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International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT)

Hybrid Variational Quantum Circuits Based Temperature Forecasting with Low-Latency FPGA Inference

Nadeem khan Nadeem khan, Shivanshu Siyanwal, Karan Islur Karan Islur, Abhishek Tiwari, Himanshu Gupta Himanshu Gupta

Published Apr 1, 2026• 0 citations
Publisher
DOI
10.1109/tqcebt67648.2026.11681266

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A hybrid quantum–classical learning architecture for noise-aware inference in distributed systems

Muhammad Inam Ul Haq, Khan Bahadar Khan, Zahid Rashid Sheikh +1 more

This paper presents Q-INTEL, a hybrid quantum–classical architecture for noise-aware inference in distributed cyber-physical systems. It integrates classical feature processing, variational quantum circuits (VQCs), classical optimisation, and edge–cloud orchestration within a five-layer pipeline. Q-INTEL does not introduce new quantum primitives; instead, it combines established encoding, variational-circuit, parameter-shift, and optimisation methods to analyse accuracy, robustness, latency, energy, and distributed operation. Evaluation is conducted entirely through noise-calibrated classical simulation using parameters representative of IBM Eagle-class superconducting devices, with no execution on physical quantum hardware. Latency and energy are analytically modelled rather than directly measured, so energy results represent relative indicators rather than physical consumption. The framework distinguishes batch-level service latency from per-inference analytical latency and identifies metrics as simulation-derived, modelled, or architectural. A quantum key distribution (QKD) synchronisation interface is included only as an architectural component and is not experimentally evaluated. Under the stated assumptions, Q-INTEL achieves favourable accuracy, robustness, latency, and energy-index results compared with the considered CNN and QNN baselines. These findings provide preliminary evidence of architectural feasibility but do not demonstrate quantum advantage. Future work requires physical-hardware validation, stronger hybrid baselines, comprehensive statistical analysis, and testing on domain-specific distributed-sensing datasets to assess practical performance under realistic deployment conditions comprehensively.

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Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic ``Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of ``quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.

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Hybrid quantum-classical algorithms rely on iterative data exchange between quantum circuit execution and classical post-processing. As the quantum circuit size increases, communication and computation latencies within these exchanges become a critical system bottleneck. In this work, we present a system-level latency characterization of a hybrid quantumclassical learning workflow implemented on a cost-effective embedded system-on-chip (SoC). We evaluate a heterogeneous execution model where the quantum forward pass is executed on either local simulator or actual Quantum Processing Unit (QPU), while classical post-processing functions such as loss computation, gradient estimation, and parameter updates are accelerated on a low-latency Field-Programmable Gate Array (FPGA) pipeline. We measure end-to-end iteration latency for quantum machine learning circuits ranging from 4 to 14 qubits, investigating CPU-FPGA and QPU-FPGA workflows. Our findings provide a performance blueprint for future design and integration of low-latency embedded systems with utility-scale quantum computing systems.
This paper presents Q-INTEL, a hybrid quantum–classical architecture for noise-aware inference in distributed cyber-physical systems. It integrates classical feature processing, variational quantum circuits (VQCs), classical optimisation, and edge–cloud orchestration within a five-layer pipeline. Q-INTEL does not introduce new quantum primitives; instead, it combines established encoding, variational-circuit, parameter-shift, and optimisation methods to analyse accuracy, robustness, latency, energy, and distributed operation. Evaluation is conducted entirely through noise-calibrated classical simulation using parameters representative of IBM Eagle-class superconducting devices, with no execution on physical quantum hardware. Latency and energy are analytically modelled rather than directly measured, so energy results represent relative indicators rather than physical consumption. The framework distinguishes batch-level service latency from per-inference analytical latency and identifies metrics as simulation-derived, modelled, or architectural. A quantum key distribution (QKD) synchronisation interface is included only as an architectural component and is not experimentally evaluated. Under the stated assumptions, Q-INTEL achieves favourable accuracy, robustness, latency, and energy-index results compared with the considered CNN and QNN baselines. These findings provide preliminary evidence of architectural feasibility but do not demonstrate quantum advantage. Future work requires physical-hardware validation, stronger hybrid baselines, comprehensive statistical analysis, and testing on domain-specific distributed-sensing datasets to assess practical performance under realistic deployment conditions comprehensively.
Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic ``Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of ``quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.