# Reducing the number of single-photon detectors in quantum key distribution
networks by time multiplexing

Jakob Kaltwasser  Institute for Applied Physics, Technische Universität
Darmstadt,
Schlossgartenstraße 7, 64289 Darmstadt, Germany  Joschka Seip  Institute for
Applied Physics, Technische Universität Darmstadt,
Schlossgartenstraße 7, 64289 Darmstadt, Germany  Erik Fitzke  Institute for
Applied Physics, Technische Universität Darmstadt,
Schlossgartenstraße 7, 64289 Darmstadt, Germany  Maximilian Tippmann
Institute for Applied Physics, Technische Universität Darmstadt,
Schlossgartenstraße 7, 64289 Darmstadt, Germany  Thomas Walther
<EMAIL_ADDRESS>Institute for Applied Physics,
Technische Universität Darmstadt,
Schlossgartenstraße 7, 64289 Darmstadt, Germany

###### Abstract

We demonstrate a method to reduce the number of single-photon detectors (SPDs)
required in multi-party quantum key distribution (QKD) networks by a factor of
two by using detector time multiplexing (DTM). We implement the DTM scheme for
an entanglement-based time-bin protocol and compare QKD results with and
without DTM in our QKD network with four users. When small efficiency losses
are acceptable, DTM enables cost-effective, scalable implementations of multi-
user QKD networks.

quantum key distribution, QKD network, time-bin protocol, detector time
multiplexing

##  I Introduction

Fundamental and technical advances in combination with existing quantum
algorithms such as Shor’s algorithm [1], will enable quantum computers to
break the current asymmetric encryption schemes [2, 3, 4]. One promising way
to restore security is to use quantum key distribution (QKD) in conjunction
with symmetric encryption methods [5, 6, 7].

In recent years, various QKD protocols, methods and networks have been
demonstrated [8, 9, 10, 11, 12, 13]. One main experimental challenge of
practical QKD setups is to provide a high quantum key rate sufficient to
encrypt the high data rates achieved in today’s digital communication. Despite
progress in the realization of long distance fiber-based [14, 12, 15, 16, 13,
17] and satellite-based two-party QKD [10, 9], the distance between the
parties still remains a major challenge due to the transmission losses as long
as quantum repeaters are not accessible in a scalable manner [18]. Of course,
large distances in QKD can be achieved when resorting to networks with trusted
nodes [9].

Another major challenge in the further development of practical QKD-systems is
the scalability regarding the number of users, i.e. to ensure that a high
number of communication parties can be connected. Only with well-developed QKD
networks providing keys for many users, the technology will become relevant
for a wide range of applications. Each pair of parties exchanging a key should
not have to trust the other parties connected to the network. This can be
achieved by implementing entanglement-based protocols in combination with
wavelength-division multiplexing (WDM). To address both, the scalability in
the number of users and the compatibility with existing telecom
infrastructure, our group has recently demonstrated a robust, entanglement-
based multi-user QKD network operating around $1550\text{\,}\mathrm{nm}$ [19].

In the present paper, we provide a method to further improve the scalabilty of
this network by halving the required number of single-photon detectors (SPDs).
The SPDs are the major cost driver for such networks and reducing their number
greatly reduces the cost for implementation. Therefore, our approach is based
on detector time multiplexing (DTM) and allows to reduce the necessary number
of SPDs per receiver unit from two to one.

In the following, we will introduce the concept of DTM and demonstrate its
implementation in our QKD network together with WDM (cf. figure 1).
Furthermore, we thoroughly evaluate the performance of DTM compared to the
regular QKD setup and find only a small reduction in the quantum key rates.
Moreover, we identify the causes for these losses and show that they are in
principle remediable to a significant extent.

Figure 1: Operating principle of an entanglement-based star-shaped QKD network
combining WDM with DTM. Multiple users are connected to the central photon
pair source, where the entangled photons cover a broad spectrum and are
demultiplexed into several frequency channels. Two users connected via
channels with symmetric spacing around the central frequency $\omega_{0}$
receive entangled photon pairs due to energy conservation in the SPDC process.
The receiver units are equipped with identical unbalanced interferometers
(IFs). Due to DTM, each receiver unit only needs one SPD.

##  II Setup And Concept

The principle architecture of our setup is a star-shaped QKD network in which
a high number of user pairs can be connected to a central photon source,
allowing multiple pairs of users to simultaneously and independently exchange
secret quantum keys. The source generates entangled photon pairs with a broad
type-0 spontaneous parametric down-conversion (SPDC) spectrum [20, 21], which
is split by WDM into various frequency channels. Each user receives the
photons from one such channel. The basic concept of this network has already
been successfully demonstrated to work in a telecom environment by our group
[19]. All connected parties can exchange keys pairwise with each other -
independently of all other parties. Since the photon pairs generated from SPDC
are entangled in frequency due to energy conservation, the frequencies
symmetric to the left and the right of the central frequency $\omega_{0}$ of
the SPDC spectrum are entangled, as depicted in figure 1. Hence, by assigning
quantum channels symmetrically positioned around the central frequency
$\omega_{0}$ to a pair of users, these users obtain entangled photons from
which they derive their quantum key. Any user pairing is possible by assigning
those channels of the photon-pair spectrum to the party pairs willing to
exchange secret keys.

