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Quantum machine learning framework synthesizes circuits from gate set tomography data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel quantum machine learning control framework for synthesizing quantum circuits directly from gate-set tomography (GST) data. This approach bypasses traditional methods by learning a generative concept space from GST data, enabling the conditional synthesis of circuits based on a desired output distribution. The framework utilizes a set-vision transformer and a diffusion model to capture device-specific noise and generate context-aware, hardware-native circuits, offering a new paradigm for quantum control and compilation. AI

IMPACT Introduces a new paradigm for quantum control and compilation by directly synthesizing hardware-native circuits from experimental data.

RANK_REASON This is a methodology article proposing a new framework for quantum circuit synthesis published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum machine learning framework synthesizes circuits from gate set tomography data

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This is a methodology article proposing a new framework for quantum circuit synthesis published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · King Yiu Yu, Aritra Sarkar, Erbing Hua, Maximilian Rimbach-Russ, Ryoichi Ishihara, Sebastian Feld ·

    From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

    arXiv:2605.01367v1 Announce Type: cross Abstract: High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine le…

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