Introduction computer. Business Risks · Supply chain needs more

Introduction

Quantum computing is a type of nonclassical computing that
is based on the quantum state of subatomic particles. Quantum computing is
fundamentally different from classic computers, which operate using binary
bits. This means the bits are either 0 or 1, true or false, positive or
negative. However, in quantum computing, the bit is referred to as a quantum
bit or qubit. Unlike the strictly binary bits of classic computing, qubits can
represent 1 or 0 or a superposition of both partly 0 and partly 1 at the same
time. Quantum computing uses quantum-mechanical phenomena, such as
superposition and entanglement. Superposition is what gives quantum computers
speed and parallelism, meaning that these computers could theoretically work on
millions of computations at once. Further, qubits can be linked with other
qubits in a process called entanglement. When combined with superposition,
quantum computers could process a massive number of possible outcomes at the
same time.

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Market Size

The global Quantum Computing market will reach $10.7 billion
by 2024, out of which $8.45 billion stem from product sales and services and
$2.25 billion from government-funded RDT&E programs.

Applications and Use Cases

The potential that quantum computing has for solving
problems in ML, AI and big data, where classic computing limits potential, is
driving a lot of innovation and growth among data scientists. Investors are
putting millions of dollars toward the technology, and more than 50 companies,
universities and research companies are working on development.

·        
Machine learning: Improved ML through faster
structured prediction. Examples include Boltzmann machines, quantum Boltzmann
machines, semi-supervised learning, unsupervised learning and deep learning.

·        
Artificial intelligence: Faster calculations
could improve perception, comprehension, self-awareness and circuit fault
diagnosis/binary classifiers.

·        
Finance: Quantum computing could enable faster,
more complex Monte Carlo simulations, for example, trading, trajectory
optimization, market instability, price optimization and hedging strategies.

·        
Healthcare: DNA gene sequencing, such as
radiotherapy treatment optimization/brain tumor detection, could be performed
in seconds instead of hours or weeks.

·        
Computer science: Faster multidimensional search
functions, for example, query optimization, mathematics and simulations.

Entry Barriers

There are a number of technical challenges in building a
large-scale quantum computer, and thus far quantum computers have yet to solve
a problem faster than a classical computer.

Business Risks

·        
Supply chain needs more development. Stronger
links are needed between companies in the supply chain, including components
suppliers.

·        
new skills and expertise and understanding

·        
There is lack of understanding of what benefits
quantum computing will offer

Technical Risks

·        
scalable physically to increase the number of
qubits;

·        
qubits that can be initialized to arbitrary
values;

·        
quantum gates that are faster than decoherence
time;

·        
universal gate set;

·        
qubits that can be read easily.

Players in Quantum Computing Race

·        
Components manufacturers- such as Toptica in
Laser technologies, and e2v (UK) in vacuum electronics and photonics

·        
Manufacturers of quantum devices- such as
IDQuantique selling quantum random number generators and quantum key distribution
systems, and Muquans selling quantum gravity sensing devices and atomic clocks.

·        
Multinational enterprises- such as IBM, Toshiba
and Bosch who are interested in developing systems based on quantum
technologies

·        
End Users- such as Airbus and Alcatel-Lucent who
are interested buying solutions, but who may not necessarily be interested in
the underlying technology.

Conclusions and Recommendations

Not every Company needs to worry about quantum computing,
but for now, those looking to explore the technology should focus their data
scientists on the advancement of quantum algorithms and how they can be applied
to solve practical business problems. Quantum programming will require a
significant learning curve. Gartner recommends getting ahead of the curve by
leveraging QCaaS, GitHub tools and SDKs. Applying quantum algorithms to
real-world problems will provide the greatest competitive advantage well into
the future.

Many of the required skills reside in companies and
therefore funding must be made available for projects which are led by
companies, performed within companies and in collaboration with the academia.
The projects should be looking to deliver tangible and functional outputs such
as working demonstrator units. Projects should support patent applications,
allow for testing, validation and, if necessary, standardization tasks.

An industry leadership group must be created who will represent
the views of industry in this emerging sector, and provide direction to other
individuals and organisations seeking to deliver commercialised products or
strategies for commercialisation.

Deloitte needs to identify and clarify markets for quantum
technologies by supporting technical and non-technical projects that look to
understand the potential benefits, relative to alternative solutions and in
real world environments. This task will clarify the market, use and business
cases for quantum technologies. Deloitte needs to identify players and tech
providers to partner with. Deloitte also need to identify and work with anchor
clients to validate business case, value and ROI and pilot Quantum Computing
use cases with the partners.