The employed BBM92 protocol [22, 23, 24] uses the distribution of photon pairs
entangled in time and phase. This protocol is very well suited to implement
scalable wavelength-multiplexed multi-party networks [19]. A major advantage
over polarization-entangled protocols is its independence from polarization
greatly enhancing the robustness of the transmission. Furthermore, unlike pure
phase coding protocols [5], we do not need active phase modulators.

The concept of phase-time coding for two parties is shown in detail in figure
2. As indicated, it can be easily adapted for more parties. We have
implemented it for four parties. In this case the photon-pair spectrum is
split up into four channels.

Figure 2: Schematic setup of our multi-party QKD with phase-time coding
without DTM. The source (yellow box) and the exemplary receivers Alice, Bob,
Charlie and Diana got identical unbalanced interferometers (only shown here
for Alice and Bob), with a specified phase
$\varphi\,,\alpha\,,\beta\,,\gamma\,,\delta$. We use a
$3.03\text{\,}\mathrm{ns}$ delay in these interferometers. This delay
generates a histogram of the photon arrival times, sketched at the top right
used to gain key bits in the time basis, marked green. The phases of the
interferometers are used to gain key-bits in the phase basis due to the
entanglement of the photons. The phase basis is marked blue and the
corresponding bits are tagged at the detectors.

(a)

(b)

Figure 3: (a) Scheme of a QKD receiver for phase-time coding. SMF - single-
mode fibers with $8.2\text{\,}\mathrm{\SIUnitSymbolMicro m}$ core diameter,
MMF - multi-mode fibers with $62.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$
core-diameter. The temperature stabilized interferometer (IF) consists of a
50:50 beam splitter and two Faraday mirrors (FRM). In the usual setup, each IF
output is connected to a separate SPD. For DTM, the receiver setup is
modified: the interferometer outputs are combined by a 2x SMF $\rightarrow$
MMF fiber combiner. (b) Resulting photon arrival histograms. Both
interferometer outputs show the left histogram in the time domain. Due to the
fiber section introducing a specific delay, one of the outputs is shifted in
time, so that it fits into the free time intervals between the peaks of the
other output (histogram in red) when combined with the other output by the
fiber combiner.

To implement the protocol, we use a photon-pair source containing a laser
generating $300\text{\,}\mathrm{ps}$ long pulses at $1550\text{\,}\mathrm{nm}$
with a repetition frequency of $109.89\text{\,}\mathrm{MHz}$. These pulses
then pass through an imbalanced interferometer, transforming them into well-
separated double-pulses, with a specific phase relation. These pulses are then
frequency-doubled in a second harmonic generation stage. Finally, we use SPDC
[20, 21] to generate the photon pairs in a fiber-coupled periodically-poled
lithium niobate (PPLN) crystal. The photons are demultiplexed into the
respective frequency channels and sent to a pair of parties, who want to
exchange a secret key. For WDM, we use a wavelength-selective switch (WSS)
allowing to arbitrarily swap the communication pairs. In the users’ receiver
stations the photons each pass through another interferometer with an
identical delay as in the source interferometer. Finally, the photons are
detected in single-photon detectors (_ID Quantique_ ID220) connected to the
two interferometer outputs.

In the time basis, three arrival times are possible per repetition cycle
determined by the paths in the interferometers of the source and at the
receiver: early arrival (short path in both interferometers), late arrival
(long path in both interferometers) and arrival during the central time bin as
a mixture of short and long paths, respectively. A qualitative arrival-time
histogram is shown in figure 2. The key bits in the phase basis are given by
the correlation between the two interferometer outputs $A_{i}$ at Alice’s
interferometer and $B_{j}$ at Bob’s interferometer, with $i\,,j=1\,,2$. Their
detection probability in the central time bin depends on the sum of the phases
of the source- and receiver interferometers [8]:

$\displaystyle
P_{A_{i},\,B_{j}}(\alpha,\beta,\phi)=\frac{1}{4}\left(1+(-1)^{i+j}\cos\left(\alpha+\beta-\varphi\right)\right)$
(1)

In the standard configuration, each receiver unit requires two SPDs. However,
by employing DTM the necessary number of SPDs is cut in half.

