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The paper introduces HAML, a meta-learning framework that learns to map control inputs and device parameters to effective Hamiltonian coefficients for superconducting qubits. HAML trains on simulated devices and then adapts online using a few hardware measurements, bypassing the need for perturbation theory. Applied to a transmon-coupler-transmon system, HAML accurately recovers two-qubit coefficients even where Schrieffer-Wolff perturbation theory fails, demonstrating efficient Hamiltonian reduction.
Forget perturbation theory: HAML meta-learns effective qubit Hamiltonians directly from multi-mode simulations, enabling accurate characterization even when traditional methods break down.
We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML proceeds in two phases. A supervised training phase uses an ensemble of simulated devices to learn an offline map from control inputs and device parameters to effective Hamiltonian coefficients. An online adaptation phase then uses a small number of hardware-accessible measurements to identify the unknown parameters of a new device. By training directly against effective two-qubit coefficients extracted from full multi-mode simulations, HAML implicitly learns the reduction from full multi-mode Hamiltonians to effective qubit descriptions without invoking perturbation theory. We further show that a variance-maximizing greedy selection of measurement configurations boosts online adaptation efficiency. We demonstrate HAML on a transmon-coupler-transmon system, recovering effective two-qubit coefficients across a wide range of operating regimes, including parameter regions where Schrieffer-Wolff perturbation theory (SWPT) breaks down. This establishes a scalable, sample-efficient approach to Hamiltonian reduction and characterization for near-term quantum processors, with direct implications for calibration, control, and error mitigation.
