# Realization of a programmable multi-purpose photonic quantum memory with
over-thousand qubit manipulations

Sheng Zhang  Center for Quantum Information, Institute for Interdisciplinary
Information Sciences, Tsinghua University, Beijing 100084, PR China  Jixuan
Shi  Center for Quantum Information, Institute for Interdisciplinary
Information Sciences, Tsinghua University, Beijing 100084, PR China  Zhaibin
Cui  Center for Quantum Information, Institute for Interdisciplinary
Information Sciences, Tsinghua University, Beijing 100084, PR China  Ye Wang
Center for Quantum Information, Institute for Interdisciplinary Information
Sciences, Tsinghua University, Beijing 100084, PR China  Yukai Wu  Center for
Quantum Information, Institute for Interdisciplinary Information Sciences,
Tsinghua University, Beijing 100084, PR China  Hefei National Laboratory,
Hefei 230088, PR China  Luming Duan<EMAIL_ADDRESS>Center for Quantum
Information, Institute for Interdisciplinary Information Sciences, Tsinghua
University, Beijing 100084, PR China  Hefei National Laboratory, Hefei 230088,
PR China  Yunfei Pu<EMAIL_ADDRESS>Center for Quantum Information,
Institute for Interdisciplinary Information Sciences, Tsinghua University,
Beijing 100084, PR China  Hefei National Laboratory, Hefei 230088, PR China

###### Abstract

Quantum networks can enable various applications such as distributed quantum
computing, long-distance quantum communication, and network-based quantum
sensing with unprecedented performances. One of the most important building
blocks for a quantum network is a photonic quantum memory which serves as the
interface between the communication channel and the local functional unit. A
programmable quantum memory which can process a large stream of flying qubits
and fulfill the requirements of multiple core functions in a quantum network
is still to-be-realized. Here we report a high-performance quantum memory
which can simultaneously store $72$ optical qubits carried by $144$ spatially
separated atomic ensembles and support up to a thousand consecutive write or
read operations in a random access way, two orders of magnitude larger than
the previous record. Due to the built-in programmability, this quantum memory
can be adapted on-demand for several functions. As example applications, we
realize quantum queue, stack, and buffer which closely resemble the
counterpart devices for classical information processing. We further
demonstrate the synchronization and reshuffle of $4$ entangled pairs of
photonic pulses with probabilistic arrival time and arbitrary release order
via the memory, which is an essential requirement for the realization of
quantum repeaters and efficient routing in quantum networks. Realization of
this multi-purpose programmable quantum memory thus constitutes a key enabling
building block for future large-scale fully-functional quantum networks.

##  I Introduction

Quantum memory is a basic ingredient for quantum information technology and
plays a key role in quantum computing ion ; sc , communication BDCZ ; DLCZ ;
oxford_qkd ; weifurner_qkd  and networking kimble ; weiner ; distributed ;
oxford_qkd ; weifurner_qkd ; hanson_teleportation ; ye_and_lukin ;
repeater_telescope ; rmp_gisin ; rmp_polzik . In a quantum network based on
the quantum repeater architecture, photonic quantum memory serves as the local
quantum nodes to connect the communication channels and thus provides the key
building block, as shown in Fig. 1(a). For future applications in large-scale
quantum computing and networking, it is desirable that the quantum memory is
versatile to support application-oriented operations and fits for tasks in
different scenarios quantum_os ; linklayer ; kimble ; weiner .

Substantial advances have been made for the realization of various quantum
memories. Photonic quantum memory with long coherence time has been achieved
based on atomic or spin ensembles 1min ; bao0.1s ; zhongmanjin ; udisk ;
hot_atom , single atoms or ions rempe0.1s ; oxford_sr_and_ca , and single
vacancy centers lukin_siv . To scale up the capacity of quantum memory and
boost the performance of a quantum repeater 07prl ; rmp_gisin ; simon ,
multiplexed quantum memory has been proposed and implemented via spatial modes
225 ; lan , temporal modes 1250 ; 1650 , orbital angular momentum modes
dingdongshen ; laurent , wavevector directions shanxi ; parniak , and other
dimensions wurst ; 1650 ; kwiat . In the multiplexed quantum repeater
protocol, a quantum memory capable of reading out the stored qubits on-demand
is an indispensable ingredient to achieve the full-scale acceleration in
entangling rate 07prl ; rmp_gisin ; simon . Random access quantum memories
(RAQMs) jiangnan ; rempe_ram ; wurst ; wolters  developed recently are
perfectly suitable for this task. To achieve a reasonable entangling rate over
a metropolitan size of $100\,$km via the quantum repeater protocol, one has to
combine long coherence time, large memory capacity, and high-fidelity
operations altogether. Although there are demonstrations for each individual
technique, how to make them compatible with each other and integrate all these
elements into a single quantum memory setup remains a challenging goal in
experiment.

Figure 1: Multi-purpose photonic quantum memory and experimental setup. (a)
Quantum network architecture with a communication backbone based on a quantum
repeater, and many local quantum nodes. The red dashed lines demonstrate the
connection scheme for distributed quantum computing based on this
architecture. (b) Different types of quantum data containers and their
utilizations. (c-d) Schematic of the multipurpose quantum memory. The system
consists of two encoding converters, two addressing units, and a two-
dimensional memory array based on a 87Rb atomic cloud. Both the input and
output qubits are encoded in polarization.

In this work, we realize a photonic quantum memory with a capacity of $72$
qubit cells (size of a state-of-the-art quantum processor), coherence time
above $0.5\,$ms for almost all cells (corresponding to the transmission time
in a $100\,$km fiber), and $1000$ consecutive operations in a random access
way (sufficient to generate any of the $72!\approx 10^{104}$ output sequences
for a $72$-qubit input), two orders of magnitude larger than the previous
record rempe_ram ; wolters . We further demonstrate quantum queue, stack, and
buffer, which are the quantum counterparts of the corresponding widely-used
classical memory devices. Finally, as an example of applications, we
demonstrate the synchronization and reshuffle of four entangled photonic pairs
with probabilistic arrival time and arbitrary release order for the first
time, which can find applications in several scenarios of a quantum network
including entanglement generation, connection, and routing. The photonic
quantum memory implemented here, which is scalable, long-lived, fully-
controllable, and combines unprecedented versatility, provides key enabling
ingredients for the implementation of quantum repeaters and large-scale
quantum networks.

##  II Results

###  II.1 Experimental setup

The multipurpose quantum memory consists of a two-dimensional quantum memory
array, two addressing units and two qubit encoding converters, as illustrated
in Fig. 1(c) and 1(d). The 2D quantum memory array is based on a macroscopic
87Rb atomic cloud divided to 12$\times$12=144 pieces spatially, and each of
the $144$ microscopic ensembles represents a quantum memory. 3 pairs of
crossed acoustic optical deflectors (AODs) act as addressing units and are
exploited to direct the input/output photon and control beam (not shown in
Fig. 1) to different memory cells. Here we use a pair of micro-ensembles to
store a qubit, and the detailed pairing strategy of the 72 qubit cells out of
the 144 micro-ensembles is illustrated in Fig. 2(d). The detailed
implementation of the experimental setup is described in Appendix A.

In the storage process, the photonic qubit is coherently converted to a spin
wave excitation by electromagnetic induced transparency (EIT). Here, we use a
pair of clock states $|5S_{1/2},F=1,m_{F}=0\rangle$ and
$|5S_{1/2},F=2,m_{F}=0\rangle$ to cancel the decoherence induced by magnetic
field noise, and a collinear configuration for the optical beams to suppress
the decoherence caused by atomic random motion respectively, which elongates
the coherence time of each memory cell to over 500$\mu$s (Fig. 2(a)). Together
with a much faster access time of 1$\mu$s through upgraded optical design (see
Appendix D), we can significantly improve the maximally allowed operations
limited by the ratio of memory coherence time to the access time, from
previously state-of-art 12 to $1000$. We further measure the storage
efficiency in each of the $144$ micro-ensembles, as illustrated in Fig. 2(b).
We also measure the storage fidelity in $6$ qubit cells located in different
regions, with $4$ complementary states $|H\rangle$, $|V\rangle$,
$|+\rangle=\frac{|H\rangle+|V\rangle}{\sqrt{2}}$, and
$|L\rangle=\frac{|H\rangle+i|V\rangle}{\sqrt{2}}$ as input zhushiliang  and
different storage time from 100$\mu$s to 500$\mu$s, as shown in Fig. 2(e) and
2(f). Note that the input polarization can be arbitrary, as shown in Fig.
2(c). The average fidelity over 4 different input states is above $95\%$.
Furthermore, the crosstalk is measured to be about $1\%$ by a round of
operations on the 6 neighboring micro-ensembles surrounding the target qubit
(Fig. 2(g)). Finally, repeated writing and reading on the same qubit cell is
found to have negligible influence to the performance.

