Quantum Generator Kernels

2026-01-30

AI Summary

This work addresses the challenge of effectively embedding large-scale real-world data, such as images, into near-term quantum devices to fully harness the potential of quantum kernel methods. To this end, the authors propose the Quantum Generator Kernel (QGK) framework, which integrates general-purpose generative models into parameterized quantum operators via Variational Generator Groups (VGGs), enabling scalable coverage of Hilbert space. The framework employs kernel alignment to optimize training weights, thereby enhancing fidelity to the target data distribution. Unlike existing hybrid architectures that rely on fixed intermediate embeddings, QGK supports flexible, context-aware data embeddings adapted to the input domain. Experimental results demonstrate that QGK outperforms state-of-the-art quantum and classical kernel methods in both projection and classification tasks, highlighting its promise as a general-purpose framework for quantum machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Quantum kernel methods offer significant theoretical benefits by rendering classically inseparable features separable in quantum space. Yet, the practical application of Quantum Machine Learning (QML), currently constrained by the limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware, necessitates effective strategies to compress and embed large-scale real-world data like images into the constrained capacities of existing quantum devices or simulators. To this end, we propose Quantum Generator Kernels (QGKs), a generator-based approach to quantum kernels, comprising a set of Variational Generator Groups (VGGs) that merge universal generators into a parameterizable operator, ensuring scalable coverage of the available quantum space. Thereby, we address shortcomings of current leading strategies employing hybrid architectures, which might prevent exploiting quantum computing's full potential due to fixed intermediate embedding processes. To optimize the kernel alignment to the target domain, we train a weight vector to parameterize the projection of the VGGs in the current data context. Our empirical results demonstrate superior projection and classification capabilities of the QGK compared to state-of-the-art quantum and classical kernel approaches and show its potential to serve as a versatile framework for various QML applications.

Problem

• Quantum Kernel
• NISQ
• Data Embedding
• Quantum Machine Learning
• Scalability

Innovation

• Quantum Generator Kernels
• Variational Generator Groups
• Quantum Kernel Methods
• NISQ
• Kernel Alignment