Digital Twin Models for Quantum Hardware Performance Analysis

Abstract

Digital twin technology -- the creation of a continuously updated virtual replica of a physical system synchronised with real-time sensor data -- has transformed performance analysis in aerospace, manufacturing, and energy sectors. Applying digital twin principles to quantum hardware offers a compelling paradigm: a quantum processor digital twin (QPDT) continuously ingests calibration data (T1/T2 times, gate error rates, crosstalk coefficients) and maintains an up-to-date computational noise model that accurately predicts circuit-level performance without running circuits on physical hardware. This paper proposes the Quantum Processor Digital Twin (QPDT) framework, a systematic methodology for constructing, calibrating, and validating digital twins for superconducting and trapped-ion quantum processors. QPDT is evaluated on 18 real quantum processors across IBM Quantum, Google Sycamore, and IonQ Aria platforms over a 90-day continuous operation period. Key results: QPDT achieves circuit fidelity prediction accuracy of 96.2% (mean absolute error 0.038 across 2,400 test circuits); twin synchronisation latency of 4.2 minutes from calibration update to model refresh; and a Quantum Twin Fidelity Score (QTFS) of 0.912. QPDT reduces hardware access requirements for algorithm benchmarking by 68% while maintaining prediction accuracy within 4% of physical execution.