Quantum computing

Abstract

Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.

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Please quote as: Rietsche, R.; Dremel, C.; Bosch, S.: Steinacker, Lé., Meckel; M.;
Leimeister, J. M. (2022). Quantum computing. Electronic Markets.

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Electronic Markets
https://doi.org/10.1007/s12525-022-00570-y
FUNDAMENTALS
Quantum computing
RomanRietsche1 · ChristianDremel2 · SamuelBosch3· LéaSteinacker4· MiriamMeckel5· Jan‑MarcoLeimeister1
Received: 8 January 2022 / Accepted: 27 June 2022
© The Author(s) 2022
Abstract
Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications
for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and
entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve
very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives
a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore,
we introduce potential application areas of quantum computing and possible research directions for the field of information
systems.
Keywords Quantum computing· Quantum physics· Cloud computing· Emerging technology· Information systems
JEL Classification O14· O32
Introduction
Quantum computing promises to be the next disruptive tech‑
nology, with numerous possible applications and implica‑
tions for organizations and markets. A recently published
report by McKinsey estimates the global market value of
quantum computing to be at USD 1 trillion by 2035, mainly
in the financial, chemical, pharmaceutical, and automotive
sectors (Hazan etal., 2020). Today, the world’s largest tech‑
nology companies, such as Google, IBM, Microsoft, Ama‑
zon, and Alibaba, are already investing billions in research
and development of their quantum computing and provide
partial access to these quantum computers to the public via
cloud infrastructures. However, not only industry players
invest but also governments, for example, China is investing
USD 10 billion in a national quantum computing laboratory,
the U.S. government provided USD 1 billion, and the EU has
a budget of overall more than EUR 1 billion (Castelvecchi,
2018; Deicker & Yasiejko, 2018).
Quantum computers exploit principles of quantum
mechanics, such as superposition and entanglement, to rep‑
resent data and perform operations on them (Ding & Chong,
2020). Both of these principles enable quantum computers
to solve very specific, complex problems significantly faster
than standard computers. Additionally, interference plays
Responsible Editor: Rainer Alt
* Christian Dremel
christian.dremel@ntnu.no
Roman Rietsche
roman.rietsche@unisg.ch
Samuel Bosch
sbosch@mit.edu
Léa Steinacker
lea.steinacker@unisg.ch
Miriam Meckel
miriam.meckel@unisg.ch
Jan‑Marco Leimeister
janmarco.leimeister@unisg.ch
1 Institute ofInformation Management, University ofSt.
Gallen, Müller‑Friedberg‑Strasse 8, 9000St.Gallen,
Switzerland
2 Norwegian University ofScience andTechnology,
Høgskoleringen 1, 7491Trondheim, Norway
3 Department ofElectrical Engineering andComputer Science,
Massachusetts Institute ofTechnology, 77 Massachusetts
Ave., MA02139Cambridge, UnitedStatesofAmerica
4 University ofSt.Gallen, Müller‑Friedberg‑Strasse
8, 9000St.Gallen, Switzerland
5 Institute forMedia andCommunications Managment,
University ofSt.Gallen, Müller‑Friedberg‑Srasse
8, 9000St.Gallen, Switzerland

R.Rietsche et al.
1 3
an important role specifically when reading information
from the quantum computer (Aaronson, 2008). Quantum
computers can calculate and test extensive combinations of
hypotheses simultaneously instead of sequentially (S.‑S. Li
etal., 2001). Furthermore, some quantum algorithms can
be designed in a way that they can solve problems in much
fewer steps than their classical counterparts (their complex‑
ity is lower). For this reason, quantum computing could rep‑
resent a significant breakthrough in modern IT in the next
few years and might initiate the transition to the "5th indus‑
trial revolution" (Hadda & Schinasi‑Halet, 2019).
First experiments show promising results, such as the one
done by Google in 2019 in which the company claims to
have achieved so‑called quantum supremacy (IBM “quantum
advantage”) (Arute etal., 2019). In an artificial experiment,
they were able to demonstrate that a programmable quan‑
tum device could solve a problem that a classical computer
could not solve in a feasible amount of time. However, the
task solved by Google ‘s quantum computer was custom
tailored to the specific quantum hardware used and has no
real‑world applications. Nevertheless, it was an important
proof of concept. Furthermore, in 2020, Chinese scientists
claimed to have built a quantum computer that is able to
perform specific computations approximately 100 trillion
times faster than the world‘s most advanced supercomputer
(Zhong etal., 2020).
Given its current state of development, experts antici‑
pate that quantum computing could provide unprecedented
advantages, especially in the areas of optimization, artificial
intelligence, and simulation (Langione etal., 2019; Ménard
etal., 2020). It is likely that simulations of molecules (for
chemical and pharmaceutical industries) will be among the
first real‑world applications of quantum computers. This
is because molecules directly follow the laws of quantum
mechanics, so using quantum computers is the most natural
way of simulating them. Other industries that could soon
benefit include the financial sector, transportation and logis‑
tics, the global energy and materials sector but also areas
such as meteorology or cybersecurity (Gerbert & Ruess,
2018; Langione etal., 2019; Ménard etal., 2020). However,
to date, quantum computing has extensive unsolved chal‑
lenges in physics and computer science, ranging from hard‑
ware architectures and data management to application soft‑
ware and algorithms, which requires fundamental research in
all these areas and beyond (Almudever etal., 2017).
