Quantum Computing
Future-proofing
data analytics and ML operations
Machine learning and data analytics use approximately 100 TWh of energy yearly, the equivalent of total energy consumption on data operations in 2020 and 2% of total global energy consumption.
With raising demand for data processing, companes need to find ways to future-proof their data operations, especially for intense business applications like machine learning, optimization and simulation.
Quantum Computing is the leading technology to ensure software of today will be useful in 20 years.
Networked Quantum
Systems
Quantum systems can naturally be represented as graphs where vertices correspond to quantum subsystems (e.g., qubits) and edges represent interactions or entanglement between them.
Quantum networks, which dictate the communication and interaction between these subsystems, form the basis for quantum data analytics.
This structure allows for the implementation of parameterized quantum circuits that evolve according to the graph topology, facilitating both quantum and classical inference.
terabytes of data in typical business utility graphs
of nodes in a typical use case graph
Data in typical graphs in commercial applications gets updated the time, requiring re-training of models and graph updates
I want to learn more.
Write us a short message or contact us directly for enquiries.
