y)$, where ${cal M}(Delta_X)$ is the set of auxiliary RVs $Z$ such that, ${bf P}_{Z|X,Y}={bf P}_{Z|X}$, $widehat{X}=f(Y,Z)$, and ${bf E}{||X-widehat{X}||^2}leq Delta_X$. We show the fundamental structural properties: (1) Optimal test channel realizations that achieve the RDF, $overline{R}(Delta_X)$, satisfy conditional independence, $ {bf P}_{X|widehat{X}, Y, Z}={bf P}_{X|widehat{X},Y}={bf P}_{X|widehat{X}}, hspace{.2in} {bf E}Big{XBig|widehat{X}, Y, ZBig}={bf E}Big{XBig|widehat{X}Big}=widehat{X} $ and (2) similarly for the conditional RDF, ${R}_{X|Y}(Delta_X) triangleq inf_{{bf P}_{widehat{X}|X,Y}:{bf E}{||X-widehat{X}||^2} leq Delta_X} I(X; widehat{X}|Y)$, when ${Y_t}_{t=1}^infty$ is available to both the encoder and decoder, and the equality $overline{R}(Delta_X)={R}_{X|Y}(Delta_X)$.
In the task of machine translation, context information is one of the important factor. But considering the context information model dose not proposed. The paper propose a new model which can integrate context information and make translation. In this paper, we create a new model based Encoder Decoder model. When translating current sentence, the model integrates output from preceding encoder with current encoder. The model can consider context information and the result score is higher than existing model.
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