###  II.1 Detector Time Multiplexing

Detector time multiplexing (DTM) has been recently used in setups to build
photon-number resolved detectors [25, 26, 27] and measuring higher-order
photon correlations [28]. In these applications, the splitting of a pulse into
several distinguishable time bins is used to obtain information about the
photon number of the pulse via the detection probability per bin.

We use DTM to reduce the number of detectors per receiver module. Due to the
relatively short ($300\text{\,}\mathrm{ps}$ long) laser pulses, the width of
the peaks in the histogram is much less than the time delay in the
interferometers of $3.03\text{\,}\mathrm{ns}$. Therefore, there are unused
time intervals in between the peaks, even when the pulses broaden in time due
to chromatic dispersion in the transmission links. Since the free time
intervals themselves are longer than a peak width, further time bins can fit
into the free intervals without interfering with the primary time bins. This
can be used, for example, to increase the key rates by doubling the repetition
rate of the photon source from $109.89\text{\,}\mathrm{MHz}$ to
$219.78\text{\,}\mathrm{MHz}$ [19]. Alternatively, it can be used to realize
DTM by combining both interferometer outputs into one fiber. In figure 3b the
peak structure of a single interferometer output is shown on the left and the
final peak structure with DTM on the right. With DTM, it becomes a six-peak
structure, where one three-peak interferometer output is nested into the
other. The shift between the peak structures is achieved by introducing a
fiber with a specific length producing a delay in one of the outputs before
the interferometer outputs are combined.

One way to combine the outputs is to use a 50:50 tap coupler. But this will
lead to additional losses of half of the photons, thus significantly
decreasing the key rate. Alternatively a fiber combiner can be used to combine
two single-mode inputs into one multi-mode output. We use this principle to
join the two single-mode interferometer outputs in our receivers into one
multimode fiber (MMF), which is then connected to an SPD. Figure 3a displays
the current setup of the receiver units and the necessary changes to implement
DTM. The combiners we use are commercially available off-the-shelf components
introducing additional insertion losses of about $5\text{\,}\mathrm{\char
37\relax}$.

With these physical changes implemented, it is possible to distinguish between
the two interferometer outputs by the different arrival times of the photons
of each output. The time bins can be assigned to two virtual detectors, which
from then on are used in the data evaluation as the two detectors in the
regular setup without DTM. Additionally, the DTM requires a change of the data
acquisition and evaluation software because it has to be able to distinguish
the joint interferometer outputs. The virtual detectors can be assigned to the
correct interferometer outputs using a cross-correlation evaluation of the
first few exchanged key bits, which are discarded afterwards. With the cross-
correlation the absolute temporal offset between the virtual detectors can be
identified. Together with the knowledge of which interferometer output has the
longer propagation time to the combiner, the outputs can always be correctly
assigned.

##  III Results

To evaluate the performance of the QKD setup with DTM, a key exchange lasting
over four hours was performed. Keys were exchanged simultaneously between
Alice and Bob with transmission distances from the source of
$26.9\text{\,}\mathrm{km}$ and $50.4\text{\,}\mathrm{km}$ as well as between
Charlie and Diana with transmission distances of $9.6\text{\,}\mathrm{km}$ and
$20.4\text{\,}\mathrm{km}$, respectively. The $26.9\text{\,}\mathrm{km}$ link
to Alice is a deployed dark fiber link provided by _Deutsche Telekom_ (cf.
Ref. [19]). All other fibers are spooled fibers in the laboratory.

Figure 4: QKD results with and without DTM for our 4-party QKD setup. The data
with DTM were acquired over an extended time period to demonstrate the long-
term stability. The key rates show a systematic difference between the
measurements with DTM and without DTM. Also note the different y-scale for
Alice - Bob and Charlie - Diana due to the different losses introduced by the
different fiber lengths.

The resulting quantum bit error rate (QBER) and sifted key rate are displayed
in figure 4 and average values are tabulated in table 1. The secure key rate
$l_{\text{sec}}$ was estimated from the sifted key rate and the QBER was
estimated as described in Ref. [19] using a formula from Refs. [29, 30].

Table 1: Overview over the average key rates and quantum bit error rates (QBERs) from figure 4 with the user pairs Alice-Bob and Charlie-Diana with and without DTM. DTM |  User combination |  Sifted key rate in bits/s |  QBER in % |  Secure key rate in bits/s
---|---|---|---|---
yes | Alice - Bob | 8.1 | 3.89 | 3.6
Charlie - Diana | 43.0 | 4.37 | 15.3
no | Alice - Bob | 22.8 | 2.74 | 12.5
Charlie - Diana | 91.8 | 2.73 | 50.2

The secure key rate with DTM is about $70\text{\,}\mathrm{\char 37\relax}$
lower than without DTM. This is due to two effects: the major reduction in
sifted key rate and the additional slightly increased QBER.

