Automating quantum feature map design via large language models

• URL: http://arxiv.org/abs/2504.07396v1
• Date: Thu, 10 Apr 2025 02:27:45 GMT
• Title: Automating quantum feature map design via large language models
• Authors: Kenya Sakka, Kosuke Mitarai, Keisuke Fujii,
• Abstract summary: We propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models.<n> Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention.
• Score: 0.8009842832476994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, designing quantum feature maps that offer practical advantages over classical methods remains an open challenge. In this work, we propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models. The system consists of five component: Generation, Storage, Validation, Evaluation, and Review. Using these components, it iteratively improves quantum feature maps. Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention. The best feature map generated outperforms existing quantum baselines and achieves competitive accuracy compared to classical kernels across MNIST, Fashion-MNIST, and CIFAR-10. Our approach provides a framework for exploring dataset-adaptive quantum features and highlights the potential of LLM-driven automation in quantum algorithm design.

Related papers

• Quantum Transfer Learning for MNIST Classification Using a Hybrid Quantum-Classical Approach [0.0]
  This research explores the integration of quantum computing with classical machine learning for image classification tasks. We propose a hybrid quantum-classical approach that leverages the strengths of both paradigms. The experimental results indicate that while the hybrid model demonstrates the feasibility of integrating quantum computing with classical techniques, the accuracy of the final model, trained on quantum outcomes, is currently lower than the classical model trained on compressed features.
  arXiv  Detail & Related papers  (2024-08-05T22:16:27Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Classical Verification of Quantum Learning [42.362388367152256]
  We develop a framework for classical verification of quantum learning. We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples. Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents.
  arXiv  Detail & Related papers  (2023-06-08T00:31:27Z)
• Feature Map for Quantum Data in Classification [2.2940141855172036]
  A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms.
  arXiv  Detail & Related papers  (2023-03-28T01:17:08Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Re-QGAN: an optimized adversarial quantum circuit learning framework [0.0]
  We propose a quantum generative adversarial network design that uses real Hilbert spaces as the framework for the generative model. We encode classical information by the stereographic projection, which allows us to use the entire classical domain without normalization procedures. This architecture improves state-of-the-art quantum generative adversarial performance while maintaining a shallow-depth quantum circuit.
  arXiv  Detail & Related papers  (2022-08-03T15:52:27Z)
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier [4.40450723619303]
  We show how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC) We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning.
  arXiv  Detail & Related papers  (2021-06-17T12:02:01Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)
• Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces [0.0]
  We study the capability of quantum feature maps in the classification of disjoint regions. Our work enables an important theoretical analysis to ensure that machine learning algorithms based on quantum feature maps can handle a broad class of machine learning tasks.
  arXiv  Detail & Related papers  (2020-09-01T09:09:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.