Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance
Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation, given only the circuit's OpenQASM description and execution context. Our key insight is that quantum circuits from different algorithmic families (e.g., QFT, Grover, VQE) exhibit fundamentally distinct simulation cost profiles due to their di...
| Authors | Honjar Xing, Yehong Jiang, Xianbang Wang et al. |
|---|---|
| Primary category | quant-ph |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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