Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

2026-06-09

AI Summary

This work addresses the challenge of time-consuming trial-and-error procedures required to determine suitable approximation parameters—such as bond dimension cutoffs—in quantum circuit simulation. The authors propose an algorithm-family-aware residual neural network approach that jointly predicts the minimal bond dimension threshold needed to achieve a target fidelity and the corresponding runtime, based on OpenQASM circuit descriptions and execution context. Innovatively treating algorithm family as a first-order conditional feature, the method integrates a pretrained family classifier with a heuristic gate-composition–based algorithm fingerprint to enable accurate and efficient prediction. Evaluated across circuits ranging from 7 to 130 qubits and spanning 10 distinct algorithm families, the model achieves a 79.5% accuracy in threshold prediction (91.2% within one threshold level) and an R² of 0.82 for runtime estimation, with each inference requiring only approximately 50 milliseconds.

Abstract

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 differing entanglement structures. We employ family-conditioned residual corrections -- additive, family-specific adjustments atop a shared backbone, drawing on established conditional computation techniques -- enabling the model to capture both universal circuit properties and algorithmic nuances. The architecture incorporates a pretrained family classifier (97.5% accuracy) and domain-informed algorithm fingerprint features derived from gate-composition heuristics. Evaluated on circuits spanning 7--130 qubits across 10 algorithm families, our system achieves 79.5% exact threshold accuracy (91.2% within one rung) and $R^2 = 0.82$ runtime correlation, with inference completing in approximately 50 ms -- replacing trial-and-error simulation runs that may take minutes to hours. Ablation studies confirm that family-aware modeling provides the single largest performance improvement (+3.2 percentage points), validating the hypothesis that algorithm family is a first-class feature for simulation cost prediction.

Problem

• quantum circuit simulation
• approximation parameters
• bond dimension threshold
• simulation cost prediction
• algorithm family

Innovation

• family-aware modeling
• residual architecture
• quantum circuit simulation
• tensor-network approximation
• algorithmic family classification