Figure 2: Performance of individual memory units. (a),(b), Coherence time and
storage efficiency of the 144 micro-ensembles. (c), The storage and retrieval
of an arbitrary polarization qubit. We first convert a polarization qubit
$a|H\rangle+b|V\rangle$ to a time-bin qubit with an encoding converter. Then
the time-bin qubit is further converted into a path qubit to be stored in two
memory cells and finally read out and converted back into a polarization qubit
$a_{4}|H\rangle+b_{4}|V\rangle$, which has a high overlap fidelity with the
input state. (d), The index of the $72$ qubit cells paired from 144 micro-
ensembles. (e-f), State fidelity of the retrieved qubit in six typical qubit
cells $1$, $4$, $20$, $22$, $34$ and $53$. Fidelities for different input
polarizations with $15\mu$s storage are in (e), and average fidelities over
$4$ polarizations at different storage time are in (f). (g), The crosstalk
error induced by accessing nearby micro-ensembles of the stored qubit. The
green micro-ensemble pair represents the qubit for storage, and we iteratively
access the $6$ neighboring regions marked grey for different rounds to
characterize the fidelity loss. A round of operations on the 6 neighbors leads
to about $1\%$ infidelity.
Figure 3: Random access quantum memory. (a) Snapshot of the RAQM with $72$
qubit cells. (b) A random sequence of $500\,\mu$s. All the domains in each
$2\,\mu$s operation are randomly generated including write-or-read (W/R),
qubit cell index, and input polarization. (c) A similar random sequence of
$1000$ write or read operations with a length of $2\,$ms. We update the
storage time of each stored qubit within a scrolling window of $500\,\mu$s,
and execute forced read-out to the stored qubits about to expire in the next
clock cycle. (d-h) The filling number of the memory, the total accesses in
each qubit cell, the distribution of storage time of all output qubits, and
the distribution of the state fidelities of all output qubits, for both
$500\,\mu$s (red) and $2\,$ms (blue) cases. The inset in (g) shows the forced
read-out happens at a chance of $0.4\%$.

###  II.2 High-performance random access quantum memory

The structure of the random access quantum memory is illustrated in Fig. 3(a).
Two qubit channels are used for the input and output of photonic qubits. The
classical instructions including write-or-read (W/R) and the index of qubit
cell are sent to the addressing units via control channels, as shown in Fig.
3(a) and 3(b). Accomplishing a write or read operation for a qubit takes
$2\,\mu$s to access a pair of micro-ensembles successively via upgraded
optical design, which is about $10$ times faster in switching than the
previous work jiangnan . Programming of this memory is to arbitrarily assign
tasks in each $2\,\mu$s cycle of the memory clock, quite similar to piling up
a Lego, with one condition that the stored qubit can’t be read out multiple
times due to the quantum non-cloning theorem non_cloning .

We first characterize the performance of this RAQM with a randomly generated
$500\,\mu$s (250 operations) sequence. Here we set the total length of the
sequence within the coherence time to guarantee the faithful storage for each
stored qubit. All the domains in each of the $250$ operations are randomly
generated, including write or read (W/R), index of qubit cell, and the
polarization for each input qubit, as illustrated in Fig. 3(b) and Fig. S2 sm
. Here the input qubit is carried by a weak coherent state ($\bar{n}=0.5$). We
use several metrics to demonstrate the sequence and characterize the
performance of the memory. The dynamic filling number representing the real-
time number of stored qubits, the number of total accesses at each of the $72$
qubit cells, and the distribution of storage time and fidelity for each
retrieved qubit are illustrated in Fig. 3(d)-3(h). In this sequence, the most
frequently accessed cell is visited by $12$ times, compared to the average
number of $3.74$ (Fig. 3(f)). The average storage time for all the $108$
retrieved qubits is about $68.6\,\mu$s. The fidelity distribution of the
retrieved states is shown in Fig. 3(h), with an average of $92(6)\%$.

Figure 4: Quantum queue, stack, and buffer. (a) Quantum queue (FIFO). We
demonstrate a special sequence of $72$ contiguous enqueues followed by $72$
contiguous dequeues under the rule of a queue. (b-e) The polarization
distribution of the input states, the real-time filling number, the
distribution of storage time for individual qubits, and the fidelities for
individual qubits, respectively. (f-j) Schematic and results for a quantum
stack. Again we use a special sequence in which we first push $72$ qubits and
then pop them out in a reversed order. (k-o) Schematic and results for a
quantum buffer. The distribution of time intervals between successive input
qubits are shown in (l).

Limiting the running time within qubit coherence is a strong condition but not
necessary. Instead, we only need to fulfill a weaker one that each in-memory
qubit stays within the coherence time. To prevent the storage time from
exceeding the coherence time, we maintain a scrolling window of $500\,\mu$s
which keeps track of the storage time of each occupied qubit cell inside the
window, and force the quantum memory to execute read-out if a stored qubit is
about to reach the maximum storage time of $500\,\mu$s in the next cycle sm .
In this way we further elongate the sequence to $2\,$ms, corresponding to a
total number of $1000$ continuous operations in one run, which is almost two
orders of magnitude larger than the previous record rempe_ram ; wolters . We
characterize the performance of this memory with a randomly generated
sequence. The average fidelity of the total $478$ output qubits is measured to
be $93(5)\%$, with each qubit cell accessed by $13.9$ times on average (Fig.
3(f)). We find the forced read-out happens at a quite rare chance below $1\%$
(Fig. 3(g)), which has negligible influence on the programming of sequence or
the performance of memory. More details regarding the sequences and results
can be found in sm .

###  II.3 Quantum queue, stack, and buffer

A queue, also known as a first-in-first-out (FIFO) data structure, follows a
special operation rule that the input (enqueue) and output (dequeue) of data
can only happen at the tail or head of the queue, as illustrated in Fig. 4(a).
With modifications in the control logic, we implement a quantum queue based on
the RAQM, to receive and release photonic qubits in a first-in-first-out
style. Unlike the sequences in the RAQM where the inputs and outputs are
intertwined, here we choose to demonstrate a special case in which $72$
continuous enqueues are first performed to fully fill the $72$ qubit cells,
followed by $72$ consecutive dequeues, to demonstrate the feature of FIFO
maximally sm , as illustrated in Fig. 4(c). In each clock cycle of $2\,\mu$s,
the input qubit is chosen from $|H\rangle$, $|V\rangle$, $|+\rangle$,
$|L\rangle$ with uniform probability. In this special sequence, all the $72$
input qubits are read out in the end and each of them has the same storage
time of $144\,\mu$s, due to the rule of FIFO (Fig. 4(d)). The average fidelity
of the $72$ qubits are measured to be about $93(4)\%$ (Fig. 4(e)).

Figure 5: Synchronization and reshuffle of multiple entangled photon pairs. a
Four Einstein-Podolsky-Rosen (EPR) pairs are sequentially generated through
the DLCZ protocol and are heralded by the detection of signal photons. The $4$
idler photons with random arrival times are stored into the quantum memory and
are read out together in an arbitrarily chosen order ($2$-$4$-$1$-$3$). b The
sequence of the experiment. c The distribution of the random arrival times of
the four idler photons $I_{1}$, $I_{2}$, $I_{3}$, and $I_{4}$. Note that the
four EPR pairs are generated sequentially so that the distributions are not
independent. The error bars correspond to one standard deviation (s.d.). d The
entanglement fidelity of the four EPR pairs $S_{1}$-$I_{1}^{\prime}$,
$S_{2}$-$I_{2}^{\prime}$, $S_{3}$-$I_{3}^{\prime}$, and
$S_{4}$-$I_{4}^{\prime}$ after synchronization and reshuffle. The fit follows
an exponential decay with the gray shaded area representing the $68$%
confidence interval. The error bars also correspond to $68$% confidence
interval. e Two applications in a quantum network, i.e. entanglement
connection of two quantum repeater segments and entanglement routing between
different network nodes.

As a counterpart to a queue, a stack is another abstract data structure which
has only one data entrance (top), for both input (push) and output (pop), as
shown in Fig. 4(f). In a stack, the most remarkable property is that the input
data received later will be output earlier, thus a stack is also known as a
last-in-first-out (LIFO) data structure. Here we still employ a special
sequence in which $72$ qubits are first pushed in a row, and later popped out
continuously. A general sequence with pushes and pops interweaved is described
in detail in supplemental material sm . In this case we present here, several
statistics and measured results are illustrated in Fig. 4(g)-4(j). The storage
time of the $72$ output qubits follows a uniform distribution ranging from
$2\,\mu$s to $286\,\mu$s, in sharp contrast to the constant $144\,\mu$s for
the queue, as a result of the FIFO and LIFO features of these two data
structures. The fidelities of the $72$ output photonic qubits are shown in
Fig. 4(j). We note that there is slight fidelity decay for the earlier pushed
qubits due to longer storage time in the stack.

Buffer, a memory which usually receives data at one rate and releases them at
another rate, is often used to bridge different components in the network,
such as online video streaming and distributed computing in classical
information technology. In analogy to the classical buffer, here we
demonstrate a quantum buffer which can receive qubits from a sparse stream,
synchronize them, and then flush all qubits out as a block in an arbitrary
order, as illustrated in Fig. 4(k). The sequence for characterizing the buffer
is generated with random arrival times and input polarizations sm . We can
clearly see the rate difference in the $356\,\mu$s receiving stage and the
following $144\,\mu$s flushing stage in Fig. 4(m). The average fidelity of the
$72$ flushed qubits is $92(6)\%$.

###  II.4 Synchronization and reshuffle of multiple quantum entanglements

Finally, we demonstrate the storage and synchronization of 4 heralded
entanglements from a probabilistic entanglement source, and later the release
of them in an arbitrary order regardless of the incoming sequence, through the
programmable multi-purpose quantum memory implemented here. It is noteworthy
that the combination of an entanglement source and a write-read quantum memory
is sufficient for heralded entanglement generation 1250 ; 1650 ; rmp_gisin ,
which serves as one of the core functions in a quantum repeater.

