Existence and Use of Large Datasets To Address Research Questions for Characterization and Autonomous Tuning of Semiconductor Quantum Dot Devices, 22409-22411 [2023-07814]

Agencies

• DEPARTMENT OF COMMERCE
• National Institute of Standards and Technology

[Federal Register Volume 88, Number 71 (Thursday, April 13, 2023)]
[Notices]
[Pages 22409-22411]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-07814]


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DEPARTMENT OF COMMERCE

National Institute of Standards and Technology


Existence and Use of Large Datasets To Address Research Questions 
for Characterization and Autonomous Tuning of Semiconductor Quantum Dot 
Devices

AGENCY: National Institute of Standards and Technology, U.S. Department 
of Commerce.

ACTION: Notice of workshop; request for comments.

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SUMMARY: The National Institute of Standards and Technology (NIST) is 
seeking input regarding needs and gaps in data-sharing approaches to 
accelerate innovations in using artificial intelligence and machine 
learning techniques to improve the experimental characterization and 
control of semiconductor quantum dot devices. As part of this effort, 
NIST hopes to identify the needs for quantum dot device tuning 
automation, including existing and future quantum dot related datasets 
that may be useful for research, means and methods currently deployed 
for tuning, barriers for advancing the current state of the art 
techniques to enable automation of large quantum dot arrays, and the 
meaningful measures of success for the various stages of 
characterization and control. NIST plans to hold a workshop on July 19-
20, 2023, in conjunction with this notice. The information received in 
response to this notice and during the workshop will inform efforts and 
coordination needed to develop a reference database of experimental and 
simulated data. The reference database will ideally represent the 
various phases of tuning quantum dot devices, along with metrics for 
benchmarking the characterization and control methods for quantum dot 
devices.

DATES: 
    For Comments: Comments must be received by 5:00 p.m. Eastern Time 
on June 12, 2023. Written comments in response to this notice should be 
submitted according to the instructions in the ADDRESSES section below. 
Submissions received after that date may not be considered.
    For Workshop: The in-person Workshop on Advances in Automation of 
Quantum Dot Devices Characterization and Control will be held on July 
19-20, 2023, from 9:00 a.m. to 5:00 p.m. Eastern Time at the National 
Cybersecurity Center of Excellence (NCCoE), 9700 Great Seneca Highway, 
Rockville, MD 20850. Attendees must register at the workshop website by 
5:00 p.m. Eastern Time on June 19, 2023.

ADDRESSES: 
    For Comments: Written comments may be submitted only by email to 
Dr. Justyna Zwolak at [email protected] in any of the following formats: 
ASCII; Word; RTF; or PDF. Please include your name, organization's name 
(if any), and cite ``Automation of Semiconductor Quantum Dot Devices'' 
in the subject line of all correspondence. Comments containing 
references, studies, research, and other empirical data that are not 
widely published should include copies of the referenced materials. All 
comments responding to this document will be a matter of public record. 
Relevant comments will generally be made publicly available at https://www.nist.gov/news-events/events/2023/07/advances-automation-quantum-dot-devices-control as submitted. NIST will not accept comments 
accompanied by a request that part or all of the material be treated 
confidentially because of its business proprietary nature or for any 
other reason. Therefore, do not submit confidential business 
information or otherwise sensitive, protected, or personal information, 
such as account numbers, Social Security numbers, or names of other 
individuals.
    For Workshop: The workshop will be held at NCCoE, 9700 Great Seneca 
Highway, Rockville, MD 20850. Please note admittance instructions under 
the SUPPLEMENTARY INFORMATION section of this notice. To register, go 
to: https://www.nist.gov/news-events/events/2023/07/advances-automation-quantum-dot-devices-control. Additional information about 
the workshop will be available at this web address as the workshop 
approaches.

FOR FURTHER INFORMATION CONTACT: For questions about this notice 
contact Justyna Zwolak or Jacob Taylor by email at [email protected] or 
Justyna Zwolak by phone at (301) 975-0527. Please direct media 
inquiries to NIST's Office of Public Affairs at (301) 975-2762.

SUPPLEMENTARY INFORMATION: 
    Background: Over the past five years, researchers working with 
semiconducting quantum dot devices have begun to take advantage of the 
data analysis tools provided by the field of artificial intelligence 
and, more specifically, supervised and unsupervised machine learning. 
When provided with proper training data, machine-learning-enhanced 
methods may have the flexibility of being applicable to various devices 
without any adjustments or retraining. Moreover, by learning the 
governing rules and dynamics directly from the data, machine learning 
algorithms may be less susceptible to programming errors. However, 
machine learning models typically require large, labeled datasets for 
training, validation, and benchmarking. They also often lack 
information about the reliability of the machine learning prediction. 
Moreover, since the application of machine learning to quantum dot 
tuning, characterization, and control is a relatively new field of 
research, it lacks standardized measures of success. The success rates 
reported in the various publications vary significantly in both the 
level and meaning of the reported performance statistics, making it 
hard (if

