Major tech companies and start-ups, including Google, are scrambling to develop quantum computers that utilize quantum physics, such as quantum entanglement and superposition, to process at rates far faster than conventional computers. If quantum computers were to become more practical, it would have a major impact on people's lives and the world's industries.
However, the quantum computers of the present and near future, called "Noisy Intermediate-Scale Quantum Computers" (NISQs), have a non-negligible probability of making errors during computation. Therefore, although quantum supremacy has been demonstrated for specific tasks (with no practical application), no definitive applications have been found as of yet.
This makes how we use the available quantum computers intelligently very important, and the creation of quantum algorithms for NISQ devices is essential for their social implementation.
Kosuke Mitarai, Assistant Professor at the Department of Systems Innovation of the Graduate School of Engineering at Osaka University, has proposed the world's first quantum machine learning algorithm for NISQ devices, called Quantum Circuit Learning. His research paper has been cited more than 200 times in two years, and has been adopted as a standard method by the Google quantum machine learning library, TensorFlow Quantum, and the Canadian quantum computing startup, Xanadu's library, PennyLane.
In machine learning, much like deep learning, adjusting the parameters of the neural network is extremely important. Mitarai's "Quantum Circuit Learning" has established a method for adjusting circuit parameters to minimize errors between the output of the circuit and the teaching data in quantum machine learning. The method is designed to work with as small of a quantum computer as possible, even with some noise, and is expected to contribute to practical applications in the present and near future, unlike quantum algorithms that focus on large-scale quantum computers.
He also co-founded QunaSys, a quantum computer software venture, in 2018 as a doctoral student to implement the results of his research in society. He is the Chief Scientific Officer (CSO), and is conducting research and development in quantum secure cloud and quantum chemical computation, in addition to quantum machine learning.