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Quantum algorithms for simulating quantum dynamics have shown promising results to overcome the difficulties from the classical counterparts. This Perspective summarizes the recent developments in the field, and further discusses the limitations and research opportunities towards the goal of quantum advantage.
Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.
Multi-messenger astronomy offers promises for exploring Universe events in distance. Nevertheless, there are numerous computational challenges when analyzing the massive heterogeneous messenger data from various detectors, creating research opportunities to the community, such as developing multimodal machine learning.
Quantum embedding theory promises the simulation of realistic materials in quantum computers. In this Perspective, challenges and opportunities of applying different embedding frameworks to calculate solid materials properties are discussed, with a focus on electronic structures of spin defects.
Machine learning has been used to accelerate the simulation of fluid dynamics. However, despite the recent developments in this field, there are still challenges to be addressed by the community, a fact that creates research opportunities.
There is still a wide variety of challenges that restrict the rapid growth of neuromorphic algorithmic and application development. Addressing these challenges is essential for the research community to be able to effectively use neuromorphic computers in the future.
