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Quantum-Classical Hybrid Models Tackle Time-Series Forecasting on NISQ Hardware

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new quantum-classical hybrid frameworks for multivariate time-series forecasting, designed to operate on near-term noisy intermediate-scale quantum (NISQ) hardware. These frameworks, including the Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F), transform time-series data into quantum states and utilize entanglement to capture inter-variable dependencies. Experiments show that these models can offer improved training stability, parameter efficiency, and robustness to quantum noise compared to classical methods, with potential applications in various forecasting tasks. AI

IMPACT These quantum-classical hybrid models could offer new capabilities for complex time-series forecasting tasks, potentially improving accuracy and efficiency on specialized hardware.

RANK_REASON The cluster contains two arXiv papers detailing novel research in quantum-classical hybrid frameworks for time-series forecasting.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Quantum-Classical Hybrid Models Tackle Time-Series Forecasting on NISQ Hardware

COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique ·

    QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

    arXiv:2605.18333v2 Announce Type: replace-cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings. This work adapts the Quantum Leaky Inte…

  2. arXiv cs.AI TIER_1 English(EN) · Sanjay Chakraborty, Fredrik Heintz ·

    A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

    arXiv:2607.16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F). The propos…

  3. arXiv cs.LG TIER_1 English(EN) · Wissal Hamhoum, Soumaya Cherkaoui, Jean-Frederic Laprade, Ola Ahmad, Shengrui Wang ·

    Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware

    arXiv:2510.13634v2 Announce Type: replace Abstract: Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints. This work introduces a gate-based QRC for multiv…

  4. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Fredrik Heintz ·

    A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

    This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F). The proposed framework investigates the complexity-fidelity …

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