Authorship attribution and aesthetic evaluation of AI poetry: a case study with Haiku
研究使用多种大型语言模型生成日语俳句，并通过问卷调查评估人类对AI与人创作俳句的区分能力及审美判断，揭示了审美评价与真实作者识别之间的差异。
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
This work addresses the challenge of efficiently preparing ground states on noisy intermediate-scale quantum (NISQ) devices, where manually designed variational imaginary time evolution (VITE) circuits are often limited by circuit depth and gate count. The authors propose the first framework that integrates deep reinforcement learning into the automated design of VITE circuits, formulating a multi-objective optimization problem that simultaneously minimizes energy expectation and circuit complexity. By combining double deep Q-networks (DDQN) with an adaptive thresholding mechanism, the method discovers non-intuitive yet hardware-aware circuit structures. On Max-Cut problems, it reduces gate count and circuit depth by 37% and 43% on average, respectively; for the hydrogen molecule (H₂), it achieves full configuration interaction (Full-CI) accuracy while maintaining significantly shallower circuits.
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Silence Routing: When Not Speaking Improves Collective Judgment
This work addresses the challenge of aggregating subjective preferences in group judgment tasks, where traditional methods such as simple averaging fail to effectively leverage social signals. The authors propose a signal routing framework that dynamically determines whether an individual should report their own preference, estimate others’ preferences, or remain silent. Notably, this approach is the first to formally model silence as an informative mechanism that conveys second-order information about uncertainty or disagreement. Simulations on a music preference dataset demonstrate that, in appropriate contexts, the proposed framework significantly outperforms baseline methods relying solely on individual preferences, with the strategic use of silence playing a crucial role in enhancing the accuracy of collective judgments.
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Error Analysis of Matrix Multiplication Emulation Using Ozaki-II Scheme
This work addresses the limitations of the Ozaki-II scheme, which suffers from degraded accuracy when applied to input matrices with widely distributed eigenvalues and lacks a reliable means to predict the number of low-precision matrix multiplications required to achieve a target accuracy. We present the first rigorous deterministic error analysis framework for this scheme, integrating floating-point error modeling, numerical analysis, and a high-precision simulation technique based on the Chinese Remainder Theorem. This framework elucidates the underlying accuracy behavior of Ozaki-II and enables precise estimation of the requisite number of low-precision operations to meet a specified accuracy threshold. Consequently, our approach significantly enhances the predictability, practicality, and computational efficiency of the Ozaki-II method on AI hardware platforms.
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Nonuniform-Grid Markov Chain Approximation of Continuous Processes with Time-Linear Moments
This work addresses the high-fidelity discretization of continuous-time, continuous-state stochastic processes—such as heat diffusion and geometric Brownian motion—whose first- and second-order moments evolve linearly in time. We propose a novel construction of discrete-time Markov chains on non-uniform spatial grids: transition probabilities are designed via moment recurrence relations, and the grid is adaptively refined to ensure exact matching of the target process’s mean and variance at any user-specified time points. Unlike conventional methods relying on uniform grids or asymptotic moment matching, our approach achieves strict moment preservation for arbitrary finite horizons, thereby significantly improving long-term statistical fidelity. Numerical experiments demonstrate stable and small Wasserstein-1 distance over extended simulation periods, accurately reproducing the prescribed moment dynamics. The method provides an efficient, analytically tractable discretization framework for linear-moment-driven stochastic systems.
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Authorship attribution and aesthetic evaluation of AI poetry: a case study with Haiku
研究使用多种大型语言模型生成日语俳句，并通过问卷调查评估人类对AI与人创作俳句的区分能力及审美判断，揭示了审美评价与真实作者识别之间的差异。
0 citationsRead paper
Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
This work addresses the challenge of efficiently preparing ground states on noisy intermediate-scale quantum (NISQ) devices, where manually designed variational imaginary time evolution (VITE) circuits are often limited by circuit depth and gate count. The authors propose the first framework that integrates deep reinforcement learning into the automated design of VITE circuits, formulating a multi-objective optimization problem that simultaneously minimizes energy expectation and circuit complexity. By combining double deep Q-networks (DDQN) with an adaptive thresholding mechanism, the method discovers non-intuitive yet hardware-aware circuit structures. On Max-Cut problems, it reduces gate count and circuit depth by 37% and 43% on average, respectively; for the hydrogen molecule (H₂), it achieves full configuration interaction (Full-CI) accuracy while maintaining significantly shallower circuits.
0 citationsRead paper
Silence Routing: When Not Speaking Improves Collective Judgment
This work addresses the challenge of aggregating subjective preferences in group judgment tasks, where traditional methods such as simple averaging fail to effectively leverage social signals. The authors propose a signal routing framework that dynamically determines whether an individual should report their own preference, estimate others’ preferences, or remain silent. Notably, this approach is the first to formally model silence as an informative mechanism that conveys second-order information about uncertainty or disagreement. Simulations on a music preference dataset demonstrate that, in appropriate contexts, the proposed framework significantly outperforms baseline methods relying solely on individual preferences, with the strategic use of silence playing a crucial role in enhancing the accuracy of collective judgments.
0 citationsRead paper
Error Analysis of Matrix Multiplication Emulation Using Ozaki-II Scheme
This work addresses the limitations of the Ozaki-II scheme, which suffers from degraded accuracy when applied to input matrices with widely distributed eigenvalues and lacks a reliable means to predict the number of low-precision matrix multiplications required to achieve a target accuracy. We present the first rigorous deterministic error analysis framework for this scheme, integrating floating-point error modeling, numerical analysis, and a high-precision simulation technique based on the Chinese Remainder Theorem. This framework elucidates the underlying accuracy behavior of Ozaki-II and enables precise estimation of the requisite number of low-precision operations to meet a specified accuracy threshold. Consequently, our approach significantly enhances the predictability, practicality, and computational efficiency of the Ozaki-II method on AI hardware platforms.
0 citationsRead paper
Nonuniform-Grid Markov Chain Approximation of Continuous Processes with Time-Linear Moments
This work addresses the high-fidelity discretization of continuous-time, continuous-state stochastic processes—such as heat diffusion and geometric Brownian motion—whose first- and second-order moments evolve linearly in time. We propose a novel construction of discrete-time Markov chains on non-uniform spatial grids: transition probabilities are designed via moment recurrence relations, and the grid is adaptively refined to ensure exact matching of the target process’s mean and variance at any user-specified time points. Unlike conventional methods relying on uniform grids or asymptotic moment matching, our approach achieves strict moment preservation for arbitrary finite horizons, thereby significantly improving long-term statistical fidelity. Numerical experiments demonstrate stable and small Wasserstein-1 distance over extended simulation periods, accurately reproducing the prescribed moment dynamics. The method provides an efficient, analytically tractable discretization framework for linear-moment-driven stochastic systems.
0 citationsRead paper