R&D advances represent the muscles of any advanced nation, and quantum computers are becoming essential for enabling great achievements in this area. The big economic powers are trying to move at the speed of light to establish their own quantum programs now. Investments in quantum computing market today is forecasted at US$177 million, but the total revenues are expected to substantially increase to just over US$15 billion by 2028.
So why are advanced nations investing heavily in quantum computing? Simply because this technology will enable them to stay ahead in terms of R&D advances, which will in turn allow them to lead the deployment and commercialization of cutting-edge technologies in the future. For this reason, quantum computing represents a tremendous opportunity for hyper-scale processing service providers and technology makers that are currently looking at upgrading facilities using quantum technology to support new use cases that would not be possible using classical computers. ABI Research has investigated the role quantum technology will play in transforming the cloud business, particularly in hyper-scale computing.
Quantum computing is a breakthrough in computing technology. Classical computers today struggle with processing some optimization calculations required for major R&D projects across many segments, including material science, medicine, nuclear physics, as well as a number of optimization approaches deployed for financial predictions, advanced AI (Artificial Intelligence) algorithms, and weather forecasts. These types of problems are extremely challenging, firstly because of the overwhelming number of instances (or instructions) they require but most importantly because these instances are coupled and cannot be processed separately. New processing approaches based on parallelism and entanglement are needed to solve these types of problems, and this is where quantum computing steps in.
Key Principles of Quantum Advantages
But what is quantum computing and why is it so relevant and important that countries are playing a strategic game of placing themselves as the leaders in quantum science?
Classical computers are very good at solving problems by breaking them down into a series of tasks that can be processed one by one in a sequential fashion. This approach proves successful in processing many types of information we deal with today, including, signal, graphic, or data processing. However, this approach is inefficient in solving complex problems that cannot be easily broken down into smaller independent tasks and may overwhelm classical computers by potentially exhausting their entire horsepower.
We are now in a completely new era where the development of major breakthrough technologies is based on the understanding of complex mechanisms that require a deep knowledge of their quantum properties. Solving problems and situations in silos (the classic way) without considering the environment they operate in is no longer enough to provide adequate solutions or analyze patterns in the way these situations or problems will develop.
Quantum computing takes a completely new approach in addressing these complex problems. Instead of breaking them down in small independent tasks and executing them in a sequential manner, quantum computers stream their strength from their quantum qualities, which are at the heart of their physical structure. Quantum computers are based on quantum gates that are able to exist in multiple states at the same time as opposed to the binary “on” or “off” states exhibited by the gates of classical computers, which are based on CMOS transistors. The quantum qualities of these machines enable them to simultaneously perform an indefinite number of superposed tasks at the same time and using the same piece of hardware. Another strength of quantum computing is entanglement. This basically describes a situation where two or more events or streams of data are tightly correlated to the point that one cannot describe them independently. Processing this type of data over classical computers require sophisticated algorithms while a quantum computer could easily deal with this situation, as long as the entanglement or correlation factor is identified or measured. Key use cases where the entanglement could be used are numerous.
Given its capabilities, quantum computers are not about replacing their classical counterpart, but rather addressing completely new types of use cases that cannot be dealt with using classical computers. These use cases span across different markets, from military applications to R&D projects. The lion’s share of use cases for 2028 do not even exist today, but every day there are new use cases emerging. Here are a few emerging use cases:
The U.S. government responded to China’s quantum radar breakthrough in September by issuing the “National Strategic Overview for Quantum Information Science” report, which is the product of the Subcommittee on Quantum Information Science under the Committee on Science of the National Science & Technology Council (NSTC). This paper gives a high-level overview of the importance of quantum computing, especially in military applications:
“Quantum information science (QIS) applies the best understanding of the sub-atomic world—quantum theory—to generate new knowledge and technologies. Through developments in QIS, the United States can improve its industrial base, create jobs, and provide economic and national security benefits,” states the report. “Advanced computing capabilities have long been used to enhance both military capability and economic productivity. As such, general purpose quantum algorithms for optimization, machine learning, materials development, and chemical calculations should continue to be explored; although their quantum speed-up is still unknown, any improvements in direct computational ability or in resulting materials and systems could greatly impact military effectiveness.
“Beyond computation, new or quantum-enhanced systems could enable the next-generation of sensors and detectors for defense applications. As an example, precision relies on the deep understanding of the quantum properties of atoms; further development can impact both next generation Global Positioning Systems (GPS) and scenarios where GPS is unavailable. There are further synergies in defense requirements for low size, weight, and power devices via new modalities of sensing.”
When it comes to deciding the future of a company, it is no longer that the shares depend on the technologies they are involved in, but also the political environment, what types of markets they are addressing, the health of those markets, as well as numerous other factors. There are a lot of problems and situations that need to be solved all at one time and all of them are inter-related, namely they are feeding each other. In normal circumstances, classical computers will be deployed. However, a classical computer can only take one problem and one situation at a time. You create the algorithms, which are then placed in sequence (a.k.a. sequential computing). But if you follow this classical computing approach, by that time you want to predict the financial health of a company, it will already be too late.
