On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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4 results for “QSVM”

L0-Regularized Quadratic Surface Support Vector Machinescs.LG

Proposes sparse QSVM for better generalization and interpretability.

problem Overfitting and difficulty in interpreting full quadratic classifiers.

method Enforces

\ell_0

-norm constraint to promote sparsity and develops a penalty decomposition algorithm.

result The proposed model enhances generalization and produces sparse solutions.

QSVM Quadratic Support Vector Machines L0-norm Sparsity Overfitting

2025-01-20abs ↗pdf ↗

Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makersq-fin.ST

Study compares quantum and classical ML in crypto trading, finding hybrid models outperform.

problem Comparing quantum and classical machine learning in crypto trading strategies.

method Backtesting 10 models across multiple crypto assets using classical ML, quantum ML, hybrid models, and transformer models.

result Hybrid quantum models achieve superior performance with 13.99% return and 1.76 Sharpe ratio.

Quantum Machine Learning Classical Machine Learning Automated Market Makers Decentralized Finance Crypto Trading

2025-09-14abs ↗pdf ↗

Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Financeq-fin.ST

Study benchmarks classical models over quantum in DeFi yield prediction.

problem Accurate yield and performance forecasting for DeFi liquidity allocation.

method Benchmarked six models on Curve Finance pools' historical data.

result Classical models, especially XGBoost, outperform quantum models.

DeFi Yield Prediction Curve Finance Quantum Machine Learning Classical Models

2025-07-22abs ↗pdf ↗

The Potential of Quantum Techniques for Stock Price Predictionq-fin.CP

Quantum algorithms improve stock price prediction accuracy.

problem Improving stock price prediction accuracy using quantum techniques.

method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.

result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.

Quantum Computing Stock Price Prediction Quantum Support Vector Machine Feature Selection Dimensionality Reduction

2023-08-25abs ↗pdf ↗