Co-Design of a Quantum Circuit using QGHNN for High Separate Fidelity and Maximum Grover Algorithm Accuracy
Abstract
Abstract A hybrid physics–machine-learning framework is introduced for the joint optimization of separate fidelity and Grover algorithm performance in superconducting quantum processors. The method combines a Hamiltonian-based open-quantum-system model of a two-qubit architecture coupled through a bus resonator with a QGHNN. Readout performance is evaluated via the Lindblad master-equation simulation of the dispersive measurement process, in which state-dependent resonator dynamics are mapped to realistic I/Q distributions and classified to estimate single-shot measurement fidelity. In parallel, algorithmic performance is assessed by simulating a two-qubit Grover search circuit in the presence of dissipation and dephasing, using effective cross-Kerr interactions derived from the underlying circuit parameters. A physically constrained dataset is constructed by sampling of some critical variables relevant to system. The QGHNN is trained to learn the nonlinear relationship between device parameters and the dual objectives of readout (separation) fidelity and Grover success probability. Gradient-based optimization is then carried out directly in parameter space using the trained network, yielding operating points that simultaneously improve measurement distinguishability and Grover algorithm accuracy. Simulation results demonstrate that the readout separation fidelity exceeds 99.4%, while the Grover success probability reaches approximately 86%, and this is comparable with the…
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