However, neither the lower sifted key rate nor the higher QBER could be
attributed to the principle of DTM - at least not to this extent. The reason
for the heavy drop in the sifted key rate and the large increase of the QBER
was found in the dependence of our single-photon detectors’ efficiencies on
spatial modes in the MMF. The fiber combiner used in the DTM setup excites
higher spatial modes in this fiber, to which our detectors are much less
sensitive even though specified as multi-mode detectors.

This effect was verified with two MMFs spliced together with a small spatial
misalignment of the cores to excite higher spatial modes. A simple power
measurement was used to verify that the insertion loss introduced by the
splice is negligible. This fiber is inserted into a setup where attenuated
laser pulses were detected with the SPD. With the spliced MMF the count rates
were around $24\text{\,}\mathrm{\char 37\relax}$ lower compared to
measurements with a regular MMF without such an offset splice due to the
relatively large spatial mode dependency of the detection efficiency.

Figure 5: QKD results for a 2-party QKD setup with and without DTM. In the
case of DTM, IDQubes are used additionally, since the ID220s were not able to
detect different spatial modes equally efficient.

After identifying this effect, a control experiment with QKD between two
parties was performed with two IDQube detectors from _ID Quantique_ employing
the same settings for detection efficiency and dead time as for the ID220
detectors. Figure 5 shows the results indicating that the effect does not
occur with these detectors. The sifted key rates with DTM using IDQubes is
more than twice as high as with the ID220s although both detector types have
approximately the same detection efficiency and dead time. This observation
corroborates our assumption that the ID220s’ lower detection efficiency for
higher spatial modes significantly reduces the key rates in our DTM setup.

Nevertheless, using DTM still decreases the key rate by around
$23.6\text{\,}\mathrm{\char 37\relax}$ even when IDQubes are used. Two reasons
were identified: On the one hand DTM required additional fiber connections in
our experimental setup introducing additional losses, and the fiber combiner
leads to insertion losses of around $5\text{\,}\mathrm{\char 37\relax}$ per
receiver. This alone reduces the sifted key rate by around
$10\text{\,}\mathrm{\char 37\relax}$. On the other hand DTM also leads to
higher saturation of the detectors since now both interferometer outputs are
detected at only one SPD. The effect of the saturation can be estimated using
[31]

$\displaystyle\frac{R_{\text{m}}}{R_{\text{e}}}\approx
1-\tau\,R_{\text{m}}\,,$ (2)

with the measured count rate $R_{\text{m}}$, the expected count rate
$R_{\text{e}}$ and the detector dead time $\tau$, which is
$10\text{\,}\mathrm{\SIUnitSymbolMicro s}$ in our setup.

Using ID220s without DTM, the detected count rates for Alice are around
$19\,500\text{\,}\text{/}\mathrm{s}$ and $13\,500\text{\,}\text{/}\mathrm{s}$
at the two interferometer outputs. Bob receives count rates of around
$9500\text{\,}\text{/}\mathrm{s}$ and $6600\text{\,}\text{/}\mathrm{s}$. Using
IDQubes with DTM, the detected count rate for Alice is around
$22\,000\text{\,}\text{/}\mathrm{s}$. Bob receives a count rate of around
$12\,500\text{\,}\text{/}\mathrm{s}$. This results in an additional decrease
of the sifted key rate of around $11\text{\,}\mathrm{\char 37\relax}$ due to
saturation according to equation 2.

The insertion loss of the additional components and the additional fiber
connections amount to an efficiency loss of $10\text{\,}\mathrm{\char
37\relax}$. In combination, both effects give a decrease of efficiency of
$20\text{\,}\mathrm{\char 37\relax}$, which essentially describes the measured
difference of $23.6\text{\,}\mathrm{\char 37\relax}$ between QKD with and
without DTM. The remaining difference of $3.6\text{\,}\mathrm{\char 37\relax}$
may be accounted to measurement uncertainties and additional fiber connections
in the DTM setup and to the fact that equation (2) is only an approximation.
Thus, DTM operates as expected.

The QBER when using ID220 without DTM is similar to the QBER using IDQubes
with DTM. However, the newer IDQubes also have lower dark count rates slightly
reducing the QBER. Since we do not have access to four IDQubes, a check
without DTM using IDQubes could not be performed and the exact effect of DTM
on the QBER could not be determined. A possible cause for a slight increase of
the QBER could be potential crosstalk between time bins due to the reduced gap
between adjacent time bins. Additionally, with DTM crosstalk in the phase
basis is also possible, because the time bins of the central peaks of both
interferometer outputs lie directly next to each other in our case. It might
be possible to reduce this effect by choosing a different time offset between
the interferometer outputs so that the central peaks of the two outputs are
further apart from each other, with a time-basis bin in between them. In
figure 3b this would be the case, when shifting the red histogram, so that the
left small peak of it is right to the blue big peak. This should reduce at
least the crosstalk in the phase basis.

