Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting

arXiv:2610.12148 · cs.LG, quant-ph · Submitted 2026-10-08 · Read on arXiv

cs.LG, quant-ph

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/PennyLaneAI/pennylane

Terminology

• Accuracy — 50× in this paper · explained in 1 episode
• Time Series Forecasting — 25× in this paper · explained in 3 episodes
• Entanglement — 17× in this paper · explained in 3 episodes
• Fidelity — 16× in this paper · explained in 2 episodes
• Quantum Circuits — 5× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Hilbert Space — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Ensemble Approach — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes

Sources

• Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
• Hybrid Quantum Neural Network in High-dimensional Data Classification
• OpenML Benchmarking Suites
• Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
• QVECTOR: an algorithm for device-tailored quantum error correction
• Quantum Machine Learning in Finance: Time Series Forecasting

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks