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IEEE Information Theory Workshop (ITW)

Achievability bounds on quantum state redistribution using convex split and position based decoding

Anurag Anshu, Rahul Jain, Naqueeb Ahmad Warsi

Published Nov 1, 2017• 0 citations
Publisher

Abstract

Quantum state redistribution is a fundamental quantum information theoretic primitive that captures a generic quantum communication scenario. In this work, we study the problem of entanglement assisted quantum state redistribution in one-shot setting and provide a new achievability result on the quantum communication required. Our bounds are in terms of max relative entropy and Rényi relative entropy of order 2. We show that our result is upper bounded by the result obtained in Berta, Christandl, Touchette (2016) (which is in terms of smooth conditional max and min entropies). We use the techniques of convex split and position based decoding (through pretty good measurement) to arrive at our result. Furthermore, in order to clarify the connection between our result and other recent results that use convex split and position based decoding, we prove a new relation between the hypothesis testing relative entropy and Rényi relative entropy of order 2.

DOI
10.1109/itw.2017.8277963

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Quantum state redistribution is a fundamental quantum information theoretic primitive that captures a generic quantum communication scenario. In this work, we study the problem of entanglement assisted quantum state redistribution in one-shot setting and provide a new achievability result on the quantum communication required. Our bounds are in terms of max relative entropy and Rényi relative entropy of order 2. We show that our result is upper bounded by the result obtained in Berta, Christandl, Touchette (2016) (which is in terms of smooth conditional max and min entropies). We use the techniques of convex split and position based decoding (through pretty good measurement) to arrive at our result. Furthermore, in order to clarify the connection between our result and other recent results that use convex split and position based decoding, we prove a new relation between the hypothesis testing relative entropy and Rényi relative entropy of order 2.
We study the problem of state redistribution both in the classical (shared randomness assisted) and quantum (entanglement assisted) one-shot settings and provide new upper bounds on the communication required. Our classical bounds are in terms of smooth-min and max relative entropies and the quantum bounds are in terms of max relative entropy and Renyi relative entropy of order 12\frac{1}{2}21​. We also consider a special case of this problem in the classical setting, previously studied by Braverman and Rao (2011). We show that their upper bound is optimal. In addition we provide an alternate protocol achieving a priory different looking upper bound. However, we show that our upper bound is essentially the same as their upper bound and hence also optimal. For the quantum case, we show that our achievability result is upper bounded by the achievability result obtained in Berta, Christandl, Touchette (2016).
This paper introduces the induced divergence, a new quantum divergence measure that replaces the hypothesis testing divergence in position-based decoding, simplifying the analysis of quantum communication and state redistribution while yielding tighter achievability bounds. Derived from a parent quantum relative entropy, it retains key properties such as data processing inequality and Löwner monotonicity. Like the hypothesis testing divergence, it depends on a smoothing parameter and interpolates between the parent relative entropy (as the smoothing parameter approaches one) and the min-relative entropy (as it approaches zero), the latter holding when applied to the sandwiched Rényi relative entropy of order α∈[0,2]α\in[0,2]α∈[0,2]. This framework refines the position-based decoding lemma, extending its applicability to a broader class of states and improving decoding success probabilities. Two key applications are considered: classical communication over quantum channels, where the induced divergence improves lower bounds on the distillable communication rate, and quantum state redistribution, where it leads to sharper bounds on communication costs. These results provide new insights into fundamental single-shot quantum information protocols and enhance existing analytical techniques.
This paper concerns the problem of quantum measurement compression with side information in the one-shot setting with shared-randomness. In this problem, Alice shares a pure quantum state with Bob and the reference system. She performs a measurement on her registers and wishes to communicate the outcome to Bob using shared-randomness and classical communication. The outcome that Bob receives must be correctly correlated with the reference system and his own registers. Our goal is to concurrently minimize the classical communication and shared-randomness cost. The suggested protocol presented in this paper is based on convex-split and position based decoding. The communication is upper bounded in terms of smooth max and hypothesis testing relative entropies. A second protocol addresses the task of strong randomness extraction in the presence of quantum side information. The protocol provides an error guarantee in terms of relative entropy (as opposed to trace distance) and extracts close to the optimal number of uniform bits. As an application, we provide a new achievability result for the task of quantum measurement compression without feedback, in which Alice does not need to know the outcome of the measurement. The result achieves the optimal number of bits communicated and the required number of bits of shared-randomness, for the same task in the asymptotic and i.i.d. setting.
Capacity of a quantum channel characterizes the limits of reliable communication through a noisy quantum channel. This fundamental information theoretic question is very well studied specially in the setting of many independent uses of the channel. An important scenario, both from practical and conceptual point of view, is when the channel can be used only once. This is known as the one-shot channel coding problem. We provide a tight characterization of the one-shot entanglement assisted classical capacity of a quantum channel. We arrive at our result by introducing a simple decoding technique which we refer to as position-based decoding. We also consider two other important quantum network scenarios: quantum channel with a jammer and quantum broadcast channel. For these problems, we use the recently introduced convex split technique [1] in addition to position based decoding. Our approach exhibits that the simultaneous use of these two techniques provides a uniform and conceptually simple framework for designing communication protocols for quantum networks.
In quantum state redistribution as introduced by Luo and Devetak and Devetak and Yard, there are four systems of interest: the A system held by Alice; the B system held by Bob; the C system that is to be transmitted from Alice to Bob; and the R system that holds a purification of the state in the ABC registers. We give upper and lower bounds on the amount of quantum communication and entanglement required to perform the task of quantum state redistribution in a one-shot setting. Our bounds are in terms of the smooth conditional minand max-entropy, and the smooth max-information. The protocol for the upper bound has a clear structure, building on the work of Oppenheim: it decomposes the quantum state redistribution task into two simpler coherent state merging tasks by introducing a coherent relay. In the independent and identical (i.i.d.) asymptotic limit our bounds for the quantum communication cost converge to the quantum conditional mutual information I(C; R|B), and our bounds for the total cost converge to the conditional entropy H(C|B). This yields an alternative proof of optimality of these rates for quantum state redistribution in the i.i.d. asymptotic limit. In particular, we obtain a strong converse for quantum state redistribution, which even holds when allowing for feedback.