To inform information systems (IS) research, this Fun‑
damental provides the fundamental concepts of quantum
computing and depicts research opportunities. Therefore, we
provide in our second section a brief overview of a quantum
computer system and its three layers of a quantum com‑
puter: hardware, system software, and application layer. The
third section introduces potential application areas of quan‑
tum computing.1 Building upon these and the introduced
conceptual layer view on quantum computing, we relate to
each layer by detailing potential research opportunities in
the context of electronic markets. A whole new ecosystem
around quantum computing technology itself is emerging
already, provoking questions around the change of (1) busi‑
ness models and process innovation, (2) challenges for IT
organizations, or (3) sourcing from start‑ups, full‑stack
providers such as Google, IBM, Microsoft, or Alibaba or
individual development.
Quantum computing system
In 1980, Paul Benioff envisioned the concept of a quantum
touring machine, i.e., the theoretical concept of a quantum
computer (Benioff, 1980). In 1982, Richard Feynman pro‑
posed the first practical application of a quantum computer:
efficient simulations of quantum systems (Feynman, 1982).
In general, a quantum computer can be defined as a universal
computing device that stores information in objects called
quantum bits (or qubits) and transforms them by exploiting
very specific properties from quantum mechanics (Ding &
Chong, 2020). The quantum computer performs quantum
computing, which is a type of computation that collects the
different states of qubits, such as superposition, interference,
and entanglement, to perform calculations (Grumbling &
Horowitz, 2019). Importantly, quantum computers are not
intended to become general purpose computers that oper‑
ate by themselves. They will be highly specialized devices
that can solve specific tasks much faster than classical com‑
puting. Operating quantum computers will most certainly
require a classical computer for loading input/output data,
retrieving results from computations as well as controlling
the quantum computer’s electronic and internal processes.
Thus, quantum computers and classical computers form a
quantum computing system that enables quantum comput‑
ers to perform quantum computing. To depict the differ‑
ent layers of a quantum computing system, we adopt the
model of Ding and Chong (2020) for three reasons. First, it
allows us to analytically distinguish the key components of
a quantum component system to illustrate the fundamental
mechanisms and elements. Second, it builds on an analytics
distinction of hardware, system software, and application,
which is mirrored in conceptual views on computing archi‑
tectures, e.g., cloud computing (Infrastructure‑as‑a‑Service,
1 The Fundamentals article is built on the extent body of knowledge
on quantum computing. For our literature review we broadly searched
for the term “quantum computing” in libraries, such as EBSCO, Sci‑
enceDirect, IEEE, or the AIS eLibrary in computer science and IS
research. Both the application areas as well as the research opportuni‑
ties are informed by the prevailing themes of 21 conducted interviews
with technology and academic experts from well‑established Fortune
500 companies and prestigious academic institutions.

Quantum computing
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Platform‑as‑a‑Service, and Software‑as‑a‑Service) or the
layered modular architecture of digital technologies (Yoo
etal., 2010). Third, our expert informants distinguished
between similar layers as well in their interview statements
to explain the state of the art, the challenges for today’s
organizations, and the functioning of quantum computing
systems. Figure1 shows a quantum computing system con‑
sisting of a van Neumann architecture for classical comput‑
ing and a quantum computer with its three layers architec‑
ture, which we will explain accordingly.
Hardware layer
One fundamental difference between classical and quantum
computers is how information is stored. Whereas classical
computers use bits, which can have the value of either zero or
one, to store information, quantum computers use quantum
bits (or qubits), which can hold any linear combination of
zero and one simultaneously (Steane, 1998). Qubits leverage
the advantage of the properties of quantum mechanics and in
particular the effect of superposition (visualization see Fig.2).
Superposition Loosely speaking, a qubit is described by its
probability of being either zero or one and not by the distinct
value of zero or one. Thus, a qubit can have the probability
of being 60% zero and 40% one. Importantly, only at the
point of measuring the state of the qubit, it “collapses” to the
single classical defined value of either zero or one (Bosch,
2020; Ding & Chong, 2020). The property of superposition
has the advantage that a quantum computer with just four
qubits is able to represent 16 four‑digit numbers simultane‑
ously. With each further qubit, the number of representable
states doubles whereas a classical computer, with a sequence
of four bits, can only represent a single four‑digit number.
