paper-with-me

홈 › Papers

Quantum device fine-tuning using unsupervised embedding learning

2020-01-13 · N. M. van Esbroeck, D. T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L. C. Camenzind, L. Yu, D. M. Zumbühl, G. A. D. Briggs, D. Sejdinovic, N. Ares

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

📄 PDF Abstract BibTeX arXiv:2001.04409

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

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

2020-01-08 · H. Moon, D. T. Lennon, J. Kirkpatrick, N. M. van Esbroeck 외

Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explored in order to find the optimal operati…

BIG-bench Machine Learning

Data needs and challenges for quantum dot devices automation

2023-12-21 · Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews, Jared Benson 외

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer …

Benchmarking

Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware

2026-04-27 · Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik arxiv

In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply…

Transfer Learning

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

2021-07-27 · B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau 외

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability…

BIG-bench Machine Learning

Automated extraction of capacitive coupling for quantum dot systems

2023-01-20 · Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans 외

Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operatio…