##  IV Discussion and Outlook

Our experiments demonstrate the functionality of DTM, showing a good stability
even for long key-exchange measurements over four to five hours.

However, we have observed a major decrease of sifted key rate in our first
attempts. We attribute this effect to the sensitivity of the detection
efficiencies of our SPD used on spatial modes. A control measurement with a
SPD, which detects all spatial modes equally efficient, has shown that in
principle QKD with DTM works as expected and without unexpected losses in
efficiency. Consequently, a requirement for the DTM are SPDs which can
efficiently detect different and in particular higher spatial modes.

Only a small increase in QBER in case of DTM is not fully explained yet and
needs further investigation. For that a comparative measurement with and
without DTM should be performed with the IDQube detectors.

A major effect reducing the sifted key rate is the saturation of the
detectors, limiting the possible count rates for shorter distances between the
parties. In this case, the photon rates arriving at the receiver units are
higher. Naturally, since both interferometer outputs are fed into a single
detector, DTM is not suitable for high count rates, since saturation becomes
the dominant effect. The saturation effect could be reduced by using other
detector types with lower dead time, such that the ratio of the measured to
the actual rate in formula 2 approaches unity and the sifted-key rate further
approximates the case without DTM. An exemplary alternative for other
detectors would be commercially available superconducting-nanowire single-
photon detectors (SNSPDs). These not only have a higher detection efficiency
but also much lower dead times (e.g. IDQ ID281) [32].

In terms of the number of detectors required for a larger network, our setup
can keep up with measurement device independent (MDI) protocols which only
require as many detectors as connected users [15, 16]. At the same time, we
maintain the advantage over MDI protocols in terms of photon source
scalability: In our setup, a single central photon source serves all users.

##  V Conclusion

To the best of our knowledge, we have for the first time presented the concept
of DTM as a solution to reduce the number of SPDs needed to one per receiver
in a multi-party QKD-network. This reduction will significantly reduce the
cost per receiver module and thereby increase the scalability of such networks
in terms of connected parties. This is an important step towards affordable
and practical QKD networks, and thus the possibility of widespread use of this
technology addressing the growing threats of quantum computing on current
encryption techniques.

From a technical point of view, our setup is already highly scalable with
regard to simultaneously connected parties due to a broad photon-pair spectrum
in conjunction with WDM [19]. WDM in combination with entanglement-based
protocols solves the challenge of serving a large number of parties through
only a single source, while DTM drastically reduces the implementation cost of
the network.

The experimental implementation of DTM was realized by inserting a fiber
introducing a specific time delay after one of the interferometer outputs and
combining both interferometer outputs with a fiber combiner. The problems
arising from spatial dependencies of our single photon detectors were
identified and we have shown that the corresponding problems do not occur with
detectors that properly detect all spatial modes with the same efficiency.

When considering the usage of SPDs which are able to detect higher spatial
modes equally efficiently, there remains only one drawback for the DTM setup:
detector saturation plays a major role because omitting one detector leads to
an increase of the photon rate for the remaining detector. In our setup, DTM
is therefore only useful at larger distances between the parties where the
count rates are lower.

###### Acknowledgements.

This research has been funded by the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation), under Grant No. SFB 1119–236615297. We thank Paul
Wagner from Deutsche Telekom Technik GmbH for lending us the WSS and fiber
spools and Felix Wissel from Deutsche Telekom Technik GmbH for the provision
of a dark fiber test link.

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L. Bozianu
C. Cocha Toapaxi
P.Jawahar
M. Olocco J. A. Gooding N/A xxx-yyy-zzzz 3.0

# The SMARTHEP European Training Network

The SMARTHEP Network

(Preprint prepared )

###### Abstract

Synergies between MAchine learning, Real-Time analysis and Hybrid
architectures for efficient Event Processing and decision-making (SMARTHEP) is
a European Training Network, training a new generation of Early Stage
Researchers (ESRs) to advance real-time decision-making, driving data-
collection and analysis towards synonymity.

SMARTHEP brings together scientists from major LHC collaborations at the
frontiers of real-time analysis (RTA) and key specialists from computer
science and industry. By solving concrete problems as a community, SMARTHEP
will further the adoption of RTA techniques, enabling future High Energy
Physics (HEP) discoveries and generating impact in industry.

ESRs will contribute to European growth, leveraging their hands-on experience
in machine learning and accelerators towards commercial deliverables in fields
that can profit most from RTA, e.g., transport, manufacturing, and finance.

This contribution presents the training and outreach plan for the network, and
is intended as an opportunity for further collaboration and feedback from the
CHEP community.

