Publisher Correction: Efficiently measuring a quantum device using machine learning

When we aim to demonstrate that a programmable quantum device can solve complex problems which cannot be addressed by classic computers, this fundamental goal is known as quantum supremacy. This concept has changed every fundamental rule of computation. In this chapter, the detailed concept of quantum computing and quantum supremacy is explained along with various open source tools and real-time applications of this technology. The major base concepts, quantum computing, the difference between classical and quantum computer on physical level, programing quantum device, and the experiment-quantum supremacy are explained conceptually. This chapter also includes an introduction of the tools Cirq and OpenFermion plus the applications like quantum simulation, error mitigation technique, quantum machine learning, and quantum optimization, which are explained with illustrations.

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Machine learning enables completely automatic tuning of a quantum device faster than human experts

Nature Communications ◽

10.1038/s41467-020-17835-9 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

H. Moon ◽

D. T. Lennon ◽

J. Kirkpatrick ◽

N. M. van Esbroeck ◽

L. C. Camenzind ◽

...

Keyword(s):

Machine Learning ◽

Automatic Tuning ◽

Quantum Device

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Efficiently measuring a quantum device using machine learning

npj Quantum Information ◽

10.1038/s41534-019-0193-4 ◽

2019 ◽

Vol 5 (1) ◽

Cited By ~ 9

Author(s):

D. T. Lennon ◽

H. Moon ◽

L. C. Camenzind ◽

Liuqi Yu ◽

D. M. Zumbühl ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Learning Algorithm ◽

Quantum Computers ◽

Quantum Devices ◽

Full Resolution ◽

Large Numbers ◽

Quantum Device ◽

On Chip ◽

Combining Information

Abstract Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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