In every situation even dealing with complex technologies, we do exactly the same thing. For example, in cybersecurity, hackers are using sophisticated tools. Whatever you do with sequential computing in regard to classical computing, hackers will find a pattern to exploit. They create the data and see the patterns, and those patterns will give them the holes in your data to exploit.
In a quantum computing paradigm, you will instead create a completely randomized environment. So when the data is exposed to the network, what hackers will see is just random data to the point it is impossible to identify any pattern to exploit and decrypt. The key you use to randomize data transferred is called an entangler. It is impossible to decrypt this data unless if you have that entanglement key. The entanglement key could then be shared separately to the recipients, enabling them to decrypt while navigating through the information.
Generative and creative learning refers to a machine learning setting where new information is generated and validated against a given set of rules and constraints. For instance, manufacturers and designers can model or create product prototypes that will fit all the requirements based on the different assumptions, parameters, and constraints in product development.
Due to the processes and complexity involved, generative and creative learning are often considered the most resource intensive AI technique. The parallel processing of quantum computing can run through all these assumptions, parameters and constraints at the same time, each being incorporated into the design and accepted and rejected concurrently. The days, or even weeks, taken by classical computers to run will be significantly shrunken. The end result of this will be more saving in man-hours and quicker time to market.
Drug synthesis is another emerging use case with quantum computing. For example, perhaps you want to understand various chemical ingredients and how they could be shaped into a new type of pill. You need to consider the microscopic or atomic feature of different chemical ingredients — including how the atoms are organized, their chemical and physical behaviors, etc., — to be able to see how different atoms are interacting with each other to create that drug effect you have in mind.
We are taking the new pill as a formation of billions and trillions of atoms that interact with each other. By going to that level, you understand the chemical and physical properties, and even predict any new characteristic of these chemical ingredients you are studying. This can’t be done with classical computing — only with quantum computing.
Quantum computers are ideal to give more accurate representation of how these chemical ingredients interact with each other and could predict a new drug phenomenon that could translate to a new cure and treatment, and even new commercial opportunities.
In any environment, problem solving is about multiple problems or many tasks that need to be dealt with in real time, all at once and all those tasks are inter-related.
In the smart city, you have a number of situations that are interdependent: public safety, environment, traffic monitoring. For example, in a traffic jam, all the vehicles will expend more fuel, which will impact the environment. If the environment is bad, that will impact the traffic jam as well. When you have a very polluted environment, you will route the traffic somewhere else. You have the traffic impacting the environment, and as a result the environment is impacting the traffic. If you want to solve that problem with classical computing, it will take years. But you need an answer in real time to route the traffic in a way to minimize the impact on the environment.
To summarize, quantum computing is not here to solve things that classical computers are already solving or even enhance the performance of classical computing. Quantum computing is about addressing new use cases. Actually, classical computers are very good at what they do, and they are still largely relevant in dealing with classical applications, such as Internet data processing, video processing, voice processing, or signal processing, despite the fact Moore’s law is slowing down.
The demands and needs for quantum computers are currently driven by a national level technology arms race, with the aim to be the first to identify the right approach to achieve the best performance while enabling quantum computing to be more coherent and cost-effective to the point it could be commercially sustainable. This race reminds us of the level of competitions and confidentialities that surrounded the development of the transistor technology back in the 1960s, which is now the backbone of the current classical computers and the engine of Moore’s law as we know it today.
Quantum computing and quantum science are the fundamental components to the race for the next-generation economic curve and leadership. Whoever can master this quantum science and take it to commercial applications will have an edge in terms of technology development.
About the Authors
Malik Saadi, Managing Director and Vice President, Strategic Technologies, is focusing on technology innovation across various industries, including telecommunications, consumer electronics IoT, and other emerging industries. With more than 16 years of experience in the telecommunications and computing industries as a technology expert and analyst, he guides his research team toward uncovering the impact of technology innovation on different industries and markets, with the ultimate goal is to provide clients with both quantitative and qualitative vision of the overall market development and how the various technologies involved will empower this development.
Lian Jye Su, Principal Analyst at ABI Research, is responsible for orchestrating research relating to robotics, artificial intelligence, and machine learning. He leads research in emerging and key trends in these industries, deep-diving into advancements in key components, regional dynamics in robotics and AI adoptions, and their future impacts and implications.
About ABI Research
ABI Research provides strategic guidance for visionaries needing market foresight on the most compelling transformative technologies, which reshape workforces, identify holes in a market, create new business models and drive new revenue streams. ABI’s own research visionaries take stances early on those technologies, publishing groundbreaking studies often years ahead of other technology advisory firms. ABI analysts deliver their conclusions and recommendations in easily and quickly absorbed formats to ensure proper context.
Executive Foresights, “Why Quantum?” Ryan Martin, Oct. 2, 2018
Webinar: “Quantum Computing: Early Insight on Commercial Viability, Use Case and Timeline,” Malik Saadi and Lian Jye Su, Sept. 26, 2018
Application Analysis Report, “Quantum Computing: Core Technologies, Development, and Use Cases,” Malik Saadi and Lian Jye Su, July 10, 2018