Quantum autoencoders for communication-efficient cloud computing
Author(s)
Zhu, Yan; Bai, Ge; Wang, Yuexuan; Li, Tongyang; Chiribella, Giulio
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Abstract
In the model of quantum cloud computing, the server executes a computation on the quantum data provided by the client. In this scenario, it is important to reduce the amount of quantum communication between the client and the server. A possible approach is to transform the desired computation into a compressed version that acts on a smaller number of qubits, thereby reducing the amount of data exchanged between the client and the server. Here we propose quantum autoencoders for quantum gates (QAEGate) as a method for compressing quantum computations. We illustrate it in concrete scenarios of single-round and multi-round communication and validate it through numerical experiments. A bonus of our method is it does not reveal any information about the server’s computation other than the information present in the output.
Date issued
2023-07-10Department
Massachusetts Institute of Technology. Center for Theoretical PhysicsPublisher
Springer International Publishing
Citation
Quantum Machine Intelligence. 2023 Jul 10;5(2):27
Version: Author's final manuscript