REVIEW 6 cited by
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.
Forward citations
Cited by 6 Pith papers
-
A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
LSTM trained with a QCBM generative prior beats a classical LSTM on SSE/CSI 300 realized volatility and retains much of that edge under zero-weight inference via Drop-Prior training.
-
Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing lim...
-
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
A graph-neural-network-guided Bayesian search discovers compact variational quantum circuits for a cybersecurity classification task, slightly beating a flat-feature MLP surrogate and dominating fixed-architecture baselines.
-
RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Partial adversarial coverage in simulated quantum federated learning improves small-perturbation robustness with little clean-accuracy loss, but label-sorted non-IID data removes about half the robustness benefit.
-
CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
CircuitHunt screens KetGPT circuits with qubit/parameter filters and 5-epoch macro-F1 scoring, selecting circuit #221 (6 qubits, 9 parameters) that reportedly hits 97% accuracy, but SMOTE-before-split inflates the tes...
-
A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations
A review that maps QGAN architectures, use cases, and hardware demonstrations, with special focus on work from 2023 onward.
Discussion (0). Sign in to comment.