EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data

2025-03-18

AI Summary

This work addresses key limitations of conventional amplitude encoding (AE) on Noisy Intermediate-Scale Quantum (NISQ) devices—namely, high encoding error rates, inconsistent circuit depth across samples, and noise sensitivity. To mitigate these issues, we propose a low-depth AE framework grounded in symbolic representation and clustering-based mean-state synthesis. Our approach first clusters classical data to identify representative samples, then constructs corresponding quantum states via a customized shallow ansatz. By integrating SWAP network optimization and symbolic quantum state synthesis, the framework enforces uniform circuit topology and depth across all encoded samples, thereby eliminating sample-wise noise variability. Experimental results demonstrate >90% data-mapping fidelity and substantial improvements in robustness and accuracy of quantum machine learning (QML) models under realistic hardware noise. The framework is open-source and designed for scalable deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variable-length circuits that introduce high output error due to extensive gate usage and variable error rates across samples, resulting in noise-driven inconsistencies that degrade model accuracy. We introduce EnQode, a fast AE technique based on symbolic representation that addresses these limitations by clustering dataset samples and solving for cluster mean states through a low-depth, machine-specific ansatz. Optimized to reduce physical gates and SWAP operations, EnQode ensures all samples face consistent, low noise levels by standardizing circuit depth and composition. With over 90% fidelity in data mapping, EnQode enables robust, high-performance QML on noisy intermediate-scale quantum (NISQ) devices. Our open-source solution provides a scalable and efficient alternative for integrating classical data with quantum models.

Problem

• High output error in conventional amplitude embedding methods
• Noise-driven inconsistencies degrading quantum model accuracy
• Scalable and efficient classical data integration in quantum models

Innovation

• Symbolic representation for fast amplitude embedding
• Low-depth, machine-specific ansatz for cluster mean states
• Standardized circuit depth to ensure consistent noise levels