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20092026
most citedInverse Problem of Cosmic-Ray Electron/Positron from Dark Matter

7 citations · 19 across the 8 of their papers we have counts for

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14 papers · 1 filter

quant-ph2026

Generative Circuit Design for Quantum-Selected Configuration Interaction

Ryota Kemmoku, Qi Gao, Shu Kanno +4

Quantum-selected configuration interaction (QSCI) has emerged as a feasible approach for approximating electronic ground states on noisy quantum devices toward large-system demonst…

quant-ph2026

Auger Spectroscopy via Generative Quantum Eigensolver: A Quantum Approach to Molecular Excitations

Kimberlee Keithley, Shunsuke Yamamoto, Ryota Kenmoku +16

Auger electron spectroscopy, a way of characterizing electronic structure through core-level decay processes, is widely used in materials characterization; however direct calculati…

quant-ph2025

Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays

Kouhei Nakaji, Jonathan Wurtz, Haozhe Huang +4

We introduce the "quantum circuit daemon" (QC-Daemon), a reinforcement learning agent for compiling quantum device operations aimed at efficient quantum hardware execution. We appl…

quant-ph20251 cited

Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver

Shunya Minami, Kouhei Nakaji, Yohichi Suzuki +2

Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabili…

quant-ph202440 cited

Artificial Intelligence for Quantum Computing

Yuri Alexeev, Marwa H. Farag, Taylor L. Patti +25

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extend…

quant-ph2024

Quantum Transformer: Accelerating model inference via quantum linear algebra

Naixu Guo, Zhan Yu, Matthew Choi +5

Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transforme…