Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage

arXiv:2607.20168 · q-fin.PR, quant-ph, stat.ML · Submitted 2026-07-22 · Read on arXiv

Junchi Shen

q-fin.PR, quant-ph, stat.ML

Submitted: 2026-07-22

Comments: 16 pages, 2 figures, 5 tables. Code and data pipeline available on request

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 20× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Kernel Ridge Regression — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Entanglement — 2× in this paper · explained in 4 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Supervised quantum machine learning models are kernel methods

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