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Quantum Volume for Photonic Quantum Processors.

Yuxuan Zhang1,2, Daoheng Niu1,2, Alireza Shabani3

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Summary
This summary is machine-generated.

This study introduces a new framework to apply standard quantum computing metrics to measurement-based quantum computing (MBQC) processors. This allows for better characterization and comparison of photonic quantum computing hardware.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing Hardware
  • Photonic Quantum Computing

Background:

  • Defining quantitative metrics is crucial for advancing quantum computing hardware development and comparing different quantum platforms.
  • Existing metrics like randomized benchmarking and quantum volume are primarily designed for circuit-based quantum computers.
  • Measurement-based quantum computing (MBQC) processors, particularly in photonic systems, lack direct applicability of these standard metrics.

Purpose of the Study:

  • To bridge the gap in characterizing measurement-based quantum computing (MBQC) processors using established quantum computing metrics.
  • To develop a framework for mapping physical noise in MBQC to logical errors in equivalent quantum circuits.
  • To enable the use of metrics like quantum volume for evaluating MBQC platforms.

Main Methods:

  • Developed a framework to translate physical noise and imperfections in MBQC processes into logical errors within equivalent quantum circuits.
  • Studied a continuous-variable cluster state using Gottesman-Kitaev-Preskill (GKP) encoding as a representative near-term photonic quantum computing candidate.
  • Derived effective logical gate error channels and calculated quantum volume based on GKP squeezing and photon transmission rates.

Main Results:

  • Successfully established a method to apply circuit-based quantum computing metrics to MBQC architectures.
  • Quantified the impact of physical imperfections on logical errors for GKP-encoded cluster states.
  • Calculated the quantum volume for a specific photonic MBQC system, demonstrating the framework's utility.

Conclusions:

  • The proposed framework effectively extends the applicability of standard quantum computing metrics to MBQC, including photonic systems.
  • This work provides essential tools for progress tracking, platform comparison, and roadmap design for MBQC hardware.
  • The study validates the use of quantum volume for characterizing near-term photonic quantum computers utilizing GKP encoding.