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Quantum Kernel Bandit Optimization Balances Expressivity and Learnability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for Gaussian process bandit optimization using quantum kernels, specifically addressing challenges in the noisy intermediate-scale quantum (NISQ) era. The study focuses on balancing the expressivity of quantum kernels with their learnability, which can be hindered by high dimensionality and complexity. To tackle this, the team proposes projected quantum kernels and classical kernel approximation techniques that reduce dimensionality while retaining crucial quantum properties. These methods aim to improve sample efficiency and reduce computational overhead for quantum-native applications. AI

IMPACT This research could lead to more efficient and scalable optimization techniques for quantum machine learning applications.

RANK_REASON The cluster contains an academic paper detailing a new method for quantum kernel bandit optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Quantum Kernel Bandit Optimization Balances Expressivity and Learnability

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The cluster contains an academic paper detailing a new method for quantum kernel bandit optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose ·

    Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

    arXiv:2607.01080v1 Announce Type: new Abstract: We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel. This setting is motivated by NISQ-er…

  2. arXiv cs.LG TIER_1 English(EN) · Sharu Theresa Jose ·

    Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

    We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the quantum kernel. This setting is motivated by NISQ-era tasks such as quantum control, state preparati…

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