Liaison statement
LS on work progress on Quantum Key Distribution (QKD) network in SG13 (as of July 2021)

State Posted
Submitted Date 2021-07-26
From Group ITU-T-SG-13
From Contact shaba
To Group opsawg
To Contacts Henk Birkholz
Joe Clarke
Tianran Zhou
CcOperations and Management Area Working Group Discussion List
Warren Kumari
Henk Birkholz
Robert Wilton
Joe Clarke
Scott Mansfield
Tianran Zhou
Response Contact gmlee@kaist.ac.kr
Purpose For information
Attachments SG13-LS213_att8
SG13-LS213_att6
SG13-LS213_att6
SG13-LS213_att4
SG13-LS213_att4
SG13-LS213_att3
SG13-LS213_att2
SG13-LS213_att1
SG13-LS213
Liaisons referring to this one Response to LS on work progress on Quantum Key Distribution (QKD) network in SG13
Body
ITU-T SG13 is pleased to inform you of our progress on Quantum Key Distribution
(QKD) topics.

As we informed you, SG13 has published 5 Recommendations on QKDN as follows:
-       Recommendation ITU-T Y.3800 “Overview on networks supporting quantum
key distribution”; -       Recommendation ITU-T Y.3801 “Functional requirements
for quantum key distribution networks”; -       Recommendation ITU-T Y.3802
“Quantum key distribution networks – Functional architecture”; -      
Recommendation ITU-T Y.3803 “Quantum key distribution networks – Key
management”; -       Recommendation ITU-T Y.3804 “Quantum key distribution
networks - Control and management”.

In the SG13 RGM (virtual meeting, 5-16 July 2021), Q16/13 has made progress of
the following work items.

1. The item agreed after the March 2021 SG13 meeting

The draft Supplement 70 to Y.3800-series was agreed.
Supplement 70 to Y.3800-series (Y.supp.QKDN-mla) “Quantum Key Distribution
Networks - Applications of Machine Learning” in TD597/WP3

For quantum key distribution networks (QKDN), the supplement presents the
applications of machine learning (ML) in the quantum layer, the key management
layer and the management and control layers of QKDN including the use case
background, issue, role of ML in QKDN, use case analysis and, benefits and
impact.

2. The item consented after the March 2021 SG13 meeting

The drat Recommendation ITU-T Y.3805 was consented.
Draft Recommendation ITU-T Y.3805 (Y.QKDN_SDNC) “Quantum Key Distribution
Networks - Software Defined Networking Control” in TD598/WP3 The Recommendation
specifies the requirements, functional architecture, reference points,
hierarchical SDN controller and overall operational procedures of SDN control.

3. Revised on-going work items

Draft Recommendation ITU-T Y.QKDN_BM “Quantum Key Distribution Networks -
Business role-based models” in TD600/WP3

Draft Recommendation ITU-T Y.QKDN_BM describes business roles, business
role-based models, and service scenarios in Quantum Key Distribution Network
(QKDN) from different deployment and operation perspectives with existing user
networks for supporting secure communications in various application sectors.
This draft Recommendation can be used as a guideline for applying QKDN from
business point of views as well as for deployment and operation of QKDN from
telecom operators’ point of views.

Draft Recommendation ITU-T Y.QKDN_frint “Framework for integration of QKDN and
secure network infrastructures” in TD601/WP3 For quantum key distribution
networks (QKDN), Recommendation ITU-T Y.QKDN_frint specifies overview of secure
storage networks (SSNs). It also specifies functional requirements, functional
architecture model, reference points and operational procedures phase-in
scenarios for SSNs.

Y.QKDN-BM and Y.QKDN-frint are candidates for consent at the December 2021 SG13
meeting.

4.New work items agreed at the July 2021 SG13 RGM

Draft Recommendation ITU-T Y.QKDN-iwfr “Quantum key distribution networks -
interworking framework” in TD604/WP3 This Recommendation specifies a framework
for interworking QKDNs.

Draft Recommendation ITU-T Y.QKDN-ml-fra “Quantum key distribution networks -
Functional requirements and architecture for machine learning” in TD607/WP3
QKDN is expected to be able to maintain the stable operation and meet various
cryptographic application requirements in an efficient way. Due to the
advantages of machine learning (ML) related to automatic learning, ML can help
to overcome the challenges of QKDN in terms of quantum layer performance, key
management layer performance and QKDN control and management efficiency. Based
on the functional requirements and architecture of QKDN in [ITU-T Y.3801] and
[ITU-T Y.3802], this recommendation is to specify the overview, functional
requirements, and functional architecture model of ML in QKDN.

Draft Recommendation ITU-T Y.QKDN-rsfr “Quantum key distribution networks -
resilience framework” in TD608/WP3 Resilience is necessary to be introduced
into QKDN to guarantee stable running of QKDN and the continuous key supply.
Based on the functional requirements of QKDN in [ITU-T Y.3801] and functional
architecture of QKDN in [ITU-T Y.3802], this recommendation is to specify the
framework of resilience in QKDN including typical scenarios of resilience as
well as requirements of resilience supported in quantum layer, key management
layer, and control and management layer, respectively.

QKDN resilience use cases considered in this recommendation include network
resources reservation, network resources recovery and alternative schemes such
as re-routing.

Draft Supplement ITU-T Y.supp.QKDN-roadmap “Standardization roadmap on Quantum
Key Distribution Networks” in TD609/WP3 This supplement presents a
comprehensive list of activities (work items) within the ITU-T associated with
QKDN.  The scope of the list includes both study groups and focus groups.  The
list will reflect the status of the work item, as well as the date of approval.

This document will be updated periodically.

5. Conclusion
SG13 will study the network aspects of QKD. Q16/13 looks forward to close
cooperation with ITU-T SG2, SG11, SG15, SG17, ETSI ISG-QKD, ISO/IEC JTC1/SC27,
AG4, IETF/IRTF, and relevant groups for future standardization on QKD networks.

Attachments:
1)      The Supplement 70 to Y.3800 series (Y.supp.QKDN-mla) (TD597/WP3),
Quantum Key Distribution Networks - Applications of Machine Learning; 2)     
The draft Recommendation ITU-T Y.3805 (Y.QKDN_SDNC) (TD598/WP3), Quantum Key
Distribution Networks - Software Defined Networking Control; 3)      The
updated draft Recommendation ITU-T Y.QKDN_BM (TD600/WP3), Quantum Key
Distribution Networks - Business role-based models; 4)      The updated draft
Recommendation ITU-T Y.QKDN_frint (TD601/WP3), Framework for integration of
QKDN and secure network infrastructures; 5)      The initial draft
Recommendation ITU-T Y.QKDN-iwfr (TD604/WP3), Quantum key distribution networks
- interworking framework; 6)      The initial draft Recommendation ITU-T
Y.QKDN-ml-fra (TD607/WP3), Quantum key distribution networks - Functional
requirements and architecture for machine learning; 7)      The initial draft
Recommendation ITU-T Y.QKDN-rsfr (TD608/WP3), Quantum key distribution networks
- resilience framework; 8)      The initial draft Supplement ITU-T
Y.supp.QKDN-roadmap (TD609/WP3), Standardization roadmap on Quantum Key
Distribution Networks.