##  1 Introduction

The Synergies between MAchine learning, Real-Time analysis and Hybrid
architectures for efficient Event Processing and decision making (SMARTHEP)
European Training Network is an EU Horizon-funded training network, with a
focus on the development of expertise in real-time analysis (RTA) techniques
through applications to High Energy Physics (HEP) research and industry. The
network centres around the training of 12 Early Stage Researchers (ESRs)
between September 2022 and September 2025.

##  2 SMARTHEP as a European Training Network

As a European Training Network (ETN), the primary aim of the network is in
training ESRs, whilst deepening synergies between HEP and industry. The
network takes a novel approach to building such synergies, structuring each
ESR position (a 3 year period of doctoral study) around academic and
industrial secondments. To achieve this, the network is formed of a series of
partnerships between universities, research institutes and organisations in
industry, as listed in Table 1.

Category |  Partners
---|---
Universities |  Lund University, Sorbonne University (LPNHE & LIP6), Technische Universität Dortmund, University of Bologna, Université de Genève, University of Heidelberg, University of Helsinki, University of Manchester, Universidade Santiago de Compostela (IGFAE), Vrije Universiteit Amsterdam
Research institutes |  CERN, CNRS, NIKHEF
Industry partners |  IBM France, Lightbox, Point 8, University of Manchester Institute for Data Science and AI, Verizon Connect, Ximantis
Table 1: The member institutes and partners of the SMARTHEP network.

The network is formally structured as 7 Work Packages (WPs), laid out in
Figure 1. WP1 “Management” covers the management of the network by the Project
Manager/Project Coordinator/Executive Board. WP2 “Training” sets out training
and development of participants, by both network partners and external
training providers. WP3 “Machine learning & advanced data analysis” develops
machine learning (ML) techniques for use in real-time environments. WP4
“Hybrid architectures” focuses upon the deployment of non-CPU computing
architectures in acceleration of data processing. WP5 “Decision-making in
research and industry”, applies WP3 and WP4 to develop RTA-based decision-
making technologies. WP6 “Monitoring and discoveries” also applies WP3 and WP4
to RTA approaches in data analysis. WP7 “Dissemination and communication of
results” publishes and propagates the results produced in WP6 and WP7.

Figure 1: The structure of work packages within the SMARTHEP network. WP1 and
WP2 define the organisation of the network; WP3 and WP4 introduce the
techniques and tools of real-time analysis to the network; WP5 and WP6 use
said techniques and tools to produce results for HEP and industry; WP7 makes
these results available and promotes their wider use and adoption.

The network has a particular focus on physics at the Large Hadron Collider
(LHC). Each ESR is thus affiliated to one of the four major experiments based
at the LHC: ALICE, ATLAS, CMS and LHCb. The network duration coincides with
Run 3 of the LHC (2022-2025), with many of the ESR projects framed in this
context.

A unique feature of the network is the extensive cooperation between HEP and
industry across all ESR positions. RTA approaches have seen significant
adoption in industry in recent years, with many organisations turning to RTA
as a means to handle the challenges of “big data”. Industry has thus seen a
swift growth in RTA expertise which can benefit symbiotically from such
cooperation.

##  3 Real-time analysis

HEP and industry share a common challenge—the rapid processing of large
quantities of data [1]. Recent advances in computing, in particular in the
areas of machine learning and hybrid architectures, have enabled the
possibility of processing data in real-time, i.e., as data is collected (also
commonly referred to as “online” processing) [2]. By processing data online,
resources (computing power, storage space, energy, etc.) can be saved and
further insights can be obtained from the data recorded, as shown in Figure 2.

Figure 2: Traditional and RTA approaches to data processing. Traditional
approaches rely on recording all data and processing this offline; in RTA,
data is processed as it is produced, recording only the relevant portions,
enabling greater volumes of processed data to be stored.

RTA techniques have seen widespread adoption across HEP, in particular amongst
trigger and data acquisition (TDAQ) systems. Since it is not possible to
record a full detector readout and carry out event reconstruction at the LHC
collision rate of 40 MHz, triggers must be devised to select only those events
relevant to the physics goals of an experiment. Such triggers conventionally
consist of a hardware system making coarse decisions from partial detector
readout, and a staged software trigger, applying gradually more finely-grained
selections with increasingly more detailed reconstructions of events.

###  3.1 Machine learning

ML, a catch-all term for a family of techniques and technologies wherein
algorithms are conditioned to analyse data, enables rapid decision-making and
pattern recognition across a broad range of use cases [3]. In HEP, the
adoption of ML began with classifiers for offline physics analysis, later
widening to a variety of classification and pattern recognition/anomaly
detection techniques for use online and offline [4].

