Curated by RSF Research Staff
New progress in Quantum Machine Learning
The increase in computational power and data availability has led machine learning techniques to impressive results in various fields like regression, classification and data-generation. Despite these successes, classical supercomputers are struggling with the complexity of some problems and quantum computation seems to become a viable solution to speed-up things. Machine learning is one field where quantum algorithms can outmatch the classical approach and quantum resources are expected to provide advantages for learning problems. In fact, learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, also need to be addressed.
Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications, which can range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosis and even plastic surgery, to pure applied physics, for studying the nature of materials, matter or even complex quantum systems. One of these techniques is the Boltzmann machine. It serves as the basis of powerful deep learning models such as deep belief networks and deep Boltzmann machines. Its particularities reside in a probabilistic network of binary units with a quadratic energy function.
In a recent study, an international team of researchers lead by Jacob Biamonte from Skoltech/IQC proposed a review of the actual status of classical machine learning and quantum machine learning. In their paper, they explored the various approaches like the quantum Boltzmann machine. Their conclusion is that quantum computers may outperform classical computers on machine learning tasks. They showed various ways on how to devise and implement quantum software that could enable machine learning. In fact, recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable. This is such a rich subject. There are still many possible ways and many ways need to be explored.
"Writing this paper was quite a challenge: we had a committee of six co-authors with different ideas about what the field is, where it is now, and where it is going. We rewrote the paper from scratch three times. The final version could not have been completed without the dedication of our editor, to whom we are indebted."
Peter Wittek, Institute for Quantum Computing, Canada
Continue reading at: https://phys.org/news/2017-09-quantum-machines.html