The entanglement source is based on the Duan-Lukin-Cirac-Zoller (DLCZ)
protocol DLCZ , in which repeated write-read laser pulses probabilistically
excite an entangled state between a signal photon and an idler photon
$|\psi\rangle=\frac{|H_{s}V_{i}\rangle+|V_{s}H_{i}\rangle}{\sqrt{2}}$, as
illustrated in Fig. 5(a) and the supplemental material sm . Here we can
receive an entangled signal-idler pair from the source in about $70\,\mu$s,
and $4$ signal-idler entangled pair in $500\,\mu$s with high probability. The
fidelity of the signal-idler entangled pair from the source is measured to be
$94(1)\%$ via quantum state tomography.

Unlike the weak coherent state used in the RAQM and quantum queue, stack, and
buffer, here the quantum information is carried by single photons. As
illustrated in Fig. 5(a) and 5(b), the protocol begins with repeated
excitation of signal-idler entangled pair. Upon a success is heralded by the
detection of a signal photon, the control unit dynamically prepares the
quantum memory for the incoming idler photon and stores it into a qubit cell
sm . We call this ‘catch’ and ‘freeze’ in a vivid way. We repeat ‘catch-
freeze’ $4$ times to store the idler photons from $4$ randomly generated
entangled pairs $S_{1}$-$I_{1}$, $\dots$ $S_{4}$-$I_{4}$ into the memory. The
measured distribution of arrival times for the $4$ idler photons is
illustrated in Fig. 5(c). After all the $4$ idler photons are stored in the
quantum memory, the control unit steers the memory to read out all the stored
qubits in a row with any desired order, and we call this ‘reshuffle’ and
‘release’. Here we demonstrate a reshuffled order of $2$-$4$-$1$-$3$, which
means the second arrived idler photon from the entanglement source will be
first read out from the memory in the final ‘release’ stage, and so on.
Therefore, the final entangled pairs are $S_{2}$-$I_{2}^{\prime}$,
$S_{4}$-$I_{4}^{\prime}$, $S_{1}$-$I_{1}^{\prime}$, and
$S_{3}$-$I_{3}^{\prime}$, sorted by idler photon release time, as shown in
Fig. 5(a) and 5(b). We measure the fidelities for these reshuffled entangled
photon pairs and the distribution of the storage time for each pair, as shown
in Fig. 5(d). The fidelities of the $4$ entangled pairs after ‘catch-freeze-
reshuffle-release’ are between $83(6)\%$ and $92(3)\%$, with slight decay due
to the increasing storage time.

Here we also present two potential applications via entanglement
synchronization and reshuffle in a quantum network, as illustrated in Fig.
5(e). In the first application, Alice and Bob each create an entangled photon
pair and send one photon to the quantum memory. The memory synchronizes these
two photons with different arrival times and performs Bell state measurement
between them to further entangle Alice and Bob. This resembles the
entanglement connection lattice ; lanyon_connection  between two repeater
segments, which is also a core function in a quantum repeater.

In the second application, Charles and David want to share their entanglements
with Frank and Emily respectively, however they only have quantum channels to
an intermediate quantum memory. Thus Charles and David each send one photon
from their entangled photon pair to the memory. The memory receives two
photons, stores them, and sends the photons to their destinations by reading
them out at appropriate clock cycles when Frank and Emily are ready to receive
respectively, as illustrated in Fig. 5(e). In this potential application, the
quantum memory can serve as a router which establishes quantum links inside a
group of quantum nodes with full connectivity and substantially saved
resources such as quantum channels.

##  III Discussion

In summary, we demonstrate a high-performance multipurpose quantum memory
which can be adapted for various core applications in a quantum network. The
quantum memory presented in this work provides a promising platform for the
implementation of quantum repeaters and a scalable quantum network. In the
future, it is possible to further improve the coherence time to over $0.1$s
bao0.1s ; 1min  and efficiency above $0.5$ zhushiliang ; hsiao  with much
higher optical depth by loading the atoms into an optical-lattice array, which
will greatly improve the performance of photonic quantum logic klm . We can
also exploit wavelength conversion techniques changwei ; ikuta ; weifurner33km
; lanyon_connection  to achieve a quantum repeater network for long-distance
communication. Finally, if in-memory quantum gates are further equipped via
Rydberg interactions rydberg  or spin superexchange yangbing , a nearly
universal platform can be realized which will support the implementation of
fully-connected quantum logic, error-corrected quantum network, or even a
global quantum internet kimble ; weiner .

##  IV Acknowledgements

The authors thank Pengcheng Lai and Zuqing Wang for helpful discussions. This
work is supported by Innovation Program for Quantum Science and Technology
(No.2021ZD0301102), the Tsinghua University Initiative Scientific Research
Program and the Ministry of Education of China through its fund to the IIIS.
Y.F.P. acknowledges support from the start-up fund and the Dushi Program from
Tsinghua University. Y.K.W. acknowledges support from the start-up fund from
Tsinghua University and National Key Research and Development Program of China
(2020YFA0309500).

##  Appendix A Experimental methods

Here we describe the experimental realization of a long-lived spatially-
multiplexed quantum memory array with $144$ individually addressable
microscopic ensembles in detail. In the beginning, we load a macroscopic 87Rb
atomic cloud in a vacuum chamber by Magneto-optical trap (MOT). The
macroscopic atomic ensemble consists of about $10^{9}$ atoms and the
temperature is estimated to be $300\,\mu$K. We further apply molasses cooling
(sub-Doppler cooling) for $1\,$ms to reduce the temperature to $20\,\mu$K. We
use a two-dimensional addressing unit (AOD, DTSXY-400) to access different
regions of the macroscopic atomic cloud. The AOD scanning frequency ranges
from $85\,$MHz to $118\,$MHz with a step of $3\,$MHz in both X and Y
directions, as illustrated in Fig. 6. The step of $3\,$MHz is chosen based on
a tradeoff between faster access time and larger memory capacity, which will
be further explained in Appendix D. By tuning the RF frequencies on the AOD,
one can access each of the $12\times 12=144$ microscopic ensembles with a
constant time of $1\,\mu$s. Every two adjacent microscopic ensembles are
paired to constitute a qubit cell, as shown in Fig. 2(d).

Figure 6: Quantum memory array and EIT storage. (a) The two-dimensional
quantum memory array with 144 microscopic ensembles. The AOD frequencies for X
and Y dimensions corresponding to each microscopic ensemble are marked in the
figure. (b) The energy level configuration for the EIT process. (c) A
schematic for the photon write-in process and read-out process through EIT.

##  Appendix B Long coherence time

The decoherence in the atomic-ensemble-based quantum memory is mainly induced
by atomic random motion and magnetic field noise. To suppress the magnetic
field influence, we employ the clock transition between
$|g\rangle=|5S_{1/2},F=1,m=0\rangle$ and $|s\rangle=|5S_{1/2},F=2,m=0\rangle$.
The initial state $|g\rangle$ is prepared through a $20\,\mu$s optical pumping
process in which two $\sigma$ lasers and one $\pi$ laser are used. The two
$\sigma$ lasers are resonant to
$|5S_{1/2},F=2\rangle\rightarrow|5P_{1/2},F=2\rangle$ transition and
$|5S_{1/2},F=2\rangle\rightarrow|5P_{3/2},F=2\rangle$ transition,
respectively. The $\pi$ laser is resonant to
$|5S_{1/2},F=1\rangle\rightarrow|5P_{1/2},F=1\rangle$ transition. We employ a
collinear beam configuration to suppress the effect of atomic random motion
and use three cascaded etalons to filter out noise. The transmission
efficiency for signal photon is $90\%$ in each etalon while the coupling light
is suppressed by $500$ times. The probe light is H polarized and the coupling
light is V polarized. The probe (coupling) light focuses on the center of the
atomic micro-ensemble with a beam waist of $125\,$($160$)$\,\mu$m. The energy
level for the EIT process is shown in Fig. 6(b). With all these efforts, we
extend the storage time of each microscopic ensemble to $500\,\mu$s level.

Figure 7: The asymmetric Mach-Zehnder interferometer. The AMZI is temperature-
stabilized to avoid phase drift. It is sealed in an aluminum box. The half-
wave plate (HWP) and quarter-wave plate (QWP) are inserted to compensate for
the polarization change in fiber transmission. The fiber beamsplitter is
PBC780SM-APC (Thorlabs).

##  Appendix C Encoding conversion

Polarization is one of the most widely used encodings for a photonic qubit.
However, it is not straightforward for our multiplexed memory array to store
polarization qubits because the addressing unit (AOD) is polarization-
selective. One solution to this problem is to convert the polarization
encoding to time-bin encoding. As shown in Fig. 7, we design an asymmetric
Mach-Zehnder interferometer (AMZI) to carry out this task 2 . The AMZI
consists of two fiber beamsplitters and two polarization-maintaining (PM)
single-mode fibers with a path difference of $200\,$m. The path difference of
$200\,$m corresponds to the time interval of $1\,\mu$s for the time-bin qubit.
1$\,\mu$s is also the access time for each microscopic ensemble. The
interferometer is integrated in an aluminum box and the temperature is
stabilised with a fluctuation less than $0.004^{\circ}$C. The path difference
of the AMZI is actively locked by stretching the fiber stuck to a piezo. The
locking laser is the $+1$st order diffraction of the acousto-optic modulator
(AOM) and is coupled into the AMZI. Finally, it comes out from the $+1$st
order of the AOM on the other side. During the memory operation, the AMZI
locking is paused, the locking laser is temporally turned off, and the signal
light passes directly through the zero order of the two AOMs. The servo of the
AMZI works during the MOT loading and molasses cooling stage.

