[Neuqubit Insights] Convergence of Neural Networks and Quantum Computing in Long-Short Strategies
Institutional Research | Neuqubit Labs
Convergence of Neural & Quantum in Long-Short Strategies
Published: Feb 2026 | Report ID: NQ-2026-002 | Topic: Quantum Finance
Executive Summary
Long-short strategies remain the cornerstone of market-neutral alpha generation, profiting from the relative performance of "Long" (strong) vs. "Short" (weak) positions. Recent empirical evidence suggests that Quantum-Enhanced Neural (QEN) frameworks significantly improve predictive accuracy and risk-adjusted returns. Key breakthroughs in cross-sectional forecasting, time-series modeling, and multi-asset optimization have demonstrated Sharpe ratio improvements of 40–70% over classical benchmarks in 2025–2026. This note outlines the practical integration of these technologies in modern strategy development.
1. Cross-Sectional Return Prediction: The Rise of QTCNN
The efficacy of a long-short portfolio hinges on its ability to rank future returns across a broad universe. Quantum Temporal Convolutional Neural Networks (QTCNN) have emerged as a superior alternative to classical architectures. By integrating parameter-efficient quantum convolution circuits, QTCNN leverages superposition to capture high-dimensional, non-linear correlations.
* Benchmark Result: Utilizing JPX Tokyo Stock Exchange data (Chen et al., 2025), QTCNN achieved an out-of-sample Sharpe ratio of 0.538, outperforming Transformers (0.313) by 72%.
* Operational Impact: The model significantly stabilizes the "Long-Top N / Short-Bottom N" spread, reducing drawdowns during high-volatility regimes where classical models typically fail.
2. Hybrid Quantum-LSTM: Advanced Sequence Modeling
While LSTMs are standard for time-series forecasting, Quantum-LSTM (QLSTM)—which integrates Variational Quantum Circuits (VQC) into LSTM gates—is redefining the baseline.
* Evidence: 2025 studies (e.g., BLS-QLSTM) show that delegating non-linear feature extraction to quantum components while retaining classical LSTM for long-term dependencies results in superior directional accuracy.
* Practicality: This hybrid structure is optimized for NISQ (Noisy Intermediate-Scale Quantum) devices, making it a viable tool for current production environments rather than a distant theory.
3. Multi-Asset Optimization via Quantum Multi-Task Learning (QMTL)
Portfolio-level prediction often suffers from the "curse of dimensionality." Quantum Multi-Task Learning (QMTL) and Contextual QNNs address this by using a share-and-specify ansatz (Mourya et al., 2026).
* Efficiency: QMTL allows the simultaneous learning of multiple asset behaviors (e.g., Big Tech basket) within a single quantum circuit. By reducing qubit requirements to a logarithmic scale, it enables more precise modeling of inter-asset correlations, leading to tighter risk control in long-short weight optimization.
4. Dynamic Policy Learning: Quantum Reinforcement Learning (QRL)
For intraday or high-frequency positioning, the fusion of Quantum Neural Evolutionary Strategies and Meta-RL is yielding impressive results.
* Case Study: A 2025 study (Quantum-Enhanced Forecasting for Deep RL) applied a QA3C (Quantum Asynchronous Advantage Actor-Critic) agent to currency pairs (e.g., USD/TWD), delivering an 11.87% cumulative return with a remarkably low 0.92% maximum drawdown. The quantum agent adapts to market regime shifts far faster than its purely classical counterparts.
Conclusion & Implementation Outlook
The "Quantum-Neural" hybrid is no longer a research curiosity; it is the next evolutionary step for quant funds. Based on 2026 industry standards, these models (QTCNN, QLSTM, QMTL) are already undergoing prototype testing at Tier-1 hedge funds.
* Recommendation: Start with a 10–50 stock universe to mitigate NISQ-era noise. We recommend building hybrid prototypes using PennyLane, Qiskit, or TensorFlow Quantum. As fault-tolerant quantum computing matures, the competitive advantage of these architectures will only widen.
References
* Chen et al., Quantum Temporal Convolutional Neural Networks, arXiv:2512.06630 (2025)
* Mourya et al., Contextual quantum neural networks for stock price prediction, Scientific Reports (2026)
* Su et al., BLS-QLSTM for High-Frequency Sequence Modeling, Hum. Soc. Sci. Comms (2025)
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