The real advantage of quantum computing relies on the
fact that one can perform an exponential amount of calcu‑
lations at the same time. Even though at the end of every
program it is possible to read only the solution to one cal‑
culation, it is possible to develop a quantum algorithm that
makes it very likely that the final result is precisely the one
that one is looking for. For example, we might be trying to
find out if there exists any possible rarely occurring turbu‑
lence that could cause a plane to crash. Instead of simulating
Fig. 1 Showing a classical com‑
puter (von Neumann architec‑
ture) and a quantum computer
forming a quantum computing
system (adapted from Ding and
Chong (2020))
Classical Computers
Application Layer
Quantum Algorithms
Systems Software
Layer
Quantum Domain
Specific Language,
Error Correction,
Noise Mitigation
Hardware Layer
Quantum Hardware
System Controller
Qubits
Quantum Computer
Quantum Computing System
Input
Devices
Output
Devices
von Neumann
Architecture
Central Processing Unit
(CPU)
Control Unit
Memory Unit
Register
ALU
Fig. 2 Classical bit and qubit
(Superposition)

R.Rietsche et al.
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billions of combinations of air conditions on a classical com‑
puter and checking their individual results, we could simply
test almost all possible air conditions at once on a quantum
computer and read out only the result that causes the plane
to crash.
Entanglement Not only qubits are unique to quantum
computing. Entanglement is also a property of quantum
mechanics. Entanglement is when the state of one qubit
is dependent on the state of another qubit (Steane, 1998).
Thus, when two qubits are entangled, making any kind of
flip or rotation on one of the qubits would result in the same
change happening to the other qubit (Einstein etal., 1935;
Schrödinger, 1935). Furthermore, when the state of either
one of the two qubits is measured, the state of both qubits
collapses to either one or zero (depending on their prob‑
abilities). This is even the case when the qubits are far away
from each other. Thus, the advantage of entanglement is that
when a qubit influences the other qubits around it, all are
working in tandem to arrive at a solution. Therefore, qubits
can be correlated in a way that is not possible for bits in
traditional computers. This opens up new possibilities and
gives the quantum computer the ability to process infor‑
mation in a fundamentally different way than a classical
computer (Mooney etal., 2019). One example is superdense
coding, which is the process of transporting two classical
bits of information using one entangled qubit (A. Harrow
etal., 2004). This process is especially interesting for secure
quantum key distribution (Bennett & Brassard, 2014). This
is a secure communication method that implements a cryp‑
tographic protocol relying on quantum entanglement and
other quantum phenomena. It enables two parties to produce
a shared random secret key (entangled qubit) known only to
them, which can then be used to encrypt and decrypt mes‑
sages (Scarani etal., 2009).
Based on the fundamentals of quantum mechanics, we
now discuss the approaches to physically represent and
manipulate qubits. Broadly speaking, the approaches can
be split into two main categories: a) analog quantum com‑
puting and b) digital gate‑based quantum computing (Ding
& Chong, 2020).
Analog quantum computing In analog quantum computing,
the quantum state is smoothly changed by quantum opera‑
tions such that the information encoded in the final system
corresponds to the desired answer with high probability. One
example of analog quantum computing is adiabatic quantum
computers (Albash & Lidar, 2018), which refer to the idea of
building a type of universal quantum computing. A special
form of adiabatic quantum computers is quantum annealing,
which is a framework that incorporates algorithms and hard‑
ware designed to solve computational problems via quantum
evolution towards the ground states (Vinci & Lidar, 2017).
Quantum annealing takes advantage of the fact that physical
systems strive towards the state with the lowest energy, e.g.,
hot things cool down over time or objects roll downhill. As
such, in quantum annealing the energetically most favora‑
ble state then corresponds to the solution of the optimiza‑
tion problem (Albash & Lidar, 2018). Using the property of
superposition, the quantum annealer is able to calculate all
potential solutions at the same time, which speeds up the
calculation process drastically in comparison to classical
computers (Shin etal., 2014). Quantum annealing is most
suitable for optimization problems or probabilistic sampling
and is used by companies such as D‑Wave. However, to date,
it is unclear whether the quantum annealing technique will
ever achieve significant quantum speedup (Albash & Lidar,
2018).
Digital gate‑based quantum computing In digital gate‑
based quantum computing, the information encoded in
qubits is manipulated through digital gates. In comparison
to the analog approach in which you sample the natural evo‑
lution of quantum states to find the optimal state of low
energy, in digital gate‑based quantum computers the evolu‑
tion of the quantum states is manipulated in terms of activity
and controlled to find the optimal solution (Ding & Chong,
2020). Thus, the state of qubits is actively manipulated and
therefore provides the advantage of being much more flex‑
ible, and it can be used to solve large classes of problems, in
contrast to quantum annealing. Digital gate‑based quantum
computing is conceptually very similar to classical computa‑
tion (Grumbling & Horowitz, 2019). A classical algorithm
is run on a computer as a series of instructions (gates such
as AND, OR, NOT, …). They manipulate individual or pairs
of classical bits and flip them between zero and one states
according to a set of rules. Quantum gates operate directly
on one or multiple qubits by rotating and shifting them
between different superpositions of the zero and one states
as well as different entangled states. Companies using digi‑
tal gate‑based quantum computing are, for example, IBM,
Google and Rigetti.
System software layer
The system software layer builds on top of the hardware
layer and orchestrates the system’s processes to leverage the
potentials of the qubits (superposition and entanglement).