We use an arbitrary input state $a|H\rangle+b|V\rangle$ as an example to
illustrate the addressing and storage process. As shown in Fig. 2(c), if an
arbitrary input polarization
$a|H\rangle+b|V\rangle=cos(\theta)|H\rangle+e^{i\phi}sin(\theta)|V\rangle$
enters the memory, it is first converted to a time-bin qubit
$a_{1}|E\rangle+b_{1}|L\rangle=cos(\theta_{1})|E\rangle+e^{i\phi_{1}}sin(\theta_{1})|L\rangle$
at the input encoding converter. Here($\theta,\phi$) and
($\theta_{1},\phi_{1}$) can be different as the efficiency imbalance and a
constant phase offset induced by the encoding converter. Then the AOD
sequentially deflects $|E\rangle$ to $|U\rangle$ and $|L\rangle$ to
$|D\rangle$, and stores the time-bin qubit into the atomic ensemble as a
spatial qubit $a_{2}|U\rangle+b_{2}|D\rangle$ after two successive EIT
storages. After some programmable storage time, the stored qubit is read out
and converted back to time-bin encoding $a_{3}|E\rangle+b_{3}|L\rangle$ again
by AODs and EIT, as a reversed step in EIT storage. Finally, the qubit is
converted back to a polarization qubit
$a_{4}|H\rangle+b_{4}|V\rangle=cos(\theta_{4})|H\rangle+e^{i\phi_{4}}sin(\theta_{4})|V\rangle$
after the output encoding converter. One can see that, if
($\theta_{4},\phi_{4}$) is very close to ($\theta,\phi$), we can guarantee
that the output state has a high fidelity with the input state. In this work
this is achieved by carefully tuning efficiency imbalance and phase in the two
paths
($|H\rangle\rightarrow|E\rangle\rightarrow|U\rangle\rightarrow|E\rangle\rightarrow|H\rangle$
and
$|V\rangle\rightarrow|L\rangle\rightarrow|D\rangle\rightarrow|L\rangle\rightarrow|V\rangle$)
via tuning the RF signals sent to AODs.

Figure 8: Storage of a time-bin qubit into two adjacent microscopic ensembles.
(a) The schematic for AOD addressing. The RF signals are generated by the
arbitrary waveform generators and go through a switch and an amplifier to
drive the AOD. (b) The move of the AOD addressing point as RF signals of
different frequencies are fed into the AOD. Every time we visit a microscopic
ensemble, the AOD addressing point keeps stationary for $200\,$ns. During the
$200\,$ns, the EIT process happens and the photons are stored in or retrieved
from the memory.  Figure 9: Optical configurations in detail. We use multiple
sets of telescopes that adjust the beam waist, in front of and behind each
AOD. The units of all the focal lengths are in mm. The telescope system in
front of the AOD ($100\,$mm and $-50\,$mm) is to narrow the beam waist for
faster switching time on the AOD. The telescope system behind the AOD
($50\,$mm and $150\,$mm) is to enlarge the beam waist to obtain a small focus
radius on the atoms. The $150\,$mm lenses on the left and right sides of the
atomic ensemble constitute a 4f system. The design of the coupling light path
is similar to that of the signal light, the only difference is the
magnification factor of the telescope. The encoding converters are not shown
for simplicity.

##  Appendix D Fast switching between different microscopic ensembles and
memory clock

In this section, we discuss the switching time between different microscopic
ensembles. The switching time results from the time for the sound wave to pass
through the laser area of the AOD. Roughly we have the following relationship

$\text{switching time}\approx\frac{w}{v_{s}},$ (1)

where $w$ is the laser beam waist on the AOD, and $v_{s}$ is the velocity of
the sound wave in the AOD crystal. Since $v_{s}$ is determined by material
($650\,$m/s), we can only reduce the light beam diameter on the AOD. However,
the light beam diameter can not be too small because a smaller diameter on the
AOD results in fewer resolvable points on the macroscopic atomic ensemble as
well as lower AOD diffraction efficiency. Given this trade-off, we set the
switching time between the adjacent microscopic ensembles to $800\,$ns and use
the following $200\,$ns for qubit storage or retrieval. As shown in Fig. 8(a)
and 8(b), the access time is $1\,\mu$s for one microscopic ensemble and thus
$2\,\mu$s for one qubit cell. The scanning frequency step from one microscopic
ensemble to its nearest neighbor is $3\,$MHz in both X and Y directions. The
maximally allowed addressing capacity is $12\times 12=144$ microscopic
ensembles. The optical configurations are shown in Fig. 9.