ML classifiers such as Boosted Decision Trees (BDTs) and Neural Networks (NNs)
are commonly employed in HEP. The use of BDTs in signal selection for offline
physics analysis has become standard practice, e.g., suppression of
combinatorial background in the LHCb experiment measurement of the
$B_{s}^{0}-\bar{B}_{s}^{0}$ oscillation frequency [5]. Classifiers can also
aid tasks such as particle identification within event reconstruction, e.g.,
the CMS boosted event shape tagger, a NN trained to discriminate between
possible $t$, $W^{\pm}$, $Z^{0}$ and $H$ candidates within an event [6]. ML
classifiers also find many decision-making applications in industry, for
example in the detection of malicious communications [7].

ML techniques can also be applied to pattern recognition and anomaly
detection—tasks which cannot otherwise be realistically performed on the
scales of data presently being analysed. In industry, this is applied
intuitively to fraud detection, wherein even subtle changes in transaction
data can indicate fraudulent activity [8]. In HEP, such anomaly detection can
be applied to searches, for example the ATLAS search for resonant decays to a
Higgs boson [9].

###  3.2 Hybrid computing architectures

Central Processing Units (CPUs) have been used ubiquitously across many fields
of research including HEP. CPUs are designed for general purpose computation
(i.e., a single processor capable of performing a wide range of tasks with
significant variety in computing/memory requirements), with large on-board
memory and often multiple processing cores. However, recent advancements in
computing have led to the development of accelerators, such as Graphical
Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), Application-
Specific Integrated Chips (ASICs), etc. Accelerators are designed to perform
specific tasks significantly faster than a CPU, by virtue of their structural
design as represented in Figure 3. A hybrid computing architecture is thereby
a system formed of $2$ or more of the categories of processing units
discussed: by designing the computing architecture around the nature of the
tasks to be computed, computation can be significantly accelerated [10].

Figure 3: Comparison of CPU, GPU and FPGA architectures, illustrated as
schematic diagrams. GPUs typically contain a greater proportion of
computational resources than CPUs, with these resources subdivided within each
multiprocessor to provide better parallel computing performance. FPGAs take a
different approach, comprising many control blocks connected to memory/IO
interface and to one another via switches [10].

GPUs contain similar centralized resources to CPUs, but consist of many
multiprocessors, each containing a greater proportion of computational
resources than an equivalent CPU core. GPUs are thus well-suited to perform
computationally intensive tasks in parallel, for example in event
reconstruction at LHCb, where tasks such as track reconstruction and particle
identification must be completed for every event [11].

FPGAs are structured very differently, with memory and IO interface connected
to many interlinked control blocks formed of simpler logic gate arrangements,
often accompanied by a small cache. FGPAs are thus unable to complete complex
tasks, though provide significant acceleration of simpler, highly
parallelisable tasks. The programmable nature of FPGAs allows for their
configuration with ML algorithms for fast performance [12, 13].

##  4 Early stage researchers

12 ESRs form the core of the network, with the training and partnerships
providing a scaffold for the completion of their respective outcomes. Each ESR
is enrolled as a doctoral student at a partner university for 3 years, during
which they complete secondments in HEP and industry, as illustrated in Figure
4. The university of enrolment is generally also the hosting institution,
where the majority of the doctoral study is completed (industry-centred ESR
positions are enrolled at a university near to the respective industry
partner). A secondment in HEP is undertaken either at another partner
university or a partner research institute. Each ESR (with the exception of
those in industry-centred positions) also undertakes a secondment in industry,
working on an RTA project relevant to their research with an industry partner.

Figure 4: Structure of a SMARTHEP ESR position. Each ESR is enrolled (i.),
during which they will undertake secondments with network partners in HEP
(ii.) and industry (iii.). Through the combination of primary and secondment
work, each ESR will complete goals in HEP (iv.) and industry (v.), discussed
in further detail in Section 5. Precise durations of the secondments vary
between ESR positions.

To illustrate further the structure of a SMARTHEP ESR position, examples of
academia- and industry-centred positions are given below. Details of the
positions are given alongside their corresponding label in Figure 4.

Firstly, an example of an academia-centred position is the ESR based at the
University of Heidelberg (i.). Their primary focus is of real-time dark photon
searches at LHCb (iv.), furthered by collaboration with the University of
Milano-Bicocca (ii.). An industry secondment (iii.) with Verizon Connect is
planned, applying ML expertise to the real-time processing of vehicle data
(v.).

An industry-centred position is well-typified by the ESR working at IBM,
enrolled at Sorbonne University (i.). Their research applies real-time rule
induction to fraud detection using real-time rule induction at IBM (v.). An
industry secondment (iii.) is not undertaken, since the primary research is
undertaken with an industry partner; however, an academic secondment (ii.) is
planned with the CNRS research institute, applying similar pattern-recognition
techniques to the classification of HEP observations (iv).

