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(CA) Adaptive directional gradients for parameterised quantum circuits

LLMs Automate Quantum Circuit Design, New Gradient Estimators Boost Training Efficiency

By PulseAugur Editorial · [4 sources] ·

Researchers have developed an LLM-driven system for autonomously designing quantum circuits, integrating knowledge acquisition, code generation, and experimental feedback. This framework has shown success in constructing quantum feature maps for machine learning and ansatz for variational quantum eigensolvers in quantum chemistry, outperforming classical methods in benchmarks. Separately, a new framework for forward gradient estimators in parameterised quantum circuits has been proposed, significantly improving training efficiency and reducing measurement costs compared to existing methods, enabling training on larger quantum neural networks. AI

IMPACT LLMs are being applied to complex scientific optimization problems, while new gradient estimation techniques promise more efficient training of quantum machine learning models.

RANK_REASON The cluster contains two distinct research papers published on arXiv detailing advancements in quantum circuit design and training efficiency.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

LLMs Automate Quantum Circuit Design, New Gradient Estimators Boost Training Efficiency

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Kenya Sakka, Wataru Mizukami, Kosuke Mitarai ·

    An LLM System for Autonomous Variational Quantum Circuit Design

    arXiv:2606.13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under…

  2. arXiv cs.AI TIER_1 English(EN) · Kosuke Mitarai ·

    An LLM System for Autonomous Variational Quantum Circuit Design

    The design of high performing quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large language models (LLMs) to conduct iterative quantum circuit designs under explicit design constraints. Our system integrate…

  3. arXiv cs.LG TIER_1 (CA) · Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi ·

    Adaptive directional gradients for parameterized quantum circuits

    arXiv:2606.09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and domi…

  4. arXiv cs.LG TIER_1 (CA) · Elham Kashefi ·

    Adaptive directional gradients for parameterized quantum circuits

    Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. …

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