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Asynchronous Multi-Party Quantum Computation

Authors Vipul Goyal, Chen-Da Liu-Zhang, Justin Raizes, João Ribeiro

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ACM Subject Classification
  • Hardware → Quantum communication and cryptography
Keywords
  • Quantum Cryptography
  • Multiparty Computation
  • Asynchronous

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Abstract

Multi-party quantum computation (MPQC) allows a set of parties to securely compute a quantum circuit over private quantum data. Current MPQC protocols rely on the fact that the network is synchronous, i.e., messages sent are guaranteed to be delivered within a known fixed delay upper bound, and unfortunately completely break down even when only a single message arrives late. 
Motivated by real-world networks, the seminal work of Ben-Or, Canetti and Goldreich (STOC'93) initiated the study of multi-party computation for classical circuits over asynchronous networks, where the network delay can be arbitrary. In this work, we begin the study of asynchronous multi-party quantum computation (AMPQC) protocols, where the circuit to compute is quantum.
Our results completely characterize the optimal achievable corruption threshold: we present an n-party AMPQC protocol secure up to t < n/4 corruptions, and an impossibility result when t ≥ n/4 parties are corrupted. Remarkably, this characterization differs from the analogous classical setting, where the optimal corruption threshold is t < n/3.

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Vipul Goyal, Chen-Da Liu-Zhang, Justin Raizes, and João Ribeiro. Asynchronous Multi-Party Quantum Computation. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 62:1-62:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ITCS.2023.62

Author Details

Vipul Goyal
  • Carnegie Mellon University, Pittsburgh, PA, USA
  • NTT Research, Sunnyvale, CA, USA
Chen-Da Liu-Zhang
  • NTT Research, Sunnyvale, CA, USA
Justin Raizes
  • Carnegie Mellon University, Pittsburgh, PA, USA
João Ribeiro
  • Carnegie Mellon University, Pittsburgh, PA, USA

Funding

Research supported in part by the NSF award 1916939, DARPA SIEVE program, a gift from Ripple, a DoE NETL award, a JP Morgan Faculty Fellowship, a PNC center for financial services innovation award, and a Cylab seed funding award.
  • Liu-Zhang, Chen-Da: Work partially done while the author was at Carnegie Mellon University.

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