##  5 Network outcomes

The network is defined around a set of concrete outcomes, guiding the
progression of the network and its participants. These intended outcomes are
summarised briefly below.

The core outcomes of the network are a set of goals, completed on behalf of
the network partners by the network participants, in particular by the ESRs,
who each contribute to at least one whitepaper. These goals include experiment
commissioning, HEP measurements and industrial results. These goals are
typically defined with respect to a specific ESR position, e.g., the goal of
“calibration of ALICE TPC for heavy-ion physics”.

Participants will write a series of three whitepapers reviewing the current
RTA state-of-the-art, expected to be submitted in late 2023. These whitepapers
will place a particular emphasis on the ongoing contributions of the network
to their respective topics. The first whitepaper reviews ML applications to
HEP in RTA contexts and their corresponding best practices. Such applications
range from data-taking, e.g., particle identification algorithms at ALICE, to
offline analysis, e.g., anomaly detection for dijet resonance searches at
ATLAS [14, 15]. The use of hybrid computing architectures by LHC experiments
is reviewed in the second whitepaper. Tasks such as selection and
reconstruction were significantly accelerated during Run 2 of the LHC
(2015-2018). Run 3 deployments of hybrid architectures capitalise on this
progress, e.g., in the use of FPGAs in the ALICE Central Trigger System and
the use of GPUs in the CMS High Level Trigger farm [16, 17]. In the third
whitepaper, TDAQ systems of LHC experiments are reviewed, with a focus upon
best practices for both TDAQ hardware and software. As such, this whitepaper
reviews topics ranging from upgrades to the ATLAS TDAQ system, to the Allen
framework of the LHCb experiment enabling software trigger operation at a 30
MHz readout rate [18, 19].

As a key network objective, participants are also encouraged to partake in a
broad range of training activities, with network resources dedicated to
facilitating this. Such activities include the attendance of external
specialist industrial training sessions and academic schools, in addition to
internally organised workshops and schools (e.g., in Section 6).

The work of the ESRs will generate digital assets (e.g., software packages,
data processing tools), with many being applicable beyond narrow
academic/industrial applications. The network is therefore committed to making
any such digital assets Findable, Accessible, Interoperable, and Reusable
(FAIR) [20]. To implement these commitments, a project on GitHub has been
created to host such assets [21]. Other assets and resources will be made
available on the network website [22].

##  6 Network events

Network events provide participants with unique opportunities to meet,
exchange ideas and develop. Whilst these events typically cater to the ESR
audience, some events have been made available to additional interested early
career researchers working/studying at SMARTHEP institutes.

To commence ESR participation in the network, a kick-off meeting was held at
the University of Manchester in November 2022 to formally introduce the ESRs
to the network and discuss network objectives and organisation. Amongst the
activities of the kick-off meeting were a visit to the Jodrell Bank Centre for
Astrophysics and a review paper-writing course by Scriptoria to train ESRs
ahead of writing the whitepapers.

In January of 2023, the First SMARTHEP School on Collider Physics and Machine
Learning was hosted by the University of Geneva. The school provided a varied
programme, including lectures on experimental physics at collider experiments
by Anna Sfyrla and on theoretical physics & Monte Carlo event generators by
Torbjörn Sjöstrand. Additionally, hands-on lessons in machine learning were
led by Maurizio Pierini, with seminars also given on multimessenger astronomy
and the CERN experimental programme by Teresa Montaruli and Jamie Boyd
respectively.

Later events of the network aim to guide the governance of the network,
develop ESR expertise and deepen collaborations between participants. The
network assembly serves as an annual forum in which to make significant
decisions on network policy and governance. Technical hackathons, foreseen for
autumn $2023$, give ESRs the opportunity to learn state-of-the-art techniques
through their direct application. Hackathons also act as effective team-
building activities, promoting problem-solving, rapid prototyping and project
management skills, which ESRs can then apply to their own research projects.
Accelerator and ML bootcamps, proposed for summer $2024$, will formalise the
practical experience of ESRs. Collaboration with industry will culminate in an
industry applications school late in the network, by which time it is expected
that all ESR industry secondments will have been completed.

##  7 Conclusion

Through the activities of the network outlined in this contribution, SMARTHEP
aims to provide valuable contributions to HEP, industry, and the wider
community. By the close of the network in September 2025, the 12 ESRs will be
well-equipped to progress their research and their careers. The network will
play a key role in the commissioning and operation of major LHC experiments
and seek to strengthen experimental RTA portfolios by a two-way sharing of
knowledge and expertise with industry partners. Such collaboration between
academic and industrial network participants will deepen synergies across the
field in the application of RTA.

## Acknowledgements

We acknowledge funding from the European Union Horizon 2020 research and
innovation programme, call H2020-MSCA- ITN-2020, under Grant Agreement n.
956086.

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