Selected work

Representative Papers

Some Experiments with Twee-Style Goal-Directedness

Jul 29, 2026

This work addresses the inefficiency of clause selection in saturation-based automated theorem proving by introducing a novel goal-directed clause selection strategy. The approach extends, for the first time, the idea of prioritizing clauses that share terms with the conjecture—originally employed in the Twee system—to full first-order logic. To achieve this generalization, the authors devise a term-sharing analysis mechanism that does not rely on equality-specific definitions. Experimental results demonstrate that the proposed strategy significantly enhances both the solving efficiency and success rate of the prover, thereby confirming its effectiveness and potential in general first-order logical reasoning scenarios.

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Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

Jul 27, 2026

This study investigates whether quantum computing can surpass classical conditional restricted Boltzmann machines (CRBMs) in time series forecasting and examines the existence of a quantum advantage under fair hyperparameter configurations. To this end, the authors construct and uniformly evaluate four conditional energy-based models: the classical CRBM, a hybrid quantum-classical QCRBM, a fully quantum QQRBM, and a QFeatureQRBM incorporating lagged features. They derive, for the first time, the conditional distributions and training gradients for these models and employ a symmetric hyperparameter grid search to ensure equitable comparison. Experiments on Gaussian process and NARMA-10 benchmarks reveal no significant performance difference between the hybrid QCRBM and the classical CRBM, while fully quantum models perform worse. No systematic quantum advantage is observed, though a marginal benefit cannot be ruled out due to limited sample sizes. This work establishes a comparable framework and theoretical foundation for applying quantum energy models to time series prediction.

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Learning Temporal Patterns in Financial Time Series: A Comparative Study of Quantum LSTM and Quantum Reservoir Computing

May 04, 2026

This study investigates how quantum computing can enhance the accuracy and robustness of financial time series forecasting, particularly in multivariate settings with strong interdependencies. The authors propose a hybrid quantum-classical architecture based on amplitude encoding and systematically compare two quantum models—Quantum Long Short-Term Memory (QLSTM) and Quantum Reservoir Computing (QRC)—augmented with lagged embedding and variational parameter optimization. Empirical evaluation on real-world financial data demonstrates, for the first time, that with carefully designed lag structures and amplitude encoding, both quantum models match the performance of classical LSTM in univariate tasks and achieve modest yet consistent improvements in multivariate scenarios. These findings underscore the critical role of data encoding strategies and dynamic model structure in effective quantum temporal modeling.

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Recent publications

Latest Papers

Some Experiments with Twee-Style Goal-Directedness

Jul 29, 2026

This work addresses the inefficiency of clause selection in saturation-based automated theorem proving by introducing a novel goal-directed clause selection strategy. The approach extends, for the first time, the idea of prioritizing clauses that share terms with the conjecture—originally employed in the Twee system—to full first-order logic. To achieve this generalization, the authors devise a term-sharing analysis mechanism that does not rely on equality-specific definitions. Experimental results demonstrate that the proposed strategy significantly enhances both the solving efficiency and success rate of the prover, thereby confirming its effectiveness and potential in general first-order logical reasoning scenarios.

0 citationsRead paper

Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

Jul 27, 2026

This study investigates whether quantum computing can surpass classical conditional restricted Boltzmann machines (CRBMs) in time series forecasting and examines the existence of a quantum advantage under fair hyperparameter configurations. To this end, the authors construct and uniformly evaluate four conditional energy-based models: the classical CRBM, a hybrid quantum-classical QCRBM, a fully quantum QQRBM, and a QFeatureQRBM incorporating lagged features. They derive, for the first time, the conditional distributions and training gradients for these models and employ a symmetric hyperparameter grid search to ensure equitable comparison. Experiments on Gaussian process and NARMA-10 benchmarks reveal no significant performance difference between the hybrid QCRBM and the classical CRBM, while fully quantum models perform worse. No systematic quantum advantage is observed, though a marginal benefit cannot be ruled out due to limited sample sizes. This work establishes a comparable framework and theoretical foundation for applying quantum energy models to time series prediction.

0 citationsRead paper

Learning Temporal Patterns in Financial Time Series: A Comparative Study of Quantum LSTM and Quantum Reservoir Computing

May 04, 2026

This study investigates how quantum computing can enhance the accuracy and robustness of financial time series forecasting, particularly in multivariate settings with strong interdependencies. The authors propose a hybrid quantum-classical architecture based on amplitude encoding and systematically compare two quantum models—Quantum Long Short-Term Memory (QLSTM) and Quantum Reservoir Computing (QRC)—augmented with lagged embedding and variational parameter optimization. Empirical evaluation on real-world financial data demonstrates, for the first time, that with carefully designed lag structures and amplitude encoding, both quantum models match the performance of classical LSTM in univariate tasks and achieve modest yet consistent improvements in multivariate scenarios. These findings underscore the critical role of data encoding strategies and dynamic model structure in effective quantum temporal modeling.

0 citationsRead paper