Our team is dedicated to applying Quantum Ising Model with the aim to solve computationally challenging problems efficiently in near term quantum devices or quantum annealing devices. We also focus on investigating of implementations in different next generation quantum processor platforms.
Quantum Portfolio Management
Based on the solution of the knapsnap problem, we aim to apply it to financial portfolio management with fixed amount of capital. We also target to process the large scale portfolio management system with quantum-classical hybrid architectures.
Quantum Monte Carlo Method
Quantum computer provides true random number source for monte carlo simulation. This is main reason we can obtain a reliable solution. Our goal is going to reveal practical ways for quantum algorithms to speed up the Monte Carlo simulation process.
Quantum Machine Learning
Quantum machine learning proposes new types of models that leverage quantum computers’ unique capabilities to, for example,
work in exponentially higher-dimensional feature spaces to improve the accuracy of models.
Researchers have proven theoretically that a Quantum Support Vector Machine can tackle certain classification problems that a classical computer cannot solve efficiently.
Quantum Circuit Generator
There is as yet no robust tool that is optimized to the features of any particular real quantum hardware to translate a high level quantum program into quantum assembly languages. We aim to find a hybrid quantum circuit synthesis method that will combine the advantages of different algorithms through research to translate high level quantum programs to a circuit of quantum gates and logic, which in turn can be implemented on the many low level platforms available today. By developing our own tool, we have the flexibility to exploit the features of different real quantum hardware.