Some details
Our goal is to transfer any code that is running on CPU or GPU and transfer it to Quantum Computers for higher efficiency. In this way, we can perform linear algebra faster along with experimenting with accelerated Quantum Neural Networks with trainable continuous parameters. Quantum computers work alongside with classical ones in a way that we tend use a classical computer to represent a quantum circuit to the quantum
computer, the quantum computer runs some cycles on this circuit, and then reports back to us again with a classical bit response. Quantum computers work well for problems that have exponential nature in terms of efficiency. Decision making for companies and the best delivery rout are two good examples of this type where each choice branches out into new possible opportunities. Moreover, quantum computers are mych more efficient in molecular and quantum simulation than classical computers which makes the material design much easier. Recently, many cloud system including Amazon Web Services (AWS), IBM, and D-Wave joined the Quantum game. Our goal here is to perform Quantum Machine Learning (QML) for industries and to fill the gap by building a bridge between classical and quantum world. Quantum computers also provide us with Stochastic Gradient Descent and Nedler- Mead aproaches for optimizations closer to the global minima. This can be effectively done through training classical neural networks to assist the quantum learning peocess to rapidly find approximate optima in the parameter landscape, i.e. meta-learning. Our team is ready to collaborate with you to perform such a beautiful transition from the classical world to the quantum universe where everything is interconnected just like galaxies. Please contact us for consultation and start the migration as soon as possible.