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.

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.