Computationally-secure and composable remote state preparation
April 12, 2019 Β· Declared Dead Β· π IEEE Annual Symposium on Foundations of Computer Science
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Authors
Alexandru Gheorghiu, Thomas Vidick
arXiv ID
1904.06320
Category
quant-ph: Quantum Computing
Cross-listed
cs.CR
Citations
77
Venue
IEEE Annual Symposium on Foundations of Computer Science
Last Checked
3 months ago
Abstract
We introduce a protocol between a classical polynomial-time verifier and a quantum polynomial-time prover that allows the verifier to securely delegate to the prover the preparation of certain single-qubit quantum states. The protocol realizes the following functionality, with computational security: the verifier chooses one of the observables $Z$, $X$, $Y$, $(X+Y)/\sqrt{2}$, $(X-Y)/\sqrt{2}$; the prover receives a uniformly random eigenstate of the observable chosen by the verifier; the verifier receives a classical description of that state. The prover is unaware of which state he received and moreover, the verifier can check with high confidence whether the preparation was successful. The delegated preparation of single-qubit states is an elementary building block in many quantum cryptographic protocols. We expect our implementation of "random remote state preparation with verification", a functionality first defined in (Dunjko and Kashefi 2014), to be useful for removing the need for quantum communication in such protocols while keeping functionality. The main application that we detail is to a protocol for blind and verifiable delegated quantum computation (DQC) that builds on the work of (Fitzsimons and Kashefi 2018), who provided such a protocol with quantum communication. Recently, both blind an verifiable DQC were shown to be possible, under computational assumptions, with a classical polynomial-time client (Mahadev 2017, Mahadev 2018). Compared to the work of Mahadev, our protocol is more modular, applies to the measurement-based model of computation (instead of the Hamiltonian model) and is composable. Our proof of security builds on ideas introduced in (Brakerski et al. 2018).
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