## References

  * (1) C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, Trapped-ion quantum computing: Progress and challenges,  Appl. Phys. Rev. 6 (2019).
  * (2) M. Kjaergaard, M. E. Schwartz, J. Braumüller, P. Krantz, J. I.-J. Wang, S. Gustavsson, and W. D. Oliver, Superconducting qubits: Current state of play,  Annu. Rev. Condens. Matter Phys. 11, 369–395 (2020).
  * (3) H.-J. Briegel, W. Dür, J. I. Cirac, and P. Zoller, Quantum repeaters: the role of imperfect local operations in quantum communication,  Phys. Rev. Lett. 81, 5932 (1998).
  * (4) L.-M. Duan, M. D. Lukin, J. I. Cirac, and P. Zoller, Long-distance quantum communication with atomic ensembles and linear optics,  Nature 414, 413–418 (2001).
  * (5) D. P. Nadlinger, P. Drmota, B. C. Nichol, G. Araneda, D. Main, R. Srinivas, D. M. Lucas, C. J. Ballance, K. Ivanov, E.-Z. Tan, P. Sekatski, R. L. Urbanke, R. Renner, N. Sangouard, and J. D. Bancal, Experimental quantum key distribution certified by bell’s theorem,  Nature 607, 682–686 (2022).
  * (6) W. Zhang, T. van Leent, K. Redeker, R. Garthoff, R. Schwonnek, F. Fertig, S. Eppelt, W. Rosenfeld, V. Scarani, C. C.-W. Lim, and H. Weinfurter, A device-independent quantum key distribution system for distant users,  Nature 607, 687–691 (2022).
  * (7) H. J. Kimble, The quantum internet,  Nature 453, 1023–1030 (2008).
  * (8) S. Wehner, D. Elkouss, and R. Hanson, Quantum internet: A vision for the road ahead,  Science 362, eaam9288 (2018).
  * (9) J. I. Cirac, A. Ekert, S. F. Huelga, and C. Macchiavello, Distributed quantum computation over noisy channels,  Phys. Rev. A 59, 4249 (1999).
  * (10) S. Hermans, M. Pompili, H. Beukers, S. Baier, J. Borregaard, and R. Hanson, Qubit teleportation between non-neighbouring nodes in a quantum network,  Nature 605, 663–668 (2022).
  * (11) P. Komar, E. M. Kessler, M. Bishof, L. Jiang, A. S. Sørensen, J. Ye, and M. D. Lukin, A quantum network of clocks,  Nat. Phys. 10, 582–587 (2014).
  * (12) D. Gottesman, T. Jennewein, and S. Croke, Longer-baseline telescopes using quantum repeaters,  Phys. Rev. Lett. 109, 070503 (2012).
  * (13) N. Sangouard, C. Simon, H. De Riedmatten, and N. Gisin, Quantum repeaters based on atomic ensembles and linear optics,  Rev. Mod. Phys. 83, 33 (2011).
  * (14) K. Hammerer, A. S. Sørensen, and E. S. Polzik, Quantum interface between light and atomic ensembles,  Rev. Mod. Phys. 82, 1041 (2010).
  * (15) W. Kong, J. Wang, Y. Han, Y. Wu, Y. Zhang, M. Dou, Y. Fang, and G. Guo, Origin pilot: a quantum operating system for effecient usage of quantum resources,  arXiv Prepr. arXiv:2105.10730 (2021).
  * (16) A. Dahlberg, M. Skrzypczyk, T. Coopmans, L. Wubben, F. Rozpedek, M. Pompili, A. Stolk, P. Pawełczak, R. Knegjens, J. de Oliveira Filho, R. Hanson, and S. Wehner, A link layer protocol for quantum networks,  Proceedings of the ACM special interest group on data communication (2019), pp. 159–173.
  * (17) Y. Dudin, L. Li, and A. Kuzmich, Light storage on the time scale of a minute,  Phys. Rev. A 87, 031801 (2013).
  * (18) X.-J. Wang, S.-J. Yang, P.-F. Sun, B. Jing, J. Li, M.-T. Zhou, X.-H. Bao, and J.-W. Pan, Cavity-enhanced atom-photon entanglement with subsecond lifetime,  Phys. Rev. Lett. 126, 090501 (2021).
  * (19) M. Zhong, M. P. Hedges, R. L. Ahlefeldt, J. G. Bartholomew, S. E. Beavan, S. M. Wittig, J. J. Longdell, and M. J. Sellars, Optically addressable nuclear spins in a solid with a six-hour coherence time,  Nature 517, 177–180 (2015).
  * (20) Y. Ma, Y.-Z. Ma, Z.-Q. Zhou, C.-F. Li, and G.-C. Guo, One-hour coherent optical storage in an atomic frequency comb memory,  Nat. Commun. 12, 2381 (2021).
  * (21) O. Katz, and O. Firstenberg, Light storage for one second in room-temperature alkali vapor,  Nat. Commun. 9, 2074 (2018).
  * (22) M. Körber, O. Morin, S. Langenfeld, A. Neuzner, S. Ritter, and G. Rempe, Decoherence-protected memory for a single-photon qubit,  Nat. Photonics 12, 18–21 (2018).
  * (23) P. Drmota, D. Main, D. Nadlinger, B. Nichol, M. Weber, E. Ainley, A. Agrawal, R. Srinivas, G. Araneda, C. Ballance, and D. M. Lucas, Robust quantum memory in a trapped-ion quantum network node,  Phys. Rev. Lett. 130, 090803 (2023).
  * (24) P.-J. Stas, Y. Q. Huan, B. Machielse, E. N. Knall, A. Suleymanzade, B. Pingault, M. Sutula, S. W. Ding, C. M. Knaut, D. R. Assumpcao, Y. C. Wei, M. K. Bhaskar, R. Riedinger, D. D. Sukachev, H. Park, M. Loncar, D. S. Levonian, and M. D. Lukin, Robust multi-qubit quantum network node with integrated error detection,  Science 378, 557–560 (2022).
  * (25) O. Collins, S. Jenkins, A. Kuzmich, and T. Kennedy, Multiplexed memory-insensitive quantum repeaters,  Phys. Rev. Lett. 98, 060502 (2007).
  * (26) C. Simon, H. De Riedmatten, M. Afzelius, N. Sangouard, H. Zbinden, and N. Gisin, Quantum repeaters with photon pair sources and multimode memories,  Phys. Rev. Lett. 98, 190503 (2007).
  * (27) Y. Pu, N. Jiang, W. Chang, H. Yang, C. Li, and L. Duan, Experimental realization of a multiplexed quantum memory with 225 individually accessible memory cells,  Nat. Commun. 8, 15359 (2017).
  * (28) S.-Y. Lan, A. Radnaev, O. Collins, D. Matsukevich, T. Kennedy, and A. Kuzmich, A multiplexed quantum memory,  Opt. Express 17, 13639–13645 (2009).
  * (29) M. Businger, L. Nicolas, T. S. Mejia, A. Ferrier, P. Goldner, and M. Afzelius, Non-classical correlations over 1250 modes between telecom photons and 979-nm photons stored in 171Yb3+:Y2SiO5,  Nat. Commun. 13, 6438 (2022).
  * (30) S.-H. Wei, B. Jing, X.-Y. Zhang, J.-Y. Liao, H. Li, L.-X. You, Z. Wang, Y. Wang, G.-W. Deng, H.-Z. Song, D. Oblak, G. C. Guo, and Q. Zhou, Storage of 1650 modes of single photons at telecom wavelength,  arXiv Prepr. arXiv:2209.00802 (2022).
  * (31) Y.-H. Ye, L. Zeng, M.-X. Dong, W.-H. Zhang, E.-Z. Li, D.-C. Li, G.-C. Guo, D.-S. Ding, and B.-S. Shi, Long-lived memory for orbital angular momentum quantum states,  Phys. Rev. Lett. 129, 193601 (2022).
  * (32) A. Nicolas, L. Veissier, L. Giner, E. Giacobino, D. Maxein, and J. Laurat, A quantum memory for orbital angular momentum photonic qubits,  Nat. Photonics 8, 234–238 (2014).
  * (33) L. Tian, Z. Xu, L. Chen, W. Ge, H. Yuan, Y. Wen, S. Wang, S. Li, and H. Wang, Spatial multiplexing of atom-photon entanglement sources using feedforward control and switching networks,  Phys. Rev. Lett. 119, 130505 (2017).
  * (34) M. Parniak, M. Dabrowski, M. Mazelanik, A. Leszczyński, M. Lipka, and W. Wasilewski, Wavevector multiplexed atomic quantum memory via spatially-resolved single-photon detection,  Nat. Commun. 8, 2140 (2017).
  * (35) J. O’Sullivan, O. W. Kennedy, K. Debnath, J. Alexander, C. W. Zollitsch, M. Simenas, A. Hashim, C. N. Thomas, S. Withington, I. Siddiqi, K. Mølmer, and J. J. L. Morton, Random-access quantum memory using chirped pulse phase encoding,  Phys. Rev. X 12, 041014 (2022).
  * (36) F. Kaneda, F. Xu, J. Chapman, and P. G. Kwiat, Quantum-memory-assisted multi-photon generation for efficient quantum information processing,  Optica 4, 1034–1037 (2017).
  * (37) N. Jiang, Y.-F. Pu, W. Chang, C. Li, S. Zhang, and L.-M. Duan, Experimental realization of 105-qubit random access quantum memory,  npj Quantum Inf. 5, 28 (2019).
  * (38) S. Langenfeld, O. Morin, M. Körber, and G. Rempe, A network-ready random-access qubits memory,  npj Quantum Inf. 6, 86 (2020).
  * (39) L. Meßner, E. Robertson, L. Esguerra, K. Lüdge, and J. Wolters, Multiplexed random-access optical memory in warm cesium vapor,  Opt. Express 31, 10150–10158 (2023).
  * (40) T. Chanelière, D. Matsukevich, S. Jenkins, S.-Y. Lan, T. Kennedy, and A. Kuzmich, Storage and retrieval of single photons transmitted between remote quantum memories,  Nature 438, 833–836 (2005).
  * (41) Y. Wang, J. Li, S. Zhang, K. Su, Y. Zhou, K. Liao, S. Du, H. Yan, and S.-L. Zhu, Efficient quantum memory for single-photon polarization qubits,  Nat. Photonics 13, 346–351 (2019).
  * (42) W. K. Wootters, and W. H. Zurek, A single quantum cannot be cloned,  Nature 299, 802–803 (1982).
  * (43) Supplemental Material.
  * (44) Y.-F. Pu, S. Zhang, Y.-K. Wu, N. Jiang, W. Chang, C. Li, and L.-M. Duan, Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments,  Nat. Photonics 15, 374–378 (2021).
  * (45) V. Krutyanskiy, M. Canteri, M. Meraner, J. Bate, V. Krcmarsky, J. Schupp, N. Sangouard, and B. P. Lanyon, Telecom-wavelength quantum repeater node based on a trapped-ion processor,  Phys. Rev. Lett. 130, 213601 (2023).
  * (46) Y.-F. Hsiao, P.-J. Tsai, H.-S. Chen, S.-X. Lin, C.-C. Hung, C.-H. Lee, Y.-H. Chen, Y.-F. Chen, A. Y. Ite, and Y.-C. Chen, Highly efficient coherent optical memory based on electromagnetically induced transparency,  Phys. Rev. Lett. 120, 183602 (2018).
  * (47) E. Knill, R. Laflamme, and G. J. Milburn, A scheme for efficient quantum computation with linear optics,  Nature 409, 46–52 (2001).
  * (48) W. Chang, C. Li, Y.-K. Wu, N. Jiang, S. Zhang, Y.-F. Pu, X.-Y. Chang, and L.-M. Duan, Long-distance entanglement between a multiplexed quantum memory and a telecom photon,  Phys. Rev. X 9, 041033 (2019).
  * (49) R. Ikuta, T. Kobayashi, T. Kawakami, S. Miki, M. Yabuno, T. Yamashita, H. Terai, M. Koashi, T. Mukai, T. Yamamoto, and N. Imoto, Polarization insensitive frequency conversion for an atom-photon entanglement distribution via a telecom network,  Nat. Commun. 9, 1997 (2018).
  * (50) T. van Leent, M. Bock, F. Fertig, R. Garthoff, S. Eppelt, Y. Zhou, P. Malik, M. Seubert, T. Bauer, W. Rosenfeld, W. Zhang, C. Becher, and H. Weinfurter, Entangling single atoms over 33 km telecom fibre,  Nature 607, 69–73 (2022).
  * (51) M. D. Lukin, M. Fleischhauer, R. Cote, L. Duan, D. Jaksch, J. I. Cirac, and P. Zoller, Dipole blockade and quantum information processing in mesoscopic atomic ensembles,  Phys. Rev. Lett. 87, 037901 (2001).
  * (52) B. Yang, H. Sun, C.-J. Huang, H.-Y. Wang, Y. Deng, H.-N. Dai, Z.-S. Yuan, and J.-W. Pan, Cooling and entangling ultracold atoms in optical lattices,  Science 369, 550–553 (2020).
  * (53) P. Farrera, G. Heinze, and H. De Riedmatten, Entanglement between a photonic time-bin qubit and a collective atomic spin excitation,  Phys. Rev. Lett. 120, 100501 (2018).


