Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks

2025-05-11

AI Summary

Quantum–neural network integration remains challenging in the NISQ era due to severe hardware noise, short coherence times, and high error rates. Method: We propose a noise-resilient Quantum Echo State Network (QESN) and deploy it end-to-end on the IBM Marrakesh superconducting quantum processor. Our approach leverages classical control-theoretic response analysis to quantify QESN’s nonlinear dynamics and memory capacity; introduces sparsification and re-uploading modules to enable tunable quantum neural dynamics; and incorporates noise-robust training strategies to enhance generalization. Contribution/Results: Experiments demonstrate that the QESN achieves long-term state prediction of the Lorenz chaotic system directly on real hardware—forecasting over a time horizon 100× longer than the average qubit T₁/T₂ coherence times. To our knowledge, this represents the state-of-the-art performance for time-series forecasting on current superconducting quantum hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficiency. Quantum computers hold the potential to overcome these limitations and increase processing power beyond classical systems. Despite this, integrating quantum computing with NNs remains largely unrealized due to challenges posed by noise, decoherence, and high error rates in current quantum hardware. Here, we propose a novel quantum echo-state network (QESN) design and implementation algorithm that can operate within the presence of noise on current IBM hardware. We apply classical control-theoretic response analysis to characterize the QESN, emphasizing its rich nonlinear dynamics and memory, as well as its ability to be fine-tuned with sparsity and re-uploading blocks. We validate our approach through a comprehensive demonstration of QESNs functioning as quantum observers, applied in both high-fidelity simulations and hardware experiments utilizing data from a prototypical chaotic Lorenz system. Our results show that the QESN can predict long time-series with persistent memory, running over 100 times longer than the median T}1 and T2 of the IBM Marrakesh QPU, achieving state-of-the-art time-series performance on superconducting hardware.

Problem

• Overcoming noise and error in quantum neural networks
• Predicting chaotic states with quantum echo-state networks
• Enhancing quantum computing scalability for time-series tasks

Innovation

• Quantum echo-state network design for noisy hardware
• Classical control-theoretic analysis enhances QESN dynamics
• Long time-series prediction on IBM quantum hardware