85 citations · 93 across the 6 of their papers we have counts for
5 papers · 1 filter
Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
Thet Htar Su, Shaswot Shresthamali, Masaaki Kondo
A scalable and resource-efficient quantum reinforcement learning framework is presented that eliminates the linear qubit-scaling barrier in multi-step quantum Markov decision proce…
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
Thet Htar Su, Shaswot Shresthamali, Masaaki Kondo
This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical…
Q-gen: A Parameterized Quantum Circuit Generator
Yikai Mao, Shaswot Shresthamali, Masaaki Kondo
Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks
Yikai Mao, Shaswot Shresthamali, Masaaki Kondo
The fidelity of quantum circuits (QC) is influenced by several factors, including hardware characteristics, calibration status, and the transpilation process, all of which impact t…