Quantum AI Company:
Empowering AI with the inherent parallelism of Quantum Computing
Why Quantum Computing?
The power of quantum computing derives from inherent parallelism deriving from the superposition property of quantum mechanics. Academic and industry researchers around the world are building quantum communications, quantum computers, quantum memory, and quantum sensing. Quantum Economy is projected to grow to $1 trillion by 2035, as per The Quantum Insider.
In the quantum realm, all possible states coexist simultaneously, each represented by a probability amplitude expressed as a complex coefficient. This property is called superposition, which allows for inherent parallelism in a higher-dimensional computational space. This is a fundamental shift from classical computation, where information is confined to binary states of 0 or 1 processed sequentially. By harnessing this parallelism and higher-dimensionality, quantum computing significantly enhances efficiency, making it ideal for solving complex problems.
Nature is continuous, not binary.
Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical, and by golly it's a wonderful problem, because it doesn't look so easy.
Richard Feynman
Theoretical physicist
We are using the binary system in digital computing because of the ON and OFF switches of transistors.
It is a hardware constraint that does not apply to the quantum world.
Quantum AI
Quantum Neural Network (QNN) algorithms exhibit a substantially reduced number of parameters to train. Using QNNs as foundation, AI tools enhanced by quantum advantage can be built such as Quantum Transformers and Quantum Language Models, resulting in huge savings in compute-resource requirements and energy consumption. They can be used in various industry verticals.
Finance & Banking
- Risk analysis and management
- Portfolio optimization and High frequency trading (HFT)
- Fraud detection and loan default prediction
- Trading strategies with enhanced algorithms
Pharmaceutical and Healthcare
- Drug discovery
- Optimizing clinical trials
- Enhancing diagnostics
- Personalized treatment plans
Energy and Utilities
- Energy grid optimization
- Improving renewable energy forecasting
- Enhancing battery storage and materials design
- Nuclear fusion and power plant optimization
Automotive
- Traffic flow optimization
- New battery material discovery
- Human-Machine Interaction (HMI)
- Crash simulation and testing
Manufacturing
- Novel material discovery
- Supply chain optimization
- Running complex simulations
- Autonomous robotics and automation
Defense and Aerospace
- Optimizing flight paths and fuel efficiency
- Improving navigation systems
- Simulating aerodynamics
- Enhancing cybersecurity
Quantum AI Solutions
With the rise of revolutionary AI solutions, the demand for computational resources and energy consumption grows at an alarming rate. The reduction in the number of parameters to train inQuantum AI allows for the compactificationof existing models.
Quantum Neural Networks
Used for classification and regression
Quantum Transformers
Used for sequential data generation
Quantum Large Language Models
Substantial reduction of parameters
Research Scientists and industry experts from
top institutions believe in our vision and mission
