Introduction
Quantum Computing has been around since the early 1980s. It has been up for
debate ever since Richard Feynman initially coined the term and proposed a
possible way of amalgamating quantum mechanics with the revolutionary invention
of computers.
The term Quantum Computing has a certain enigma attached to it; even after
theorizing over it for a relatively long time, we are yet to see practical
implication of this phenomenon touted to be a 'cultural reset' as far as
technology and computers are concerned.
Theorizing probable problems that can be solved through quantum computing is one
thing, realizing these theories and implementing them on a mass scale is another
question that keeps glaring at scientists & researchers unflinchingly.
This is not to deny that there haven’t been experiments to put these theories to
test and IBM has been successful in developing a 433 qubits computer. These
theories have even proved to stand their ground, but the argument that we’re
trying to drive is that despite the long discussions, debates & research gone
into materializing quantum computing the results are at a rather nascent stage
i.e., we are yet to see all the loose ends of quantum computing being tied &
stitched together into a flawless and fully functioning system.
This article discusses the challenges accounted for quantum computing to be as
prevalent as AI is today. Keep reading...
Quantum Computing Facts
- In 2021 alone, announced investments in quantum-computing start-ups have
surpassed $1.7 billion, more than double the amount raised in 2020.
- The United States is home to the highest number of quantum-computing
start-ups, with software seeing the highest level of global start-up growth.
- In 2000, scientists at IBM built a 5-qubit quantum computer and used it to
demonstrate Shor's algorithm. In 2019, Google claimed quantum supremacy when
their 53-qubit quantum computer performed a calculation in 200 seconds that
would take a supercomputer 10,000 years.
- 2020 alone saw about $700 million in private funding for quantum technology
startups. Announced private investments for 2021 are already double this
amount, bringing the total private investment in quantum computing from 2001
to 2021 to more than $3.3 billion.
- In 2022, venture capitalists plowed a record $1.8 billion into companies
working on quantum computing hardware or software worldwide, according to
data from Pitchbook. That’s nearly five times the amount invested in 2019.
- Announced public investments in quantum computing are even higher: nearly
$30 billion to date.
Challenges
Quantum Computing occupies a liminal space in the world of technology—— we’re not
very far from having an fully functioning quantum computer operating on a
regular basis but we’re not close enough (ready) to quantum computing to be
fully operational.
1. Hardware
Researchers are sceptic about having a fully functional quantum computer until
they are not completely fault tolerant. A report by Mckinsey also suggests that
it is
not until 2030 that we may have fully fault-tolerant computers.
Although this does not render quantum computers unusable but may negatively
affect their business value.
2. Software
McKinsey states “Quantum computing requires a new programming paradigm— and
software
stack.” It goes without being said companies are yet to build robust software
that supports full-fledged quantum computers. A commercial quantum computer
requires
100x more qubits than what we can deliver today. Not to forget generating and
managing qubits is an engineering challenge in that a stable qubit requires
temperatures
to be cooler than deep space.
3. Talent gap
To gain command over a field as niche as quantum computing requires a person to
bridge a gap between diverse fields like computer science and an in-depth
knowledge in concepts of physics like superposition, entanglement, and other
quantum
concepts.
5. Suitable Environment
Another hurdle in the way of quantum computing is the volatile nature of qubits.
Compared to conventional computers that either exist in 1s or 0s, qubits exist
in any combination of both digits. Eventually when the status of a qubit
changes,
the chances of qubits losing their inputs and even throwing off the results
accuracy.
We've still got a long way to go when it comes to having a robust ecosystem which
can help quantum computing thrive.
UseCases
Quantum Simulation
McKinsey defines Quantum Simulation as “simulation of quantum-mechanical systems
or processes such as molecules, chemical reactions, or electrons in solids.
Conventional computers available to us solve problems on an approximate basis or
not at all, this is because current conventional computers do not have the
capability to fully simulate quantum systems. This slows down the process
considerably without any respite in the immediate future.
Industry Use Cases: Quantum Simulation use cases are mostly applicable in
the pharmaceuticals and chemicals industries for tasks such as lead
identification
or catalyst optimization.
Quantum Linear Algebra
This sub-field of quantum computing uses quantum algorithms
to solve problems in linear algebra more efficiently than classical algorithms.