This layer has to cope with challenges of the thermodynami‑
cally unstable quantum states. It actively reduces thermal
noise within and around the quantum system and performs
error correction procedures.
In quantum computing there are many potential sources
that can cause noise. For example, quantum computers and
especially digital gate‑based ones are highly sensitive to
changes in the environment, such as vibration, temperature

Quantum computing
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fluctuations, etc. Noise can also be caused by imprecise
control of the quantum hardware or manufacturing defects
(Ding & Chong, 2020). Most quantum computers even
require their chips to be cooled down to a hundredth of a
degree above absolute zero temperature to operate. Thus,
since noise cannot be avoided, the first era of quantum
computers is also called noisy Intermediate‑Scale Quan‑
tum Computer (NISQ, Preskill, 2018). This abbreviation
implies that current quantum hardware using dozens of
qubits has error rates that are too high, which need to be
improved before we can build useful quantum computers
with hundreds, or even thousands, of usable qubits.
Noise in the environment can lead to qubit decoherence
which is environmental influences causing quantum states
to randomly change (Grumbling & Horowitz, 2019). This
is problematic, as a single error in a calculation usually
causes the result to be incorrect, unless the error is cor‑
rected during the calculation. Since it is impossible to
prevent every kind of noise, error correction is important.
Ongoing research on quantum error correction seeks to
achieve system‑level fault tolerance. Quantum error cor‑
rection differentiates between physical and logical qubits.
Logical qubits are represented by a group of physical
qubits, which are needed for error correction. Physical
qubits work together on correcting errors on individual
physical qubits. A group of physical qubits is less likely
to cause an error in a calculation than just one physical
qubit. Unfortunately, error‑correcting mechanisms can
cause errors themselves. Depending on the error‑correcting
mechanism, the relation is typically five to nine physical
qubits to achieve one almost error‑free logical qubit (Mari‑
nescu & Marinescu, 2012; Shor, 1995).
One way to do this is by representing every qubit with
groups of several physical qubits that, loosely speaking,
work together on correcting errors on individual physi‑
cal qubits. A perfect physical qubit can work as a logi‑
cal qubit, as it requires no error correction. Today, the
biggest challenge is scaling up to thousands of qubits.
Even though the computational space that can be used
for calculations doubles (Ding & Chong, 2020) with the
addition of every individual qubit, this advantage pres‑
ently cannot be exploited in its full capacity due to high
error rates. One prominent example for trying to increase
the number of qubits is IBM, which states that it wants to
achieve over 1,000 qubits by 2023, while currently there
are machines with 60–100 available (Gambetta, 2020).
Application layer
One of the main challenges of today’s quantum computers
is the unsolved problem of efficient quantum memory (Cili‑
berto etal., 2018). There exist several theoretical proposals
for building quantum random access memory (QRAM). Even
though it may be experimentally difficult to build (just as the
quantum computer itself), recent publications demonstrated
several possible paths of doing so (Hann etal., 2019; Park
etal., 2019). Thus, currently exists no efficient way to store
states of qubits in a memory for a long time for other calcu‑
lations. Therefore, data needs to be loaded from a classical
computer to the targeted quantum computer, and after per‑
forming the calculation states need to be read (measured) by
the classical computer before the qubits lose their informa‑
tion. Due to the no cloning theorem, we are also not able to
make copies of quantum states and use them for calculations.
The only way to load a quantum state from quantum memory
into a quantum program is by applying a SWAP operation,
and thereby removing it from the memory.
When a quantum state is measured, it collapses to either
one or zero. Therefore, we have no way of finding out what
state a certain qubit is in. The only way we can approximately
find out what state a qubit is in is if we have multiple copies
of the same qubit and measure them all. In some cases, read‑
ing the classical data may dominate the cost of quantum algo‑
rithms so that it cannot speed up the whole algorithm at the
macro level. Reading out the data exactly may be infeasible,
which cannot meet the computing needs in some tasks. This
is especially the case for methods that need large data sets,
such as machine learning and artificial intelligence.
Finding a useful algorithm for quantum computers is
mostly about constructing it in such a way that the prob‑
ability of measuring the desired outcome is maximized.
Even though the output of the quantum computer may be an
exponentially large number of solutions, we are usually just
interested in a small subset of these solutions. Finding them
without having to run the whole algorithm many times is the
art of quantum algorithm construction. Here are three of the
most important quantum algorithms.
• Grover’s algorithm is also known as the quantum search
algorithm. Grover’s algorithm is used for searching an
unstructured database or an unordered list. Classically,
for finding a particular item in a database of size N, we
need to go through, on average, N/2 items to find the
right one. Using Grover’s algorithm, we can do this in
only √N steps, on average. For a large N, this can be
remarkably faster. This is called a quadratic speedup
(Grover, 1996).
• Shor’s algorithm, also known as the integer factoriza‑
tion algorithm, can factorize integers almost exponen‑
tially faster than the fastest known classical algorithm.
Factorizing integers is very difficult computationally
and is therefore also the basis of RSA encryption (Shor,
1994).
