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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Materials Science

Background:

  • Quantum computing faces decoherence and noise challenges, limiting practical algorithm implementation.
  • Understanding collective qubit behavior and its relation to connectivity architecture is crucial but computationally complex.
  • Existing methods struggle to systematically characterize the link between quantum architecture and noise susceptibility.

Purpose of the Study:

  • To develop a machine learning framework for predicting quantum device decoherence lifetime directly from connectivity patterns.
  • To bridge graph theory features with quantum device characterization for noise-optimized quantum processor design.
  • To identify platform-specific relationships between topological features and decoherence mechanisms.

Main Methods:

  • Representing quantum architectures as connected graphs with 14 topological features.
  • Utilizing supervised learning models to predict decoherence lifetime from graph features.
  • Analyzing sensitivity to global connectivity (superconducting) and system scale (semiconductor).

Main Results:

  • Achieved accurate lifetime predictions (R2>0.96) for superconducting and semiconductor platforms using graph features.
  • Identified distinct decoherence sensitivities: superconducting qubits sensitive to global connectivity, semiconductor qubits to system scale.
  • Demonstrated complete failure of cross-platform model transfer, highlighting platform-specific design needs.

Conclusions:

  • Machine learning can accurately predict decoherence lifetime from quantum architecture connectivity.
  • Connectivity design for noise optimization is platform-specific, requiring tailored approaches for superconducting and semiconductor qubits.
  • This framework offers rapid assessment of quantum architectures, guiding practical quantum processor development.