University of Sydney Scientists Use Machine Learning To Predict Quantum SystemsBy Chris Brandt, UniversityHerald Reporter
In an era of advanced technological innovations, It is not surprising that machine learning and quantum systems will finally cross each others paths. This is what's happening as scientists from the University of Sydney developed a system using machine learning in predicting quantum systems.
There has been a race to build a quantum computer that actually works. Big tech companies, such as Microsoft and Intel, are putting in billions for research and construction of such powerful machines.
The biggest obstacle in this endeavor, however, is what scientists call as quantum decoherence, a process where quantum systems decay when it comes in contact with its surroundings.
However, quantum physicists from the University of Sydney led by professor Michael J. Biercuk, have found a technique using big data to address this problem. Through this, they were able to predict the breakdown of the quantum systems ahead of time and prevent it from happening.
Biercuk compared quantum systems to the individual components of mobile phones saying that as each of these components are bound to fail, so do quantum systems. Moreover, we have created preventive techniques in every areas of our lives to prevent these failures or mistakes to happen. However, the failures in the quantum systems are more difficult to predict because they are random. Therefore, the main goal is to predict when and how the system will randomly break or disintegrate.
Biercuk and his team turned to machine learning to help them to predict this randomness and stabilize the qubits. Their technique contained enough information to predict the changes in the future giving them time to compensate for the coming changes.
He said that their technique can be used in any qubit and technology, even the superconducting circuits used by a lot of major companies. Biercuk and his team are very excited about the potential capabilities of their quantum systems in the future.