Harnessing Causal Indefiniteness for Accessing Locally Inaccessible Data
Sahil Gopalkrishna Naik, Samrat Sen, Ram Krishna Patra, Ananya, Chakraborty, Mir Alimuddin, Manik Banik, Pratik Ghosal

TL;DR
This paper explores how indefinite causal structures can enhance classical data retrieval tasks, demonstrating quantum advantages, super-activation phenomena, and the superiority of classical causally inseparable processes over quantum ones.
Contribution
It introduces a novel application of causal indefiniteness in classical data retrieval, establishing criteria for quantum process usefulness and revealing new phenomena like super-activation.
Findings
Quantum indefinite causal structures outperform definite ones in data retrieval.
Super-activation allows two ineffective processes to become useful together.
Classical causally inseparable processes can surpass quantum bi-causal processes.
Abstract
Recent studies suggest that physical theories can exhibit indefinite causal structures, where the causal order of events is fundamentally undefined yet logically consistent. Beyond its foundational appeal, causal indefiniteness has also emerged as a novel information-theoretic resource, offering advantages in various information processing tasks. Here, we investigate its utility in the classical Data Retrieval (DR) task. In its simplest version, a referee encodes classical messages into bipartite quantum states and distributes the local parts to two distant parties, ensuring that neither can independently extract any information about the encoded message. To retrieve their assigned data, parties must collaborate, and we show that those embedded in an indefinite causal structure generally outperform those operating within a definite causal framework. For the bipartite case, we establish…
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Taxonomy
TopicsCryptography and Data Security · Advanced Steganography and Watermarking Techniques · Privacy-Preserving Technologies in Data
