A pioneering study conducted by researchers at the University of Oxford has made significant progress in overcoming a major challenge in the development of quantum devices. By harnessing the power of machine learning, the researchers have tackled the “reality gap,” the discrepancy between predicted and observed behavior in quantum devices, resulting in new possibilities for the advancement of quantum computing.
The groundbreaking findings, published in the journal Physical Review X, hold promising insights for a wide range of applications, from climate modeling to drug discovery. In the field of quantum computing, the scalability and integration of individual quantum devices, known as qubits, are crucial for achieving enhanced functionality in various areas. However, the inherent variability among seemingly identical quantum units has long been a persistent obstacle.
This variability is believed to arise from nanoscale imperfections in the materials used to construct quantum devices. Since these imperfections cannot be directly measured, accurately predicting outcomes becomes extremely challenging. To address this issue, the research team adopted a “physics-informed” machine learning approach.
The lead researcher, Associate Professor Natalia Ares, compared the methodology to improving predictions in a game of “crazy golf” by taking repeated shots using a simulator and machine learning techniques. The researchers collected data by measuring the output current for different voltage settings within an individual quantum dot device. This data was then fed into a simulation that calculated the disparities between the measured and theoretical current values, while accounting for the absence of internal disorder.
By iterating this process for various voltage settings, the simulation successfully identified internal disorder configurations that could explain the observed measurements. This novel approach combines mathematical and statistical techniques with deep learning. Associate Professor Ares explained, “In the crazy golf analogy, it would be equivalent to placing a series of sensors along the tunnel, allowing us to measure the ball’s speed at different points.”
This breakthrough in closing the “reality gap” in quantum devices opens up new horizons for the field of quantum computing and paves the way for more accurate predictions of device performance. The successful application of machine learning techniques in this context has the potential to revolutionize various industries and drive advancements in fields such as climate modeling and drug discovery.
The source of the article is from the blog elblog.pl