The Rise of Quantum Computing

Quantum computing is a specialized technology that utilizes computer hardware and algorithms based on quantum mechanics to solve complex problems that classical computers and supercomputers cannot solve. While supercomputers can run advanced artificial intelligence and perform large calculations, they are still reliant on 20th-century transistor technology and struggle to solve certain kinds of problems. When classical computers fail, it's often due to complexity. There are some complex problems that we do not know how to solve with classical computers at any scale. This is where quantum computing comes in.

By utilizing the quantum states of quantum bits, quantum computers can provide solutions to problems with a high degree of complexity, including identifying subtle patterns of fraud in financial transactions or new physics in a supercollider. With quantum computing, we have a powerful tool that can help us better understand and navigate the complexities of the real world, which ultimately runs on quantum physics.

Digital computers have been a game-changer in processing information for decades. But have you heard about quantum computers? They're poised to take computing to a whole new level. Quantum computers use the principles of quantum physics to solve complex statistical problems that today's computers can't. They represent a completely new approach to computing and are one of the next big trends in tech. According to McKinsey, quantum computing alone could account for nearly $1.3 trillion in value by 2035. Keep an eye on this emerging technology, as it has the potential to revolutionize the way we think about computing.

Unlike classical computing, which operates on bits that can store either a zero or a one, quantum computing operates on quantum bits, or qubits, that can store both zeros and ones simultaneously. This is called a superposition, which allows qubits to represent any combination of both zero and one. This technology has the potential to revolutionize industries ranging from healthcare to finance.

Quantum computing is a rapidly evolving field that promises to revolutionize the way we approach complex problems. Over the next few years, the major players in quantum computing, as well as a small cohort of start-ups, will steadily increase the number of qubits that their computers can handle. However, progress is expected to be slow: McKinsey estimates that by 2030, only about 5,000 quantum computers will be operational. The hardware and software required to handle the most complex problems may not exist until 2035 or later. Stay tuned for more updates on the exciting world of quantum computing!

One of the biggest hurdles in the world of quantum computing is the volatile nature of qubits. While a traditional bit can only be in a state of one or zero, a qubit can be any combination of the two. This volatility makes it difficult to maintain accuracy and can lead to lost or altered inputs. Additionally, a quantum computer capable of delivering significant breakthroughs will require millions of connected qubits - a far cry from the few existing quantum computers today. The road to quantum computing may be long, but the potential rewards are enormous.

Quantum computers have four fundamental capabilities that differentiate them from today’s classical computers:

Quantum simulation - Quantum computers are able to model complex molecules, which may eventually help reduce development time for chemical and pharmaceutical companies. Scientists looking to develop new drugs need to examine the structure of a molecule to understand how it will interact with other molecules. It’s almost impossible for today’s computers to provide accurate simulations, because each atom interacts with other atoms in complex ways. But experts believe that quantum computers are powerful enough to eventually be able to model even the most complex molecules in the human body. This opens up the possibility for faster development of new drugs and transformative new cures.

Optimization and search - Every industry relies in one way or another on optimization. Where should I place robots on the factory floor? What’s the shortest route for my delivery truck? There are almost infinite questions that need to be answered to optimize for efficiency and value creation. With classical computing, companies must make one complicated calculation after another, which is a time-consuming and costly process given the many variables of any situation. Since quantum computers can work with multiple variables simultaneously, they can be used to quickly narrow the range of possible answers. From there, classical computing can be used to zero in on one precise answer.

Quantum AI - Quantum computers have the potential to work with better algorithms that could transform machine learning across industries as diverse as pharmaceuticals and automotive. In particular, quantum computers could accelerate the arrival of self-driving vehicles. Companies like Ford, GM, Volkswagen, and numerous mobility start-ups are running video and image data through complex neural networks. Their goal? To use AI to teach a car to make crucial driving decisions. Quantum computers’ ability to perform multiple complex calculations with many variables simultaneously allows for faster training of such AI systems.

Prime factorization - Businesses today use large, complex prime numbers as the basis for their encryption efforts, numbers too large for classical computers to process. Quantum computing will be able to use algorithms to solve these complex prime numbers easily, a process called prime factorization. Once quantum computers have advanced enough, new quantum-encryption technologies will be needed to protect our online services. Scientists are already at work on quantum cryptography to prepare for this eventuality. McKinsey estimates quantum computers will be powerful enough for prime factorization by the late 2020s at the very earliest.

Research shows that the potential value at stake for the pharmaceutical, chemical, automotive, and finance industries from quantum computing could be as much as $1.3 trillion.

The pharmaceutical industry could revolutionize the research and development of molecular structures in biopharmaceuticals. Quantum computing could make drug research and development less reliant on trial and error, leading to more efficient processes.

In the chemical industry, quantum computing could improve catalyst design, saving on production costs and enabling the use of more sustainable feedstock.

The automotive industry could benefit from quantum computing in R&D, supply chain management, production, and mobility and traffic management. For example, quantum computing could decrease manufacturing costs by optimizing complex multirobot processes, such as welding, gluing, and painting.

While quantum-computing use cases in finance are further in the future, the long-term promise lies in portfolio and risk management. One example could be quantum-optimized loan portfolios that focus on collateral to allow lenders to improve their offerings.

Although these four industries stand to gain the most from quantum computing, leaders in every sector should prepare for the inevitable advancements of the next few years.

A wide talent gap exists between the business need for quantum computing and the number of quantum professionals available to meet that need. This skill gap could jeopardize potential value creation, which McKinsey estimates to be as much as $1.3 trillion.

McKinsey research has found that there is only one qualified quantum candidate for every three quantum job openings. By 2025, McKinsey predicts that less than 50 percent of quantum jobs will be filled, unless there are significant changes to the talent pool or predicted rate of quantum-job creation.

Here are five lessons derived from the AI talent journey that can help organizations build the quantum talent they need to capture value:

Define your talent needs clearly - In the early days of AI, some organizations hired data scientists without a clear understanding of what skills were needed. To avoid making the same error with quantum, organizations should first identify possible fields of applications that a quantum-computing team would work on and then ensure that new hires come from diverse backgrounds (reflecting best practices).

Invest early in translators - As buzz built up around AI, the role of analytics translators became crucial to helping leaders identify and prioritize challenges best suited for AI to solve. With quantum, there’s a similar need: for translators with engineering, application, and scientific backgrounds who can help organizations understand the opportunities and players in the rapidly expanding ecosystem.

Create pathways for a diverse talent pipeline - Many of the first AI models reflected the same biases that were present in the information that was used to train them. There often was also a lack of people with diverse perspectives and experience building and testing the models, which contributed to the bias issue. While it’s too early to know what risks will emerge from quantum technologies, we can expect similar challenges if we don’t build and empower a diverse quantum workforce. Efforts are needed at the university level, as well as in K–12 education.

Build technology literacy for all - In order for employees at all levels of an organization to understand the potential of a new technology, they need a basic understanding of how it works and what it can do. With quantum, business leaders as well as workers up and down the supply chain, in marketing, IT infrastructure, finance, and more will require basic fluency in quantum topics.

Don’t forget talent development strategies - Companies focus heavily on talent attraction during times of technological foment—but that’s just one piece of the talent puzzle. In order to retain specialists, companies need to carve out clear paths for talent development. One pharmaceutical company leans into both the purpose of its work—developing use cases that will help save lives—and the freedom it offers its team.

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