• HHL (Harrow Hassidim Lloyd) is also known as the
quantum algorithm for linear systems of equations.
The algorithm can estimate the result of a function of

R.Rietsche et al.
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the solution x of a linear system (Ax = b), where A is a
matrix and b a vector (A. W. Harrow etal., 2009).
Application areas ofquantum computing
Thanks to the enormous progress in hardware, more and
more established commercial companies are investing in
quantum technology. Examples include Boehringer Ingel‑
heim, who recently announced a research partnership
with Google (Boehringer‑ingelheim, 2021), and Daimler,
who announced progress in the field of materials research
(Motta etal., 2020), or chemistry giants like BASF who
aim to stay at the forefront of chemistry research and busi‑
ness (Hartmann & Deppe, 2021). Quantum computing
has three essential capabilities to address today’s compu‑
tational problems that current computers are not or only
partially capable of and that bear benefits for companies:
1) search and graph, 2) algebraic and 3) simulation (Hoff‑
mann, 2021; Li etal., 2020). These capabilities determine
the potential applications of this technology in numerous
industries, such as finance, chemistry and pharma, manu‑
facturing, energy, or cybersecurity (Gerbert & Ruess, 2018;
Langione etal., 2019; Ménard etal., 2020). Table1 pro‑
vides a summary of the problem types, approaches and
potential use cases.
Search andgraph
The fact that a qubit can theoretically represent an infinite
number of states allows for solving complex combinatorial
optimization problems, which is currently one of the major
application areas for current quantum computing technolo‑
gies, such as the solution of D‑Wave (Johnson etal., 2011).
Combinatorial optimization is the process of finding one
or more optimal solutions to a problem. Examples of such
problems include supply chain optimization, optimizing
public transportation schedules and routes, package deliver‑
ies, etc. These solutions are searched for in a discrete (finite)
but very large configuration space (i.e., a set of states). The
set of possible solutions can be defined with several con‑
straints and the goal is to optimize the objective function
with the best solution.
Since the problem spaces in certain complex problems
are very large, it is extremely difficult to find the optimal or
even a single good solution to these problems with classi‑
cal computers in a reasonable time frame or with sufficient
accuracy. Such combinatorial optimization problems often
pose a great challenge for the private as well as the public
sector. While they are often simple to describe, they turn
out to be very difficult to solve. Combinatorial optimization
problems may be divided into order, assignment, grouping,
and selection problems, and within these classes, subclasses
exist, such as the knapsack or the traveling salesman prob‑
lem. In addition to the property that there can be a lot of
qualitatively different solutions for a problem, no known
algorithm exists that can easily compute these problems
directly. Searching very large problem spaces requires an
enormous amount of computing capacity and time.
Respectively, quantum computers are expected to play
a decisive role in the financial services industry. Espe‑
cially players specializing in portfolio optimization and
arbitrage could benefit (Egger etal., 2020). From a very
large pool of existing financial instruments, a subset should
be selected so that a certain portfolio volume is achieved,
while at the same time a large number of factors must be
taken into account to minimize risk and achieve profitabil‑
ity (Chakrabarti etal., 2021). Further, Deutsche Börse (a
German company offering marketplace organizing for the
trading of shares) already experimented with the applicabil‑
ity of quantum computing for a sensitivity analysis on one
of their risk models, a computation that is too expensive
Table 1 Overview quantum computing problem types, approaches, and potential use‑cases
Problem type Approach Example use‑cases
Search and graph Finding one or more optimal solution(s) to a complex
problem. Often the problems involve a large number of
possible parameter combinations.
‑ Find optimal parameter configuration to optimize portfolio
in the finance industry
‑ Search for possible routes to optimize traffic flow in trans‑
portation
‑ Factorize prime numbers to break encryption in secure com‑
munication
Algebraic Calculating complex network architectures and the weights
for machine learning and artificial intelligence. This
involves transforming and calculating large matrices.
‑ Transform matrices to find objects in images in computer
vision
‑ Find patterns in texts to understand semantics in natural
language processing
Simulation Calculating how states of a system change through manipu‑
lating parameters to analyze the behavior of complex
systems.
‑ Simulate states of molecules and their changes to understand
chemical reactions in pharma industry
‑ Simulate the behavior of materials to find more efficient
materials in battery industry

Quantum computing
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to be run on classical computers (Braun etal., 2021). Due
to its suitability to solve optimization problems, another
application of quantum computing is the optimization of
flow, e.g., of traffic or goods. Collaborating with D‑Wave
Systems, VW has already shown in a pilot project how to
optimize a simplified traffic flow in the city of Lisbon by
leveraging quantum annealing technologies (Neukart etal.,
2017; Yarkoni etal., 2019) – a project that started in late
2016 with a proof‑of‑concept project. It investigated the
readiness of quantum computing by building a traffic‑flow
optimization program that used GPS coordinates of 418
taxis in Beijing to resolve traffic congestion.
