Data Storage
A computer with N qubits stores 2N data for mathematical operations. A 250-qubit memory may store up to 2250 data, outnumbering all atoms existing in the known universe.
Industry Background
The big data technology is leaping forward, asking for higher computing efficiency. Generally speaking, big data has a large volume, comes in various types, can be quickly processed and is of low value density. The explosive growth of big data and the limitation of Moore's Law pose huge challenges to classic computers, whereas quantum computing opens a new horizon for big data development with its unique characteristics. The advantage of applying quantum computing to big data lies in that it breaks the computational limit of the Turing Machine, which not only reduces the complexity of problems, but also improves computing efficiency and computing performance exponentially.
Application Scenarios
Data Storage
A computer with N qubits stores 2N data for mathematical operations. A 250-qubit memory may store up to 2250 data, outnumbering all atoms existing in the known universe.
Data Search
It takes the traditional algorithm O(N) time to process an unordered database with N elements, while quantum algorithm can finish the task in O(√N) time. For an unordered database with 100 million elements, it only takes 10,000 operations to retrieve the target value.
Cluster Analysis
Two methods are available for optimizing clustering analysis in a quantum manner, including the classical clustering algorithm accelerated by the Grover algorithm, and the quantum dynamics clustering analysis developed from the principles of quantum mechanics.
Related Products
Cooperation Cases
Hefei Big Data Assets Operation Company
Jointly explore the application of quantum computing in big data based on the OriginQ cloud platform, quantum software, and etc.
Guangdong Hua-shao-shu-zhi Technology Co., Ltd.
Explore and collaborate on the application of quantum computer prototypes, quantum software and algorithms in big data centers.