[[Page 22410]]

not impossible) to benchmark the proposed techniques against more 
traditional tuning approaches or against one another.
    Through this notice, we seek public comment to identify existing 
large datasets that may be useful for research; identify best practices 
for creating new, large datasets that are valuable for research; 
understand the challenges and limitations that may impact data access; 
and current and future key metrics of performance for the tuning 
methods.
    Request for Comments:
    The following statements are not intended to limit the topics that 
may be addressed. Responses may include any topic believed to have 
implications for the development of auto-tuning methods for 
semiconductor quantum dot devices, regardless of whether the topic is 
included in this document. All relevant responses that comply with the 
requirements listed in the DATES and ADDRESSES sections of this notice 
will be considered.
    NIST seeks input from stakeholders regarding the broadly defined 
needs for automation of quantum dot device characterization and tuning. 
A simple but crucial component of success for the field will be to 
solidify key metrics of performance as well as establish standard 
datasets that can be used to assess those metrics on the newly proposed 
methods and algorithms. Among the simple metrics that have been used to 
date are state identification accuracy (probability of a classifier 
identifying the right topology) and tuning success (probability of the 
navigation algorithm getting to the right region of parameter space). 
However, more such metrics, and associated datasets, will be necessary 
to leverage machine learning algorithms most effectively. So far, 
machine learning efforts for semiconductor quantum dots rely on 
datasets that either come from simulations (and thus may lack important 
features representing real-world noise and imperfections) or are 
labeled manually (and subject to qualitative and/or erroneous 
classification). Moreover, with a few exceptions, these datasets are 
not made publicly available. Yet, systematic benchmarking of tuning 
methods on standardized datasets, analogous to the MNIST or CIFAR 
datasets in the broad machine learning community, is a crucial next 
step on the path to developing reliable and scalable auto-tuners for 
quantum dot devices.
    Through this notice, we seek public comment to initiate a 
community-wide effort to build an open-access data repository for 
benchmarking automated methods for quantum dot devices. To initiate 
such efforts, NIST has provided a starting point: an open dataset, 
QFlow, hosted at the NIST science data portal www.data.nist.gov, that 
includes a large number of simulated measurements as well as a small 
set of experimental scans. A standardized dataset that would enable 
systematic benchmarking of the already existing and new auto-tuning 
methods should represent data from different types of devices. This 
standardization work will take time and community engagement, based on 
experience from other machine learning disciplines. Once 
standardization is in place, more algorithmic exploration and 
improvement can be achieved.
    We invite any member of the public, and specifically those who are 
aware of datasets relevant to auto-tuning quantum dot devices or 
interested in establishing a large open-access database of experimental 
data; those who have perspectives on the value of these datasets for 
research; and those who are aware of challenges and limitations to both 
access and use of large datasets to share their input on the following 
points in their comments:
    (1) Identify public or restricted use datasets related to the 
various phases of tuning semiconductor quantum dot devices that are 
available for training and benchmarking new artificial intelligence 
models or to test hypotheses using data mining/machine learning 
methods. Describe the research needs that are not being met by the 
datasets that are currently available.
    (2) Describe the work researchers need to do to access, and then 
explore the quality of, an existing dataset before conducting research 
with it. Identify what aspects of this work could be reduced or 
conducted just once so that future researchers can reduce the time 
needed to complete a research project.
    (3) Describe promising approaches to testing and improving the 
validity of performance metrics within large datasets, especially those 
datasets that consist of experimental data that does not come with 
ground truth labels.
    (4) Describe whether existing datasets, both simulated and acquired 
experimentally, contain data that are valuable for researchers and are 
of sufficient quality that research could be conducted with a high 
amount of rigor.
    (5) Describe to what extent existing datasets capture enough 
information to address research related to all aspects of tuning 
quantum dot devices. Identify what additional data should be collected 
to address these research questions.
    (6) Describe the best practices for creating new datasets or 
linking existing datasets and sharing them with researchers (open or 
restricted use) while adhering to local, State, and Federal laws. 
Identify barriers and limitations that currently exist.
    (7) Describe what role NIST can play in developing infrastructure 
that supports the use of large-scale datasets for research on tuning 
quantum dot devices
    Workshop:
    The purpose of the workshop is to convene stakeholders from 
industry, academia, and the government interested in the research and 
development of semiconductor quantum computing technologies. Topics to 
be discussed include opportunities for research and development of 
tuning, characterization, and control methods for semiconductor quantum 
dot devices, the need for facilitating interaction and collaboration 
between the stakeholders to build a large open-access database of 
experimental and simulated data for benchmarking new machine learning 
algorithms, determining key performance metrics for the various aspects 
of the tuning, characterizing, and controlling of quantum dot devices, 
and identifying barriers to near-term and future applications of the 
auto-tuning methods. Furthermore, this workshop will provide a 
discussion place to consider methods of collaboration in a neutral 
setting and future roadmap development for methods for tuning large-
scale devices.
    This workshop will focus on addressing the key challenges described 
above under ``Request for Comments.'' It will include invited 
presentations by leading experts from academia, industry, and 
government; time for group discussion; and breakout sessions for 
discussing questions (1) through (7). No proprietary information will 
be accepted, presented or discussed as part of the workshop, and all 
information accepted, presented or discussed at the workshop will be in 
the public domain.
    More information about the workshop can be found at https://www.nist.gov/news-events/events/2023/07/advances-automation-quantum-dot-devices-control. All participants must pre-register to be admitted. 
Also, please note that federal agencies, including NIST, can only 
accept a state-issued driver's license or identification card for 
access to federal facilities if such license or identification card is 
issued by a state that is compliant with the REAL ID Act of 2005 (Pub. 
L. 109-13), or by a state that has an extension for REAL ID compliance. 
NIST currently accepts other forms of federally-issued identification 
in lieu of a state-issued driver's license. For detailed information 
please contact Meliza Lane

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at [email protected] or by phone (303) 497-5356 or visit: https://www.nist.gov/public_affairs/visitor/.
    Authority: 15 U.S.C. 272(b) & (c); 15 U.S.C. 278h-1.

Alicia Chambers,
NIST Executive Secretariat.
[FR Doc. 2023-07814 Filed 4-12-23; 8:45 am]
BILLING CODE 3510-13-P