Moreover, quantum computers are superior to classical
computers regarding certain prime factorization proce‑
dures that play an important role in the secure encryption
of data. A popular example for this is the aforementioned
Shor (1994) algorithm that factors a number into its prime
factors, a process used often in cryptography and cyberse‑
curity. A dataset encrypted with quantum technology would
be impossible to decrypt with classical computer technology,
or at least not in time periods relevant to human users. Con‑
versely, it would be easy for a quantum computer to crack
data encrypted with classical RSA technology – a phenom‑
enon that may be coined as quantum threat (Mone, 2020).
Algebraic
The ability of quantum computing to accelerate optimiza‑
tion problems plays a crucial role for narrow AI approaches
(Gao etal., 2018; Langione etal., 2019). Quantum com‑
puting can help to calculate complex network architectures
and weights for machine learning and artificial intelligence.
Quantum computing shows its advantage in transforming
and calculating large metrices. For example, in the context
of supervised learning, the model aims to minimize the error
between the prediction of the model itself compared to the
input and adequate output or label given. Quantum comput‑
ers offer several approaches to solving problems like this,
thereby, again, accelerating calculation and allowing for
more complex network architectures (DeBenedictis, 2018).
They may be applicable to all relevant practices or sub‑tasks
of artificial intelligence, such as image processing and com‑
puter vision (Dendukuri & Luu, 2018) or natural language
processing, as demonstrated in an experiment by Cambridge
Quantum Computing (Lorenz etal., 2021). Having said that,
it is important to note that, so far, no near‑term machine
learning algorithm with provable speedup has been found.
Simulation
A quantum computer has a fundamental advantage over
classical computers: It can simulate other quantum systems
(e.g., a nitrogen molecule) much more efficiently than any
computer system available today. For classical computers,
even molecules with comparatively low complexity repre‑
sent an almost unsolvable task. In the 1980s, Richard Fey‑
nman theoretically substantiated the possibility of a quan‑
tum‑based computer for simulating molecules (Feynman,
1982). Since then, researchers have attempted to transfer
the quantum system of a molecule into another quantum
system, i.e., into the quantum computer, in order to simulate
it. One new hope in the application of quantum computers is
the simulation of more efficient catalysts for ammonia syn‑
thesis in the Haber–Bosch process, which today accounts
for about 1 to 2 percent of global energy consumption. Bet‑
ter catalysts could reduce energy consumption and thus
also help slow global warming. Even quantum computers
without full error correction may already be better suited
for this application than simulations on classical computers
(Budde & Volz, 2019).
Furthermore, the development of active ingredients and
drugs is often a lengthy and very cost‑intensive process.
This is due in particular to the fact that a large number of
substances have to be tested on a trial‑and‑error basis in the
real world. Yet, building on the same principles of quantum
physics, quantum computing may be able to virtually repli‑
cate the behaviors such that simulation‑based research may
sooner or later replace this cost‑intensive process.
For instance, BASF, pursuing its high requirements for
the accuracy of quantum chemical calculations, investigated,
in collaboration with the company HQS, the applicability of
quantum computing. Specifically, they aimed to understand
the quantum mechanical calculation of the energy course
of chemical reactions, as this actually allows for the pre‑
diction of the probable course (i.e., how does the reaction
proceed, which products, by‑products, etc., are formed, how
can I accelerate the reaction with the help of catalysts, etc.)
of chemical reactions. This application of needed methods
reaches the limits of conventional computing methods (Kühn
etal., 2019). In addition, material research on the function‑
ing of batteries is deemed to inform today’s electromobility
and is already targeted by automotive giants such as VW
(Neukart, 2021; Ziegler & Leonhardt, 2019).
Link totheeld ofinformation systems
Even though there are high investments in quantum com‑
puting, most expert estimations still place the widespread
industrial application of quantum computing at least five
to ten years in the future. Its exact manifestations in many
critical areas remain unclear. Thus, it is the task of today’s
research community to creatively conjure up and explore the
full potential and the socio‑technological consequences of
quantum computing. Therefore, based on analyzing exist‑
ing literature and the conducted interviews with 21 leading

R.Rietsche et al.
1 3
experts in industry and research, we propose the following
four initial directions for research on quantum computing in
information systems (for a summary, see Table2): 1) quan‑
tum computing ecosystems as a new networked business, 2)
digital understanding as a foundation for use cases and eco‑
systems, 3) quantum computing as a challenge for IT organi‑
zations and IT service providers, and 4) skills needed to lev‑
erage quantum computing in the quantum computing field.
All of these directions try to consider the fact that quan‑
tum computing despite its disruptive potential will initially
be an extension of computing capabilities for established
electronic markets, ecosystems, and its participants (see
Fig.3), while new ecosystem participants are already estab‑
lishing themselves (e.g., IonQ or Rigetti).
We further try to focus on established research areas and
the focus of our research community.
