Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering

Earth (MDPI). 2024. Vol. 5. No. 3. P. 491–547.

Rodriges Zalipynis R. A.

Earth data is essential for global environmental studies. Many Earth data types are naturally
modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems
for tensor-related workloads and can be especially helpful for Earth data engineering, which takes
up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and
new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential
speedups when applied to many toughest Array (Tensor) DBMS challenges stipulated by classical
computing and real-world Earth data use-cases. We also propose new types of charts: Quantum
Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how
multiple operations occur simultaneously across different data items and what are the input/output
data dependencies between these operations. Unlike traditional Gantt charts, which typically track
project timelines and resources, QGantt charts integrate specific data items and operations over
time. A Quantum Network Diagram combines several QGantt charts to show dependencies between
multistage operations, including their inputs/outputs. By using a static format, QGantt charts and
Quantum Network Diagrams allow users to explore complex processes at their own pace, which can
be beneficial for educational and R&D purposes.

Keywords: квантовые вычисления, data processing, data management, climate reanalysis, data model, Earth remote sensing, array (tensor) operations, database indexing, Array (Tensor) DBMS, Quantum Computing, quantum memory, exponential speedup, research, development, and education, Растровые (Тензорные) СУБД