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Meta-learning framework HAML aids superconducting qubit Hamiltonian reduction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed HAML (Hamiltonian Adaptation via Meta-Learning), a new framework designed for the rapid online adjustment of effective Hamiltonian models in superconducting quantum processors. This system uses a two-phase approach: initial supervised training on simulated devices to map control inputs to Hamiltonian coefficients, followed by an online adaptation phase that employs minimal hardware measurements to identify device parameters. HAML bypasses traditional perturbation theory by learning direct reductions from complex multi-mode Hamiltonians to simpler qubit descriptions, showing promise for improving calibration and control in quantum computing. AI

IMPACT Introduces a novel meta-learning approach for quantum system characterization, potentially accelerating quantum processor calibration and control.

RANK_REASON Academic paper introducing a new framework for quantum computing.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Meta-learning framework HAML aids superconducting qubit Hamiltonian reduction

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Academic paper introducing a new framework for quantum computing.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Arielle Sanford, Andrew T. Kamen, Frederic T. Chong, Andy J. Goldschmidt ·

    Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning

    arXiv:2604.24912v1 Announce Type: cross Abstract: 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 u…

  2. arXiv cs.LG TIER_1 English(EN) · Andy J. Goldschmidt ·

    Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning

    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 o…

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