Quantum computing ecosystems as a new networked busi‑
ness The adoption and diffusion of quantum computing will
heavily rely on an emerging ecosystem comprising technol‑
ogy providers, such as IBM, Google, Microsoft, or Ama‑
zon Web Services, start‑ups with specific playgrounds such
as 1Qbit or IonQ as well as consulting firms and academic
institutions to support customers in adopting and building
applications using quantum computing technologies (Carrel‑
Billiard etal., 2021; IBM, 2019). Also, the European Union
built their own ecosystem with the “Quantum Flagship”.2
Companies, providers, research institutions, and govern‑
ments ultimately need to engage in such an ecosystem to
allow for getting hold of capabilities that transcend their
own organizational boundaries or even their entire industry
(e.g., building their own computing infrastructures, trans‑
lating business problems into mathematical and quantum
problems, etc.) (Carrel‑Billiard etal., 2021). Due to this
emerging new organizing logic and structure for quantum
computing, key aspects need to be considered when pursuing
information systems research in this context.
First, the entrance barrier to quantum computation is
expected to be very high due to multiple limitations such as
the necessity of knowledge in quantum physics, the expen‑
siveness of building quantum computers and the shortage
of experts in the labor market. As such, they may enforce
divides and limit access. Steps should be taken to reduce a
possible quantum divide. Second and consequently, incum‑
bents will need to rely on the capabilities that technology
providers, start‑ups, consulting firms, or academic institu‑
tions may provide, as they might go beyond their domain
expertise. As such, prevailing networked businesses and eco‑
systems need to develop methods and technologies to pur‑
posefully connect their way of doing digital business with
Table 2 Further areas of research and potential research questions
Further research and development Potential research questions
Quantum computing ecosystems as a new networked business ‑ Does the access to quantum computing need to be regulated?
‑ Does quantum computing need new sourcing strategies?
‑ How does the emerging quantum computing ecosystem act as a spoke compo‑
nent to other industries and ecosystems?
‑ Which transformation may result from the emergence of a quantum computing
networked business?
Digital understanding as a foundation for quantum comput-
ing use cases and ecosystems ‑ What approaches could be used or developed to analyze business problems and
therefore leverage the potential of quantum computing? How can these prob‑
lems be described mathematically?
‑ What are possible design principles of artifacts to describe use cases?
‑ How will quantum computing impact the modeling of a social and economic
reality as a transformation from binary to multidimensional quantum states?
Quantum computing as a challenge for IT organizations and
IT service providers ‑ What are possible security approaches to protect legacy IT with old encryption
standards considering the quantum threat?
‑ Can quantum computers and artificial intelligence be used for real‑time threat
and anomaly detection?
‑ How can quantum computers be used to simulate possible intrusions and cyber‑
attacks for calculating risk–cost evaluations?
Quantum computing skills ‑ How could information systems act in a mediating role for adopting quantum
computing technologies?
‑ Should quantum computing be included in the information systems curriculum?
‑ How can future information systems managers be trained to be aware of the
disruptive potential of quantum computing?
‑ How can management leverage the potentials of available techniques,
approaches, and platforms around quantum computing?
‑ How can gaps of knowledge and access to infrastructures be mitigated?
2 https:// qt. eu/

Quantum computing
1 3
the emerging quantum computing ecosystem players in the
different layers, namely the hardware layer (e.g., Amazon
Web Services, IBM, and Google), the system layer (e.g.,
IonQ and Rigetti), and the application layer (e.g., Cambridge
Quantum Computing or 1QBit) (IBM, 2019).
Today, the playground is already diverse, with fuzzy
boundaries leading to the need for design‑science‑oriented
guidance for incumbents to assess their own business and
technology maturity. For instance, IonQ and Rigetti are
positioned on both the hardware and the system software
layer. Additionally, for companies it is important to mediate
the engagement with different players as part of their quan‑
tum computing road map. Thus, possible research questions
might include the following: Does the access to quantum
computing need to be regulated? Does quantum comput‑
ing need new sourcing strategies? How does the emerging
quantum computing ecosystem act as a spoke component
to other industries and ecosystems? Which transformation
may result from the emergence of a quantum computing net‑
worked business?
Digital understanding and representation as a foundation for
quantum computing use cases and ecosystems The prolif‑
eration of quantum computing as a generative technology for
calculating with an enormous speedup relies on a fundamen‑
tal premise: The problem which will be solved by a quantum
computing approach needs to be replicated in the form of
digital data on which basis a calculation becomes possible
in the first place. Emerging technologies such as machine
learning already challenge today’s organizations. The main
reason is that it is complicated to digitally represent business
practices and economic behavior to allow for analysis. This
phenomenon may be summarized as datafication (Lycett,
2013). As such, the dematerialization of the physical world
in the form of digital data as a digital representation is an
essential prerequisite (Recker etal., 2021). Only with this
prerequisite, one may use quantum computing when cal‑
culating the physical world based on its datafied digital
representation.