* et. al (2015) Cédric LÉVY-BENCHETON et. al. 2015\.  _Security and Resilience of Smart Home Environments: Good practices and recommendations_.  Retrieved October 20, 2021 from https://www.enisa.europa.eu/publications/security-resilience-good-practices
  * ETSI (2015) ETSI. 2015.  _Methods for Testing & Specification; Risk-based Security Assessment and Test- ing Methodologies_.  Retrieved September 13, 2021 from https://www.etsi.org/deliver/etsi_eg/203200_203299/203251/01.01.01_50/eg_203251v010101m.pdf
  * Eusgeld et al. (2011) Irene Eusgeld, Cen Nan, and Sven Dietz. 2011.  “System-of-systems” approach for interdependent critical infrastructures.  _Reliability Engineering & System Safety_ 96, 6 (2011), 679–686.
  * Fortino et al. (2020) Giancarlo Fortino, Fabrizio Messina, Domenico Rosaci, Giuseppe ML Sarné, and Claudio Savaglio. 2020.  A trust-based team formation framework for mobile intelligence in smart factories.  _IEEE Transactions on Industrial Informatics_ 16, 9 (2020), 6133–6142.
  * Franco et al. (2021) Javier Franco, Ahmet Aris, Berk Canberk, and A. Selcuk Uluagac. 2021.  A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems.  _IEEE Communications Surveys Tutorials_ (2021), 1–1.  https://doi.org/10.1109/COMST.2021.3106669
  * Frank et al. (2018) Charles Frank, Samuel Jarocki, Cory Nance, Wayne E Pauli, and SD Madison. 2018.  Protecting IoT Devices from the Mirai Botnet.  _JOURNAL OF INFORMATION SYSTEMS APPLIED RESEARCH_ (2018).
  * García et al. (2019) Sara Nieves Matheu García, Alejandro Molina Zarca, José Luis Hernández-Ramos, Jorge Bernal Bernabé, and Antonio Skarmeta Gómez. 2019\.  Enforcing behavioral profiles through software-defined networks in the industrial internet of things.  _Applied Sciences_ 9, 21 (2019), 4576.
  * George and Thampi (2019) Gemini George and Sabu M. Thampi. 2019.  Vulnerability-based risk assessment and mitigation strategies for edge devices in the Internet of Things.  _Pervasive and Mobile Computing_ 59 (2019), 101068.  https://doi.org/10.1016/j.pmcj.2019.101068
  * Gerodimos et al. (2023) Apostolos Gerodimos, Leandros Maglaras, Mohamed Amine Ferrag, Nick Ayres, and Ioanna Kantzavelou. 2023\.  IOT: Communication protocols and security threats.  _Internet of Things and Cyber-Physical Systems_ (2023).
  * Giraldo et al. (2018) Jairo Giraldo, David Urbina, Alvaro Cardenas, Junia Valente, Mustafa Faisal, Justin Ruths, Nils Ole Tippenhauer, Henrik Sandberg, and Richard Candell. 2018.  A Survey of Physics-Based Attack Detection in Cyber-Physical Systems.  _ACM Comput. Surv._ 51, 4, Article 76 (jul 2018), 36 pages.  https://doi.org/10.1145/3203245
  * Hamadeh et al. (2017) Hala Hamadeh, Soma Chaudhuri, and Akhilesh Tyagi. 2017\.  Area, energy, and time assessment for a distributed TPM for distributed trust in IoT clusters.  _Integration_ 58 (2017), 267–273.
  * Hamza et al. (2020) Ayyoob Hamza, Hassan Habibi Gharakheili, and Vijay Sivaraman. 2020.  IoT network security: Requirements, threats, and countermeasures.  _arXiv preprint arXiv:2008.09339_ (2020).
  * Hassan et al. (2019) Wan Haslina Hassan et al. 2019\.  Current research on Internet of Things (IoT) security: A survey.  _Computer networks_ 148 (2019), 283–294.
  * Hassija et al. (2019) Vikas Hassija, Vinay Chamola, Vikas Saxena, Divyansh Jain, Pranav Goyal, and Biplab Sikdar. 2019\.  A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures.  _IEEE Access_ 7 (2019), 82721–82743.  https://doi.org/10.1109/ACCESS.2019.2924045
  * Hemberg et al. (2018) Erik Hemberg, Joseph R. Zipkin, Richard W. Skowyra, Neal Wagner, and Una-May O’Reilly. 2018.  Adversarial Co-Evolution of Attack and Defense in a Segmented Computer Network Environment. In _Proceedings of the Genetic and Evolutionary Computation Conference Companion_ (Kyoto, Japan) _(GECCO ’18)_. Association for Computing Machinery, New York, NY, USA, 1648–1655.  https://doi.org/10.1145/3205651.3208287
  * Hromada et al. (2023) Dominik Hromada, Rogério Luís de C Costa, Leonel Santos, and Carlos Rabadão. 2023\.  Security Aspects of the Internet of Things.  In _Research Anthology on Convergence of Blockchain, Internet of Things, and Security_. IGI Global, 67–87.
  * Illiano and Lupu (2015) Vittorio Paolo Illiano and Emil Lupu. 2015.  Detecting Malicious Data Injections in Wireless Sensor Networks: A Survey.  _ACM Comput. Surv._ 48 (10 2015), 24:1–24:33.  https://doi.org/10.1145/2818184
  * Illiano et al. (2017) Vittorio P. Illiano, Luis Muñoz-González, and Emil C. Lupu. 2017\.  Don’t fool Me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks.  _IEEE Transactions on Dependable and Secure Computing_ 14 (2017), 279–293.
  * Kamalov et al. (2023) Firuz Kamalov, Behrouz Pourghebleh, Mehdi Gheisari, Yang Liu, and Sherif Moussa. 2023.  Internet of medical things privacy and security: Challenges, solutions, and future trends from a new perspective.  _Sustainability_ IoT device hardware protection and defence approaches, 4 (2023), 3317.
  * Kissel (2013) Richard Kissel. 2013\.  Glossary of Key Information Security Terms.  https://doi.org/10.6028/NIST.IR.7298r2
  * Koeberl et al. (2014) Patrick Koeberl, Steffen Schulz, Ahmad-Reza Sadeghi, and Vijay Varadharajan. 2014.  TrustLite: A security architecture for tiny embedded devices. In _Proceedings of the Ninth European Conference on Computer Systems_. 1–14.
  * Koutsiamanis et al. (2018) Remous-Aris Koutsiamanis, Georgios Z. Papadopoulos, Xenofon Fafoutis, Julián Martín Del Fiore, Pascal Thubert, and Nicolas Montavont. 2018\.  From Best Effort to Deterministic Packet Delivery for Wireless Industrial IoT Networks.  _IEEE Transactions on Industrial Informatics_ 14, 10 (2018), 4468–4480.  https://doi.org/10.1109/TII.2018.2856884
  * Kumar et al. (2023) K Dinesh Kumar, M Sudhakara, Ravi Kumar Poluru, et al. 2023\.  Towards the integration of blockchain and IoT for security challenges in IoT: A review.  _Research Anthology on Convergence of Blockchain, Internet of Things, and Security_ (2023), 193–209.
  * Kumar et al. (2021) Vikash Kumar, Ayan Kumar Das, and Ditipriya Sinha. 2021\.  UIDS: a unified intrusion detection system for IoT environment.  _Evolutionary Intelligence_ 14, 1 (2021), 47–59.
  * Kuzlu et al. (2021) Murat Kuzlu, Corinne Fair, and Ozgur Guler. 2021.  Role of artificial intelligence in the Internet of Things (IoT) cybersecurity.  _Discover Internet of Things_ 1, 1 (2021), 1–14.
  * Laszka et al. (2018) Aron Laszka, Waseem Abbas, Yevgeniy Vorobeychik, and Xenofon Koutsoukos. 2018.  Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening. In _2018 IEEE International Conference on Industrial Internet (ICII)_. 153–158.  https://doi.org/10.1109/ICII.2018.00025
  * Lee and Lee (2020) Yong Lee and G. Lee. 2020\.  Attack Detection Using Network Coding in IoT Environment.  _Sensors (Basel, Switzerland)_ 20 (2020).
  * Leo et al. (2014) Marco Leo, Federica Battisti, Marco Carli, and Alessandro Neri. 2014.  A federated architecture approach for Internet of Things security. In _2014 Euro Med Telco Conference (EMTC)_. 1–5.  https://doi.org/10.1109/EMTC.2014.6996632
  * Li et al. (2017) Jian Li, Yun Liu, Zhenjiang Zhang, Jian Ren, and Nan Zhao. 2017.  Towards green IoT networking: Performance optimization of network coding based communication and reliable storage.  _IEEE Access_ 5 (2017), 8780–8791.
  * Liao et al. (2019) Chen-Hung Liao, Hong-Han Shuai, and Li-Chun Wang. 2019\.  RNN-Assisted Network Coding for Secure Heterogeneous Internet of Things With Unreliable Storage.  _IEEE Internet of Things Journal_ 6, 5 (2019), 7608–7622.  https://doi.org/10.1109/JIOT.2019.2902376
  * Linkov and Kott (2019) Igor Linkov and Alexander Kott. 2019.  Fundamental concepts of cyber resilience: Introduction and overview.  In _Cyber resilience of systems and networks_. Springer, 1–25.
