Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing

2026-06-11 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Quantum Computing · Depth: Expert, medium

Summary

A calibration-aware graph reinforcement learning (GRL) router significantly improves quantum circuit fidelity on noisy intermediate-scale quantum processors by considering real-time hardware calibration data. This router, trained with Proximal Policy Optimization (PPO), selects hardware-edge SWAPs using same-day IBM Heron r2 calibration data to mitigate fidelity loss from poorly calibrated couplers. Evaluated across nine Munich Quantum Toolkit (MQT) Bench circuits and three calibration snapshots, the GRL router achieved a pooled mean exact fidelity of 0.727. This performance surpasses traditional methods like SABRE-best20 (0.440) and target-aware SABRE (0.481). While fidelity gains were concentrated in 5q and 8q circuit families, 10q families under a fixed tree action graph still favored SABRE-best20. These results demonstrate that calibration-aware learned routing can enhance fidelity beyond compilation strategies focused solely on gate counts.

Key takeaway

For Quantum Software Engineers optimizing circuit compilation on noisy intermediate-scale quantum processors, you should integrate real-time hardware calibration data into your routing algorithms. Relying solely on gate-count metrics can lead to significant fidelity loss. By adopting graph reinforcement learning approaches, like those trained with PPO, you can achieve substantial fidelity improvements, particularly for 5q and 8q circuits, ensuring more robust quantum program execution on current hardware.

Key insights

Integrating real-time hardware calibration into quantum circuit routing via graph reinforcement learning significantly enhances circuit fidelity.

Principles

• Quantum circuit fidelity on NISQ devices benefits from calibration-aware routing.
• Reinforcement learning can optimize routing beyond simple gate-count metrics.

Method

Train a graph reinforcement learning policy with Proximal Policy Optimization (PPO) to select hardware-edge SWAPs, informed by same-day hardware calibration data.

In practice

• Apply PPO to develop calibration-aware quantum circuit routers.
• Incorporate real-time hardware calibration data into quantum compilation.
• Focus calibration-aware routing on 5q and 8q circuits for fidelity gains.

Topics

• Graph Reinforcement Learning
• Quantum Circuit Routing
• Quantum Compilation
• NISQ Processors
• Hardware Calibration
• Proximal Policy Optimization

Code references

• Aqasch/Gadget_RL

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.