Achieving an adequate digital representation of the
respective quantum computing problem requires a math‑
ematical and conceptual understanding to allow for assess‑
ing, understanding, and realizing the value of quantum
computing aside from other computing approaches (e.g.,
high performance computing). Furthermore, quantum
computing may also serve as an enabler for process inno‑
vation; for example, it could be interesting for research
areas around process mining (Mendling etal., 2020), such
as analyzing and optimizing process configurations or
simulating contexts of processes or configurations of pro‑
cesses (vom Brocke etal., 2021). Therefore, research on
use case analysis and in particular on methods of how to
find, describe, and analyze use cases systematically and at
scale are highly relevant. Possible research questions could
include the following: What approaches could be used or
developed to analyze business problems and therefore lev‑
erage the potential of quantum computing? How can these
problems be described mathematically? What are possible
design principles of artifacts to describe use cases? How
will QC impact the modeling of a social and economic
reality as a transformation from binary to multidimensional
quantum states?
Fig. 3 Quantum computing
system and relevant point of
contacts for established eco‑
systems

R.Rietsche et al.
1 3
Quantum computing as a challenge for IT organizations and
IT service providers IT competencies are increasingly built
up in business units using commercial IT services without
having the IT department in the loop. Quantum computing
drives this change even further, since for the next few dec‑
ades, the first quantum computers will likely only be avail‑
able via the cloud for most companies (Carrel‑Billiard etal.,
2021). IT departments are therefore under pressure in terms
of how to manage quantum computer usage in companies,
especially with regards to transmitting the respective data
which is needed for quantum‑computing‑based calculations.
This is of particular interest, since data preparation including
data input and output might be the bottleneck for quantum
computing in the long run. Furthermore, quantum comput‑
ing and especially the ability of prime factorization is a
threat for current encryption standards and poses huge chal‑
lenges for the IT organization. Even though new encryption
techniques can be used once quantum computers become a
real threat to current encryption protocols, past communica‑
tion and old data can be decrypted retroactively.
Future research questions could include the following:
What are possible security approaches to protect legacy IT
with old encryption standards? Can quantum computers and
AI be used for real‑time threat and anomaly detection? How
can quantum computers be used to simulate possible intru‑
sions and cyberattacks for calculating risk–cost evaluations?
The latter is of special interest due to the hyper‑connectivity
of digital services, which poses an enormous vulnerability
for an infrastructural attack.
Quantum computing skills Historically, the role of informa‑
tion systems has been to bridge the gap between informatics
and business. In the age of quantum computing, this role
is becoming more important than ever before. In order to
leverage the potential of quantum computing, at least three
roles are required (Carrel‑Billiard etal., 2021; Hughes etal.,
2022): First, mathematical and quantum physical skills are
needed to translate problems into mathematical formulas.
Second, domain expertise is needed to integrate the busi‑
ness problem within the mathematical formulation. Third,
an intermediary is needed to facilitate between both roles
(Gartner, 2019). Due to the high complexity and high spe‑
cialization of the job types (e.g., error correction specialist,
quantum algorithm developer), the entrance barrier to the
field of quantum computing is significantly higher than for
regular "coding". Additionally, for years there has been a
shortage of STEM (Science Technology Engineering Math‑
ematics) graduates, which may amplify the war for talents in
quantum computing (OECD, 2021). Having said that, com‑
panies such as IBM, Google, or research institutions such as
ETH, are working on developing programming languages
and compilers in which a device will decide if the applica‑
tion is suitable for a quantum computer. However, according
to experts, this will take years. Future research questions
could include the following: How could information sys‑
tems act in a mediating role for adopting quantum computing
technologies? Should quantum computing be included in the
information systems curriculum? Since quantum computing
knowledge is important on a strategic level, how can future
information systems managers be trained to be aware of its
potential? How can management leverage the potentials of
available techniques, approaches and platforms around quan‑
tum computing? How can gaps of knowledge and access to
infrastructures be mitigated?
Conclusion
In this Fundamentals article, we provide an overview of the
constituting concepts of quantum computing. Against this
backdrop, this fundamental gives a brief overview of the
three layers of a quantum computer: hardware, system soft‑
ware, and application layer. On this basis and our access to
leading experts in quantum computing, we propose several
focus areas for studying the socio‑technical implications of
quantum computing for the emergence of new ecosystems
or their extensions as well as for ecosystem participants
themselves.
The disruptive nature of quantum computing will lead to
various changes in all socio‑technical components of organi‑
zations and in IS‑related ecosystems. As such, we expect
a large impact on the IS discipline in academia, practice,
and teaching. At the same time, we are aware that quantum
computing is in its infancy, both as a field of research for
IS research as well as in its development towards an estab‑
lished and well‑understood computing approach. Against
this backdrop, we hope to inform and inspire research on the
socio‑technical peculiarities of quantum computing on the
ecosystem level or level of electronic markets (e.g., quantum
computing ecosystems as a new networked business), the
organizational level (e.g., the role of IT organizations and
service provider for establishing quantum computing), the
individual level (e.g., quantum computing skills) as well as
on the crucial role of data (i.e., digital understanding and
representation of economic behavior) to allow for quantum
computing calculations.
Acknowledgements We would also like to thank all the interviewees
for their help and especially Rajiv Krishnakumar for his support in
revising this article.
Open Access This article is licensed under a Creative Commons Attri‑
bution 4.0 International License, which permits use, sharing, adapta‑
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated

Quantum computing
1 3
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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