Deep reinforcement learning for efficient measurement of quantum devices

Nguyen, Vu; Orbell, S. B.; Lennon, Dominic T.; Moon, Hyun-Joo; Vigneau, Florian; Camenzind, Leon C.; Yu, Liuqi; Zumbühl, Dominik M.; Briggs, G. Andrew D.; Osborne, Michael A.; Sejdinović, Dino; Ares, Natalia

doi:10.1038/s41534-021-00434-x

Cited by 33 publications

(24 citation statements)

References 53 publications

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“…Many quantum methods have been proposed for image processing [15,29,73,76], image recognition [28,52,53], and object detection [47]. Also, several methods explored the tasks of classification and training a deep neural network [42,54,65]. Recently, Golyanik and Theobalt [37] proposed a practical quantum algorithm for rotation estimation to align two point sets.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan¹,

Sasdelli²,

Suter³

et al. 2022

Preprint

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Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation 1 .

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Section: Related Workmentioning

confidence: 99%

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Doan¹,

Sasdelli²,

Suter³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the past decade, we have seen a series of breakthroughs using Reinforcement Learning (RL, (Sutton & Barto, 2018)) to train agents in a variety of domains, such as games (Mnih et al, 2013;Berner et al, 2019;Silver et al, 2016;Vinyals et al, 2019) and robotics (OpenAI et al, 2018), with successes emerging in real world applications (Bellemare et al, 2020;Nguyen et al, 2021). As such, there has been a surge of interest in the research community.…”

Section: Introductionmentioning

confidence: 99%

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

Parker-Holder,

Rajan,

Song

et al. 2022

Preprint

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The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems, while also limits its full potential. In many other areas of machine learning, AutoML has shown it is possible to automate such design choices and has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey we seek to unify the field of AutoRL, we provide a common taxonomy, discuss each area in detail and pose open problems which would be of interest to researchers going forward.

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“…Moreover, since the number of control voltages grows linearly with the number of quantum dots, hand-tuning their values becomes more and more challenging due to cross-talk between the dots. Only recently did we see the emergence of automatic tuning algorithms, often implemented using machine-learning [6][7][8][9][10][11][12]. These approaches were used only for small arrays, and still lag behind the results achievable via manual tuning.…”

Section: Introductionmentioning

confidence: 99%

Learning Coulomb Diamonds in Large Quantum Dot Arrays

Krause,

Chatterjee,

Kuemmeth

et al. 2022

Preprint

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We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on onedimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.

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Deep reinforcement learning for efficient measurement of quantum devices

Cited by 33 publications

References 53 publications

A Hybrid Quantum-Classical Algorithm for Robust Fitting

A Hybrid Quantum-Classical Algorithm for Robust Fitting

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

Learning Coulomb Diamonds in Large Quantum Dot Arrays

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