A technical paper titled “Measurement-induced entanglement and teleportation on a noisy quantum processor” was published by researchers at Google Quantum AI, Google Research, Stanford University, University of Texas at Austin, Cornell University, University of Massachusetts, University of Connecticut, Auburn University, University of Technology Sydney, University of California, and Columbia University.
“Measurement has a special role in quantum theory, by collapsing the wavefunction, it can enable phenomena such as teleportation and thereby alter the ‘arrow of time’ that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space–time that go beyond the established paradigms for characterizing phases, either in or out of equilibrium. For present-day noisy intermediate-scale quantum (NISQ) processors, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling to measurement-induced teleportation. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.”
Find the technical paper here. Published October 2023 (preprint).
Google Quantum AI and Collaborators. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 622, 481–486 (2023). https://doi.org/10.1038/s41586-023-06505-7
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