Quantum Explorations

Jun Lin, Yanju Ji, Jing Zhao +2 more

Quantum-based sensing and measurement technology is a disruptive technology that can realize fine detection of the gravity and magnetic fields in deep Earth, and it has become a key development direction for geophysical exploration equipment worldwide. This study focuses on the frontier technologies for the quantum-based high-precision measurement of Earth’s gravity and magnetic fields, summarizes the development status of quantum-based geophysical exploration equipment, and analyzes demand for the superconducting quantum-based electromagnetic detection system, magnetic vector gradient detection system, superconducting gravity detection system, and cold-atom absolute-gravity exploration system during deep resource exploration. Moreover, the international trend of quantum-based high-precision measurement technology as well as the development opportunities and challenges in this field are analyzed. To improve the core technology research, complete localization, and exploration application of quantum-based geophysical exploration equipment in China, we propose the development goals, technical system, key tasks, and strategic planning for a new generation of quantum-based high-precision deep geophysical exploration equipment. This aims to achieve breakthroughs in superconducting quantum chips and high-sensitivity sensors, establish a collaboration model for the independent development of China’s quantum-based geophysical exploration equipment, promote the high-quality development of deep exploration equipment, and provide strategic support for solving major problems associated with deep mineral resource exploration and revealing of Earth’s deep structure.

Lauren Peters

Chao Huang, Yibei Guo, Zhihui Zhu +3 more

With the latest developments in sensors, battery, and Artificial Intelligence (AI) technologies, robots can perform missions in unstructured environments (e.g., disaster sites). However, their adaptation speed is still slow due to their limited onboard computing capability and dynamic disruption from environments, leading to inaccurate planning and delayed emergency reactions. Even though reinforcement learning increases robots’ exploration speed by fusing effective guidance, the numerous interactions make the learning time-consuming and even risky (e.g., collisions). To fundamentally improve robot adaptation speed, this work seeks help from quantum power. A novel Quantum Exploration based Dreamer model (QED) was developed to facilitate reinforcement learning explorations. QED based on stochastic quantum walker quickly explores environments, evaluates action quality, and obtains a global optimal exploration strategy; then, these high-quality exploration samples will be used to facilitate reinforcement learning speed. A theoretical benefit of QED is facilitating reinforcement learning speed without changing the underlying learning architecture, which makes the proposed QED applicable to general robot learning scenarios. To validate QED effectiveness, a robot path-planning task in an obstacle-dense environment was designed. The number of needed training episodes validated the effectiveness. The results show that QED achieves around ten times faster policy learning compared with Monte Carlo tree-based reinforcement learning methods and vanilla reinforcement learning methods.

Jincheng Zhang

Quantum-Inspired Non-Local Calculus – Exploring the Limits of Intuition Quantum-Inspired Non-Local Calculus – Exploring the Limits of Intuition