  * Little (2003) Richard G Little. 2003\.  Toward more robust infrastructure: observations on improving the resilience and reliability of critical systems. In _36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the_. IEEE, 9–pp.
  * Liu et al. (2023) Yijia Liu, Jie Wang, Zheng Yan, Zhiguo Wan, and Riku Jäntti. 2023.  A Survey on Blockchain-based Trust Management for Internet of Things.  _IEEE Internet of Things Journal_ (2023).
  * Lu et al. (2020) Di Lu, Ruidong Han, Yulong Shen, Xuewen Dong, Jianfeng Ma, Xiaojiang Du, and Mohsen Guizani. 2020.  xTSeH: A trusted platform module sharing scheme towards smart IoT-eHealth devices.  _IEEE Journal on Selected Areas in Communications_ 39, 2 (2020), 370–383.
  * Lu and Xu (2019) Yang Lu and Li Da Xu. 2019\.  Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics.  _IEEE Internet of Things Journal_ 6, 2 (2019), 2103–2115.  https://doi.org/10.1109/JIOT.2018.2869847
  * Luiijf and Klaver (2021) Eric Luiijf and Marieke Klaver. 2021.  Analysis and Lessons Identified on Critical Infrastructures and Dependencies from an Empirical Data Set.  _International Journal of Critical Infrastructure Protection_ (2021), 100471.
  * Mahmoud et al. (2015) Rwan Mahmoud, Tasneem Yousuf, Fadi Aloul, and Imran Zualkernan. 2015.  Internet of things (IoT) security: Current status, challenges and prospective measures. In _2015 10th International Conference for Internet Technology and Secured Transactions (ICITST)_. 336–341.  https://doi.org/10.1109/ICITST.2015.7412116
  * Martin et al. ([n.d.]) Andrew Martin, Awais Rashid, Howard Chivers, George Danezis, Steve Schneider, and Emil Lupu. [n.d.].  Introduction to CyBOK Issue.  ([n. d.]).
  * Matheu et al. (2020a) Sara N Matheu, José L Hernández-Ramos, Antonio F Skarmeta, and Gianmarco Baldini. 2020a.  A survey of cybersecurity certification for the Internet of Things.  _ACM Computing Surveys (CSUR)_ 53, 6 (2020), 1–36.
  * Matheu et al. (2020b) Sara N. Matheu, José L. Hernández-Ramos, Antonio F. Skarmeta, and Gianmarco Baldini. 2020b.  A Survey of Cybersecurity Certification for the Internet of Things.  _ACM Comput. Surv._ 53, 6, Article 115 (Dec. 2020), 36 pages.  https://doi.org/10.1145/3410160
  * Matheu-García et al. (2019) Sara N Matheu-García, José L Hernández-Ramos, Antonio F Skarmeta, and Gianmarco Baldini. 2019\.  Risk-based automated assessment and testing for the cybersecurity certification and labelling of IoT devices.  _Computer Standards & Interfaces_ 62 (2019), 64–83.
  * Meidan et al. (2018) Yair Meidan, Michael Bohadana, Yael Mathov, Yisroel Mirsky, Asaf Shabtai, Dominik Breitenbacher, and Yuval Elovici. 2018.  N-baiot—network-based detection of iot botnet attacks using deep autoencoders.  _IEEE Pervasive Computing_ 17, 3 (2018), 12–22.
  * Meneghello et al. (2019) Francesca Meneghello, Matteo Calore, Daniel Zucchetto, Michele Polese, and Andrea Zanella. 2019\.  IoT: Internet of threats? A survey of practical security vulnerabilities in real IoT devices.  _IEEE Internet of Things Journal_ 6, 5 (2019), 8182–8201.
  * Mercado-Velázquez et al. (2021) Andrés Aharhel Mercado-Velázquez, Ponciano Jorge Escamilla-Ambrosio, and Floriberto Ortiz-Rodríguez. 2021\.  A Moving Target Defense Strategy for Internet of Things Cybersecurity.  _IEEE Access_ 9 (2021), 118406–118418.  https://doi.org/10.1109/ACCESS.2021.3107403
  * Mohamad Noor and Hassan (2019) Mardiana Binti Mohamad Noor and Wan Haslina Hassan. 2019.  Current research on Internet of Things (IoT) security: A survey.  _Computer Networks_ 148 (2019), 283–294.  https://doi.org/10.1016/j.comnet.2018.11.025
  * Mohan et al. (2018) Sibin Mohan, Mikael Asplund, Gedare Bloom, Ahmad-Reza Sadeghi, Ahmad Ibrahim, Negin Salajageh, Paul Griffioen, and Bruno Sinipoli. 2018\.  Special session: The future of IoT security. In _2018 International Conference on Embedded Software (EMSOFT)_. IEEE, 1–7.
  * Mämmelä et al. (2016) Olli Mämmelä, Jouni Hiltunen, Jani Suomalainen, Kimmo Ahola, Petteri Mannersalo, and Janne Vehkaperä. 2016.  Towards Micro-Segmentation in 5G Network Security.
  * Najmi et al. (2021) Kholoud Y. Najmi, Mohammed Abdullatif Alzain, Mehedi Masud, N. Z. Jhanjhi, Jehad F. Al-Amri, and Mohammed Baz. 2021.  A survey on security threats and countermeasures in IoT to achieve users confidentiality and reliability.  _Materials Today: Proceedings_ (2021).
  * Navas et al. (2021) Renzo E. Navas, Frédéric Cuppens, Nora Boulahia Cuppens, Laurent Toutain, and Georgios Z. Papadopoulos. 2021\.  MTD, Where Art Thou? A Systematic Review of Moving Target Defense Techniques for IoT.  _IEEE Internet of Things Journal_ 8, 10 (2021), 7818–7832.  https://doi.org/10.1109/JIOT.2020.3040358
  * Navas et al. (2020) Renzo Efrain Navas, Håkon Sandaker, Frédéric Cuppens, Nora Cuppens, Laurent Toutain, and Georgios Papadopoulos. 2020\.  IANVS: A Moving Target Defense Framework for a Resilient Internet of Things. In _The 25th IEEE Symposium on Computers and Communications (ISCC)_ _(The 25th IEEE Symposium on Computers and Communications (ISCC))_. IEEE, Rennes, France, 1–6.  https://doi.org/10.1109/ISCC50000.2020.9219728
  * Osman et al. (2020) Amr Osman, Armin Wasicek, Stefan Köpsell, and Thorsten Strufe. 2020.  Transparent Microsegmentation in Smart Home IoT Networks. In _3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20)_. USENIX Association.  https://www.usenix.org/conference/hotedge20/presentation/osman
  * Ouaddah et al. (2016) Aafaf Ouaddah, A. A. E. Kalam, and A. A. Ouahman. 2016\.  FairAccess: a new Blockchain-based access control framework for the Internet of Things.  _Secur. Commun. Networks_ 9 (2016), 5943–5964.
  * OUTCHAKOUCHT et al. (2017) Aissam OUTCHAKOUCHT, Hamza ES-SAMAALI, and Jean Philippe LEROY. 2017\.  Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things.  _International Journal of Advanced Computer Science and Applications_ 8, 7 (2017).  https://doi.org/10.14569/IJACSA.2017.080757
  * Pa et al. (2015) Yin Minn Pa Pa, Shogo Suzuki, Katsunari Yoshioka, Tsutomu Matsumoto, Takahiro Kasama, and Christian Rossow. 2015.  IoTPOT: Analysing the Rise of IoT Compromises. In _9th USENIX Workshop on Offensive Technologies (WOOT 15)_. USENIX Association, Washington, D.C.  https://www.usenix.org/conference/woot15/workshop-program/presentation/pa
  * Pacheco et al. (2019) Jesus Pacheco, Victor H Benitez, and Zhiwen Pan. 2019\.  Security framework for IoT end nodes with neural networks.  _International Journal of Machine Learning and Computing_ 9, 4 (2019), 381–386.
  * Pauna et al. (2019) A. Pauna, I. Bica, Florin Pop, and Aniello Castiglione. 2019\.  On the rewards of self-adaptive IoT honeypots.  _Annals of Telecommunications_ 74 (2019), 501–515.
  * Peralta et al. (2019) Goiuri Peralta, Raul G Cid-Fuentes, Josu Bilbao, and Pedro M Crespo. 2019.  Homomorphic encryption and network coding in iot architectures: Advantages and future challenges.  _Electronics_ 8, 8 (2019), 827.
  * Peralta et al. (2018) Goiuri Peralta, Raul G Cid-Fuentes, Josu Bilbao, Pedro M Crespo, and MA Matin. 2018.  Network coding-based next-generation IoT for industry 4.0.  In _Network Coding_. InTech, 37.
  * Pervez et al. (2018) Huma Pervez, Muhammad Muneeb, Muhammad Usama Irfan, and Irfan Ul Haq. 2018.  A comparative analysis of DAG-based blockchain architectures. In _2018 12th International conference on open source systems and technologies (ICOSST)_. IEEE, 27–34.
  * Ravi et al. (2004) S. Ravi, A. Raghunathan, and S. Chakradhar. 2004.  Tamper resistance mechanisms for secure embedded systems. In _17th International Conference on VLSI Design. Proceedings._ 605–611.  https://doi.org/10.1109/ICVD.2004.1260985
  * Rayes and Salam (2022) Ammar Rayes and Samer Salam. 2022.  Internet of things security and privacy.  In _Internet of Things from Hype to Reality_. Springer, 213–246.
