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Rise of Quantum Computing and its use cases

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

  1. In 2021 alone, announced investments in quantum-computing start-ups have surpassed $1.7 billion, more than double the amount raised in 2020.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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?”

References

  1. Quantum computing: An emerging ecosystem and industry use case. McKinsey & Co.
  2. Quantum computing use cases are getting real—what you need to know
  3. Macro trends in the tech industry
  4. The WIRED Guide to Quantum Computing
  5. Quantum computers: what are they good for?
  6. Explainer: What is a quantum computer?
  7. A game plan for quantum computing