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Quantum Volume for Photonic Quantum Processors
Yuxuan Zhang1,2, Daoheng Niu1,2, Alireza Shabani3
1Cisco Quantum Lab, San Jose, California 95134, USA.
Physical Review Letters
|March 31, 2023
Summary
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.
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.
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