  * Rosenberg and Reinhardt (2021) Henrik Rosenberg and Andreas Reinhardt. 2021.  WIP: Collaborative Approaches to Mitigate Links of Variable Quality in LoRa Networks. In _2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)_. 244–247.  https://doi.org/10.1109/WoWMoM51794.2021.00044
  * Salayma et al. (2017) Marwa Salayma, Ahmed Al-Dubai, Imed Romdhani, and Youssef Nasser. 2017.  Wireless body area network (WBAN) a survey on reliability, fault tolerance, and technologies coexistence.  _ACM Computing Surveys (CSUR)_ 50, 1 (2017), 1–38.
  * Seshadri et al. (2004) Arvind Seshadri, Adrian Perrig, Leendert Van Doorn, and Pradeep Khosla. 2004.  SWATT: Software-based attestation for embedded devices. In _IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004_. IEEE, 272–282.
  * Shrobe et al. (2018) Howard Shrobe, David L. Shrier, and Alex Pentland. 2018\.  _CHAPTER 9 Moving Target Techniques: Cyber Resilience through Randomization, Diversity, and Dynamism_.  293–311.
  * Sidhu et al. (2019) Simranjeet Sidhu, Bassam J. Mohd, and Thaier Hayajneh. 2019\.  Hardware Security in IoT Devices with Emphasis on Hardware Trojans.  _Journal of Sensor and Actuator Networks_ 8, 3 (2019).  https://doi.org/10.3390/jsan8030042
  * Sinha (2021) Satyajit Sinha. 2021\.  devices growing 9% to 12.3 billion globally, cellular IoT now surpassing 2 billion.  https://iot-analytics.com/number-connected-iot-devices
  * Soikkeli et al. (2019) Jukka Soikkeli, Luis Muñoz-González, and Emil C. Lupu. 2019\.  Efficient attack countermeasure selection accounting for recovery and action costs.  arXiv:1904.03082 [cs.CR]
  * Spitzner (2003) Lance Spitzner. 2003\.  Endpoint Protection: Dynamic Honeypots.  https://community.broadcom.com/symantecenterprise/communities/community-home/librarydocuments/viewdocument?DocumentKey=4771f0ac-4bd6-4f6f-a0a6-7cd795727c4e&CommunityKey=1ecf5f55-9545-44d6-b0f4-4e4a7f5f5e68&tab=librarydocuments
  * Stellios et al. (2018) Ioannis Stellios, Panayiotis Kotzanikolaou, Mihalis Psarakis, Cristina Alcaraz, and Javier Lopez. 2018.  A survey of iot-enabled cyberattacks: Assessing attack paths to critical infrastructures and services.  _IEEE Communications Surveys & Tutorials_ 20, 4 (2018), 3453–3495.
  * Sterbenz et al. (2014) James PG Sterbenz, David Hutchison, Egemen K Çetinkaya, Abdul Jabbar, Justin P Rohrer, Marcus Schöller, and Paul Smith. 2014\.  Redundancy, diversity, and connectivity to achieve multilevel network resilience, survivability, and disruption tolerance invited paper.  _Telecommunication Systems_ 56, 1 (2014), 17–31.
  * Stergiopoulos et al. (2020) George Stergiopoulos, Panagiotis Dedousis, and Dimitris Gritzalis. 2020.  Automatic network restructuring and risk mitigation through business process asset dependency analysis.  _Computers & Security_ 96 (2020), 101869.
  * Stergiou et al. (2023) Christos L Stergiou, Elisavet Bompoli, and Konstantinos E Psannis. 2023.  Security and Privacy Issues in IoT-Based Big Data Cloud Systems in a Digital Twin Scenario.  _Applied Sciences_ 13, 2 (2023), 758.
  * Swessi and Idoudi (2022) Dorsaf Swessi and Hanen Idoudi. 2022.  A Survey on Internet-of-Things Security: Threats and Emerging Countermeasures.  _Wirel. Pers. Commun._ 124, 2 (may 2022), 1557–1592.  https://doi.org/10.1007/s11277-021-09420-0
  * Vacca (2012) John R Vacca. 2012\.  _Computer and information security handbook_.  Newnes.
  * Venkatakrishnan and Vouk (2016) Roopak Venkatakrishnan and Mladen A Vouk. 2016.  Using redundancy to detect security anomalies: Towards IoT security attack detectors: The internet of things (ubiquity symposium).  _Ubiquity_ 2016, January (2016), 1–19.
  * Vetterl (2020) Alexander Vetterl. 2020\.  Honeypots in the age of universal attacks and the Internet of Things.
  * Voas and Laplante (2018) Jeffrey Voas and Phillip A. Laplante. 2018.  IoT’s Certification Quagmire.  _Computer_ 51, 4 (2018), 86–89.  https://doi.org/10.1109/MC.2018.2141036
  * Wagner et al. (2017a) Neal Wagner, Cem Şafak Şahin, Jaime Pena, James Riordan, and Sebastian Neumayer. 2017a.  Capturing the Security Effects of Network Segmentation via a Continuous-Time Markov Chain Model. In _Proceedings of the 50th Annual Simulation Symposium_ (Virginia Beach, Virginia) _(ANSS ’17)_. Society for Computer Simulation International, San Diego, CA, USA, Article 17, 12 pages.
  * Wagner et al. (2017b) Neal Wagner, C. Sahin, Jaime Peña, and W. Streilein. 2017b.  A nature-inspired decision system for secure cyber network architecture.  _2017 IEEE Symposium Series on Computational Intelligence (SSCI)_ (2017), 1–8.
  * Wagner et al. (2017c) Neal Wagner, Cem Ş Şahin, Michael Winterrose, James Riordan, Diana Hanson, Jaime Peña, and William W Streilein. 2017c.  Quantifying the mission impact of network-level cyber defensive mitigations.  _The Journal of Defense Modeling and Simulation_ 14, 3 (2017), 201–216.  https://doi.org/10.1177/1548512916662924 arXiv:https://doi.org/10.1177/1548512916662924
  * Wagner et al. (2016) N. Wagner, C. Ş. Şahin, M. Winterrose, J. Riordan, J. Pena, D. Hanson, and W. W. Streilein. 2016.  Towards automated cyber decision support: A case study on network segmentation for security. In _2016 IEEE Symposium Series on Computational Intelligence (SSCI)_. 1–10.  https://doi.org/10.1109/SSCI.2016.7849908
  * Wasicek (2020) Armin Wasicek. 2020\.  The future of 5G smart home network security is micro-segmentation.  _Network Security_ 2020, 11 (2020), 11–13.
  * Xing (2021) Liudong Xing. 2021\.  Cascading Failures in Internet of Things: Review and Perspectives on Reliability and Resilience.  _IEEE Internet of Things Journal_ 8, 1 (2021), 44–64.  https://doi.org/10.1109/JIOT.2020.3018687
  * Xu et al. (2018) Cheng Xu, Hongzhe Liu, Peifeng Li, and Pengfei Wang. 2018\.  A remote attestation security model based on privacy-preserving blockchain for V2X.  _Ieee Access_ 6 (2018), 67809–67818.
  * Yadav et al. (2019) Poonam Yadav, Qi Li, Richard Mortier, and Anthony Brown. 2019\.  Network Service Dependencies in Commodity Internet-of-Things Devices. In _Proceedings of the International Conference on Internet of Things Design and Implementation_ (Montreal, Quebec, Canada) _(IoTDI ’19)_. Association for Computing Machinery, New York, NY, USA, 202–212.  https://doi.org/10.1145/3302505.3310082
  * Zarca et al. (2019) Alejandro Molina Zarca, Jorge Bernal Bernabe, Ruben Trapero, Diego Rivera, Jesus Villalobos, Antonio Skarmeta, Stefano Bianchi, Anastasios Zafeiropoulos, and Panagiotis Gouvas. 2019.  Security management architecture for NFV/SDN-aware IoT systems.  _IEEE Internet of Things Journal_ 6, 5 (2019), 8005–8020.
  * Zarpelão et al. (2017) Bruno Bogaz Zarpelão, Rodrigo Sanches Miani, Cláudio Toshio Kawakani, and Sean Carlisto de Alvarenga. 2017\.  A survey of intrusion detection in Internet of Things.  _Journal of Network and Computer Applications_ 84 (2017), 25–37.  https://doi.org/10.1016/j.jnca.2017.02.009
  * Zavalyshyn et al. (2020) Igor Zavalyshyn, Thomas Given-Wilson, Axel Legay, and Ramin Sadre. 2020.  Brief Announcement: Effectiveness of Code Hardening for Fault-Tolerant IoT Software. In _Stabilization, Safety, and Security of Distributed Systems_ , Stéphane Devismes and Neeraj Mittal (Eds.). Springer International Publishing, Cham, 317–322.
  * Zhang et al. (2019) Yuanyu Zhang, Shoji Kasahara, Yulong Shen, Xiaohong Jiang, and Jianxiong Wan. 2019.  Smart Contract-Based Access Control for the Internet of Things.  _IEEE Internet of Things Journal_ 6, 2 (2019), 1594–1605.  https://doi.org/10.1109/JIOT.2018.2847705
  * Zverev et al. (2019) Mihail Zverev, Pablo Garrido, Ramón Agüero, and Josu Bilbao. 2019.  Systematic Network Coding with Overlap for IoT Scenarios. In _2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)_. IEEE, 1–6.