Experimental simulation of postselected closed timelike curves for decoding scrambled quantum information
Yi-Te Huang, Hsiang-Wei Huang, Jhen-Dong Lin, Adam Miranowicz, Neill Lambert, Guang-Yin Chen, Franco Nori, Yueh-Nan Chen

TL;DR
This paper introduces a circuit-based decoding protocol using postselected closed timelike curves to interpret quantum experiments as time loops, enabling decoding of scrambled quantum information before it is generated.
Contribution
It presents a novel method to simulate time loops and decode scrambled quantum information via postselection, with experimental validation on cloud quantum processors.
Findings
Decoding scrambled quantum information is possible before its original creation.
Success probability relates to out-of-time-ordered correlations in quantum systems.
Experimental implementation achieved on cloud-based quantum processors.
Abstract
Quantum information scrambling (QIS) describes the rapid spread of initially localized information across an entire quantum many-body system through entanglement generation. Once scrambled, the original local information becomes encoded globally, inaccessible from any single subsystem. In this work, we introduce a circuit-based decoding protocol. By utilizing the concept of postselected closed timelike curves (PCTCs), we demonstrate how postselection allows us to interpret an ordinary quantum experiment as an example of a paradox-free trajectory, simulating a consistent time loop and reliable information recovery. Specifically, when conditioned on a final postselected outcome, this experiment can be interpreted as decoding the scrambled information even before the original information is generated. Furthermore, the success probability of the PCTC is governed by out-of-time-ordered…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
