Joseph Hickie

University of Oxford

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Natalia Ares

University of Oxford

Barnaby van Straaten

University of Oxford

Federico Fedele

University of Oxford

Daniel Chrastina

Politecnico di Milano

Andrea Ballabio

Federico II University Hospital

Daniel Jirovec

Delft University of Technology

Lucas Schorling

University of Oxford

Giovanni Isella

Politecnico di Milano

Jonas Schuff

University of Oxford

Georgios Katsaros

Institute of Science and Technology Austria

Cooperative Institutions

University of Oxford

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

CEA Grenoble

Politecnico di Milano

Institut Nanosciences et Cryogénie

Université Grenoble Alpes

Johannes Kepler University of Linz

Institute of Science and Technology Austria

Centre National de la Recherche Scientifique

Polo d’Innovazione di Genomica

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Automated long-range compensation of an rf quantum dot sensor

arXiv (Cornell University) (2023)

Joseph Hickie Barnaby van Straaten Federico Fedele Daniel Jirovec Andrea Ballabio

Charge sensing is a sensitive technique for probing quantum devices, of particular importance for spin qubit readout. To achieve good readout sensitivities, the proximity of the charge sensor to the device to be measured is a necessity. However, this proximity also means that the operation of the device affects, in turn, the sensor tuning and ultimately the readout sensitivity. We present an approach for compensating for this cross-talk effect allowing for the gate voltages of the measured device to be swept in a 1 V x 1 V window while maintaining a sensor configuration chosen by a Bayesian optimiser. Our algorithm is a key contribution to the suite of fully automated solutions required for the operation of large quantum device architectures.

10.48550/arxiv.2310.02135

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Codebase release 1.3 for QArray

SciPost Physics Codebases (2024)

Barnaby van Straaten Joseph Hickie Lucas Schorling Jonas Schuff Federico Fedele

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100 100×100 pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

Codebase

10.21468/scipostphyscodeb.35-r1.3

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Citations (2)

QArray: A GPU-accelerated constant capacitance model simulator for large quantum dot arrays

SciPost Physics Codebases (2024)

Barnaby van Straaten Joseph Hickie Lucas Schorling Jonas Schuff Federico Fedele

Constant (computer programming)

10.21468/scipostphyscodeb.35

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Citations (2)

All rf-based tuning algorithm for quantum devices using machine learning

arXiv (Cornell University) (2022)

Barnaby van Straaten Federico Fedele Florian Vigneau Joseph Hickie Daniel Jirovec

Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.

Reflectometry

10.48550/arxiv.2211.04504

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Citations (1)

Automated long-range compensation of an rf quantum dot sensor

Physical Review Applied (2024)

Joseph Hickie Barnaby van Straaten Federico Fedele Daniel Jirovec Andrea Ballabio

Charge sensing is a sensitive technique for probing quantum devices, of particular importance for spin-qubit readout. To achieve good readout sensitivities, the proximity of the charge sensor to the device to be measured is a necessity. However, this proximity also means that the operation of the device affects, in turn, the sensor tuning and ultimately the readout sensitivity. We present an approach for compensating for this crosstalk effect allowing for the gate voltages of the measured device to be swept in a 1-V × 1-V window while maintaining a sensor configuration chosen by a Bayesian optimizer. Our algorithm will hopefully be a major contribution to the suite of fully automated solutions required for the operation of large quantum device architectures. Published by the American Physical Society 2024

10.1103/physrevapplied.22.064026

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Citations (1)

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

arXiv (Cornell University) (2024)

Barnaby van Straaten Joseph Hickie Lucas Schorling Jonas Schuff Federico Fedele

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a $100 \times 100$ pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

Constant (computer programming)

10.48550/arxiv.2404.04994

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Rapid optimal work extraction from a quantum-dot information engine

arXiv (Cornell University) (2024)

Kushagra Aggarwal Alberto Rolandi Yikai Yang Joseph Hickie Daniel Jirovec

The conversion of thermal energy into work is usually more efficient in the slow-driving regime, where the power output is vanishingly small. Efficient work extraction for fast driving protocols remains an outstanding challenge at the nanoscale, where fluctuations play a significant role. In this Letter, we use a quantum-dot Szilard engine to extract work from thermal fluctuations with maximum efficiency over two decades of driving speed. We design and implement a family of optimised protocols ranging from the slow- to the fast-driving regime, and measure the engine's efficiency as well as the mean and variance of its power output in each case. These optimised protocols exhibit significant improvements in power and efficiency compared to the naive approach. Our results also show that, when optimising for efficiency, boosting the power output of a Szilard engine inevitably comes at the cost of increased power fluctuations.

10.48550/arxiv.2412.06916

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All rf-based tuning algorithm for quantum devices using machine learning

Research Square (Research Square) (2023)

Natalia Ares Barnaby van Straaten Federico Fedele Florian Vigneau Joseph Hickie

Abstract Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements, i.e. without resorting to current measurements. By exploiting their bandwidth and impedance matching, we demonstrate an algorithm that automatically tunes double quantum dots with only radio-frequency measurements. The tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures.

10.21203/rs.3.rs-2757955/v1

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Citations (1)

Correction: QArray: A GPU-accelerated constant capacitance model simulator for large quantum dot arrays

SciPost Physics Codebases (2025)

Barnaby van Straaten Joseph Hickie Lucas Schorling Jonas Schuff Federico Fedele

Constant (computer programming)

10.21468/scipostphyscodeb.35-update-1

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