They can speed up complex algorithms but require a defined hardware to be able
to
process these algorithms. They are mostly applied in AI/ML, when deployed at
scale, this
processing will be useful for automation of complex tasks such as providing
financial advice.
Industry Use Case: Quantum linear algebra enhances optimization,
simulations, and data analysis in
finance, healthcare, logistics, AI, telecommunications, automotive industry etc.
Quantum Optimization
Mckinsey again defines quantum optimization as a technique that “could find
better solutions in the same amount of time and solve previously intractable
problems”. Quantum optimization can increase the speed of
calculations quadratic times as compared to present day systems. When deployed
at scale, this process will be useful for automation of complex tasks such as
providing financial advice.
Industry Use Case: Quantum optimization is most helpful in portfolio
optimization
within finance, where it helps balance risk and return more efficiently than
classical methods, enhancing investment decision-making.
Quantum Factorization
This is the most widely known use case of quantum computing.
“Efficient quantum factorization is most readily applicable to breaking RSA
encryption, the basis of most of today's secure data-transfer protocols.”
(McKinsey,
18) The quantum factorization algorithm leverages quantum parallelism and
entanglement to achieve this speedup. It requires a sufficiently powerful
quantum
computer with stable qubits and low error rates.
Industry Use Case: Quantum factorization, primarily through Shor's
algorithm, has
several prominent industry use cases due to its potential to solve the integer
factorization problem exponentially faster than classical algorithms. These
include: Cryptography & security; Blockchain & digital signatures; Cloud
computing & data storage
Business Growth
Quantum computing has still got a lot of roads to cover until we start to see the
business impact and difference in numbers that quantum computing has brought
about for organizations that have already invested in them. Even before we see a
full-fledged functioning quantum computer, McKinsey claims that around 2030,
quantum computing use-cases will have a hybrid operating model that is a cross
between quantum and conventional high-performance computing. For example,
conventional high-performance computers may benefit from quantum-inspired
algorithms.
However, based on the investments made so far, an estimated $300 billion to $700
billion could be at stake. Here are key industries that may see a surge:
- Pharmaceuticals: For a new drug to be discovered and reach the public
it takes
somewhere around $2 billion and more than ten years. This R&D can be
comparatively cut short through quantum computing. This means a minimum
increase
of $15 billion to $75 billion in additional revenues. (McKinsey)
- Chemicals: In the chemicals sector quantum computing can help improve
R&D,
production and even supply-chain management. If the industry gains in value,
then they could increase their revenue between $20 billion to $40 billion in
value. (McKinsey)
- Automotive: We can expect the automotive industry to improve their
product
design, production, mobility and traffic management, etc. Through the
integration of quantum computing. Researchers at Mckinsey state that with an
annual spend of $500 billion in manufacturing and a mere gain of 2-5% the
annual
revenue could hike somewhere between $10 billion to $25 billion per year.
- Finance: Finance is one industry that may these gains the latest of
all these
industries mentioned. The most prominent areas of improvement are portfolio
&
risk management. Mckinsey& Co. Report that the industry already stands at
$6.9
trillion, at the global lending market and quantum optimization will only
take
their benefits further.
Our Services
At MetricDust we integrate Quantum Computing power to Artificial Intelligence,
Machine Learning and Cyber Security applications. Our Quantum capabilities
include:
1. Quantum Cloud Services:
An in-house fintech model which enhances security in financial transactions and
fraudulent activities along with optimization of pricing models with highest
accuracy.
2. Quantum Simulators:
We developed a model which could simulate the financial market behavior
accurately and rapidly by assisting in understanding the market dynamics
simulating & predicting the behavior accurately.
Conclusion
As per the extensive research conducted by scientists and industry experts,
transitioning quantum computing to a mainstream technology is dependent on six
key factors—funding, accessibility, standardization, industry consortia, talent,
and digital infrastructure.
The biggest obstacle in the road to finally realizing this dream is the fallacy
within the functioning of quantum machines themselves. Compared to existing
systems quantum computers are way more error-prone and this is mainly because of
decoherence.
The chasm between the theoretical brilliance of quantum computing and the
practical applications of it can be tied together by the following statement by
Freeke Heijman “What we're looking at is not only the technological roadmap, but
an ecosystem...how do you get the right talent, how to educate the right people
to move into the field?”
