Speakers
Description
Quantum processors are limited by coherent errors that depend on the circuit context in which each gate operates, a dependence that conventional calibration protocols do not capture because they characterize gates in isolation. We present CARTES (Context-Aware RealTime Error Suppression), a framework that applies model-free multiagent reinforcement learning (MARL) to adapt pulse-level gate parameters to the surrounding circuit context during execution.
Our work features two main contributions:
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In simulations of a six-qubit superconducting chain that is subject to microwave crosstalk and static ZZ coupling, three RL agents calibrate three parallel two-qubit gates. The agents raise the local average gate fidelity of every gate above 0.9979 and recover 87% to 95% of the gap between the context-agnostic baseline and the numerical contextual optimum.
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Experimentally, we integrate the MARL workflow directly into the Quantum Orchestration Platform, which reduces communication and compilation latency while it preserves a flexible parametrization of custom calibrations. This integration enables contextual calibration within minutes of wall-clock time, versus the many hours typical of traditional closed-loop methods.
The method requires no extra controller memory and no extra latency, since a compact classical neural network infers the corrections in real time during circuit execution. Because the framework embeds learning into the control stack, it provides context-dependent error suppression at the cost of standard circuit execution, with direct applications in drift compensation and quantum error correction.
| Topic | Machine learning techniques for experimental quantum information science |
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| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |