Fintech
Since the early days of computer development to the recent period of booming artificial intelligence and quantum technology, the financial industry has been at the forefront of actively embracing cutting-edge technology. With quantum computing, there is significant economic value in improving computational speed and accuracy for a wide range of quantitative analysis tasks that are widely available in the financial industry. Based on noise-containing, medium-scale quantum computing and all-optical deep learning acceleration chips, it can already be applied to a wide range of scenarios such as portfolio optimisation, risk analysis, credit rating and high-frequency trading, providing high-performance solutions for specific financial applications
AI
Artificial intelligence is an important bridge for light quantum computing to empower a hundred industries. Technology giants such as Google and Amazon continue to invest in R&D in the direction of quantum artificial intelligence to enable general AI and general quantum computing. Quantum computing development tools and quantum machine learning tools are the entry tickets. TorchQ is the classical-quantum hybrid machine learning framework in the TuringQ framework, which is very friendly to traditional machine learning practitioners. At this stage, applications of quantum AI are dominated by quantum kernel methods based on quantum circuits empowering traditional AI models, such as QGAN, QLSTM, QRL, QCNN, etc. Other new quantum kernel approach AI models and compilation frameworks will be open for use soon
Biomedical
The intersection of biomedical and computational sciences is an important direction where quantum computing can demonstrate its powerful arithmetic and make an impact. After years of development, computational biology and computational chemistry are entering a turning point where the combination with quantum computing facilitates the development of new computational theoretical approaches, employing quantum computing to solve combinatorial optimisation problems in the field, such as pharmacophore and mRNA reverse translation. At the same time, quantum artificial intelligence will enable structure generation, protein-protein interactions (PPI), virtual screening, inverse synthesis, genomics and targeted therapeutics, and even enable process simulation and property prediction in ultra-large scale systems such as cells
Big Data
Big Data has contributed to the industrial revolution of the Digital Age. Specific subfields such as Big Data for Biology, Big Data for Finance, Big Data for Materials and Big Data for Transport have technical commonalities, such as sorting and Information Search, Data Mining, algorithmic recommendation and a wide range of other optimisation problems. Quantum computing and optical quantum chip technologies have significant advantages in NP problems such as combinatorial optimisation. Quantum PageRank is used for the processing of aviation big data and has demonstrated its quantum algorithmic advantages. Quantum reinforcement learning algorithms in combination with chips for phase change process search in material big data. Existence of optimisation models in RNA gene data that can be used for QUBO problem representation and chip solving. A quantum machine learning framework and a highly collaborative CPU/QPU based computing architecture will enable a new wave of big data brought about by 5G
