Quantum computers have shown the potential to be game-changing for large-scale industries in the near future. Quantum solutions (hardware and software), in their prime, have the potential to put humankind on planet Pluto with their optimized calculations. According to a Gartner report, 20% of organizations will be budgeting for quantum computing projects by 2023 (The CIO’s Guide to Quantum Computing, https://tinyurl.com/yrk4rp2u). This technology promises to achieve better accuracy and deliver real-world experiences via simulations. This book delves into the potential applications of quantum solutions to solve real-world financial problems.
In this chapter, we will discuss various computing paradigms currently in the research phase. A chronicle of quantum computing is also curated and presented. Then, we will cover the limitations faced by classical computers and how these challenges will be overcome with the help of quantum computers. After that, the role of quantum computing in shaping the next generation of business is defined.
Later in the chapter, we will go through the basics of quantum computing. The types of hardware powering quantum computers are described in a subsequent section. We will also look into the potential business applications of this technology and how organizations can align their business strategy accordingly to harness their true potential.
The following topics will be covered in this chapter:
Computing paradigms can be defined as the significant milestones that have been achieved over the years. To say that computers have made the lives of humans easier is an understatement. On a daily basis, we need machines that can analyze, simulate, and optimize solutions to complex problems. Although the shapes and sizes of computers have changed over time, they still operate on the doctrines proposed by Alan Turing and John von Neumann.
In this section, we will study the evolution of quantum technology over the years. We will also study some of the technology’s limitations in the face of certain business challenges.
Turing showed us the types of problems computers can solve, von Neumann built programmable computers, and Michael Moore’s pioneering work in semiconductors made computers more capable. Figure 1.1 shows the advancement of computing paradigms over the years, and their ability to affect growth in human history:
1821 |
Mechanical calculator |
Has Enabled humans to migrate from mechanical devices to electronic devices with better accuracy in calculations. |
1890 |
Punch-card system |
Demonstrated first use case of large-scale computing by aiding in the US Census. |
1936 |
Turing machine |
Theoretical conceptual framework was laid down to solve large computational problems. |
1941 |
Digital electronic computer |
First time a computer was able to store information on its main memory. |
1945 |
Electronic Numerical Integrator and Calculator (ENIAC) |
First digital computer to perform large class of numerical problems through reprogramming. |
1958 |
Integrated Circuit (IC) |
Helped in the transition of enterprise-level computing to personal computing. |
1976 |
Cray-1 Supercomputer |
Aided 240 million calculations useful for large-scale scientific applications and simulations. |
1997 |
Parallel computing |
Multiple-CPU core was used to solve complex problems in a limited timeframe, enabling Google to form a better search engine. |
2006 |
Cloud computing |
Technology has enabled users to access large computational resources from remote locations. |
2016 |
Reprogrammable quantum computer |
Offers a better platform to solve complex simulation or optimization problems in comparison to classical computers |
2017 |
Molecular informatics |
Harnesses molecular properties for rapid, scalable information storage and processing. |
Figure 1.1 – Evolution of computing paradigms
The evolution of computing technology has enabled humans to evolve from an agrarian society to an industrial society. Progress in computing prowess has catapulted society from bartering goods to building e-commerce platforms. Figure 1.1 has given a conclusive summary of how computing technology has benefitted society through its progression from a device that merely performs calculations to the multifunction device in its present form. In the next section, we are going to assess the challenges faced by large-scale businesses and the limitations of current digital technology in addressing them.
Current digital technologies have advantages as well as limitations in providing solutions and insights in real time. The rise of numerous variables and their increasing complexity can affect decision-making in the real world. It is essential to have technology that is reliable and accurate, and fast-paced at the same time. The need for a reliable technology stack has prompted scientists worldwide to investigate technology that is beyond the reach of humans. The current challenges faced by large-scale businesses are as follows:
In order to perform a business task more efficiently and optimally, the business fraternity has started looking for technological solutions. Digital computing in its current state has helped businesses to achieve more efficiency via automation and augmented intelligence. However, current hardware technology has not been able to solve a few complex tasks, which can be associated with an abundance of data and the limitation of computing memory. The following section highlights the types of problems that can be solved by digital computing, and other problems that have generated the need to look beyond the current computing paradigm.
Digital computers are powered by integrated circuits (ICs), a technology that reached its peak in the 20th century. According to Moore’s law, the number of transistors powering microchips will double every year. In 2021, IBM announced that it can fit 50 billion transistors into its 2 nm chip technology, which basically allows a chip to fit in a space the size of a fingernail. The presence of a large number of transistors has enabled the classical computer to perform large calculations and complex procedures that help in solving day-to-day problems much faster.
However, due to internal leakages and the miniaturization effect, classical gates (OR and AND gates) have been showcasing the quantum effect. Also, digital computers are traditionally unable to solve NP-hard problems (Figure 1.2). In layman’s language, NP-hard problems are measured by the amount of time it takes to solve a problem based on the complexity and number of variables. An example of this, as discussed previously, is how to choose the optimum route out of the 8,543,811,434,435,330 combinations determined for charging station locations in the San Francisco Bay Area. While it would take years for a classical computer to solve the aforementioned problem, ideally, quantum computers can solve it in 3 seconds.
Figure 1.2 – Classification of NP-hard problems based on level of complexity
To understand the limitations of classical computers in a better way, imagine that you have to pick a portfolio of 100 penny stocks with a limited budget, and let’s assume that the prices are discrete (for example, tick size on stock markets). Suppose you have to construct a portfolio in polynomial time (p = problems a computer can solve in a reasonable amount of time) and assume that it takes 100 steps (n = no. of inputs) to obtain an optimized portfolio or, in other words, n3 time. Theoretically, digital computers will solve the problem in three hours. This problem was easy to solve, and experts can easily verify the solution since we are dealing with stocks of the same class. Hence, we can confidently say that p-class problems are easy to check and solve. Now, the same problem but with a variation (a portfolio optimization of 100 stocks belonging to different risk classes in a limited time) will take around 300 quintillion years to solve, because although the solution is verifiable in a polynomial (n) timeframe, it is obtained in an exponential (NP) timeframe. This problem is classified as an NP problem. For an analogy, imagine a sudoku or tic-tac-toe problem: this is an NP problem for which it is difficult to obtain the solution (it takes exponential time), but easy to verify in polynomial time.
Following on from the preceding discussion, four types of NP problems that are deemed difficult to be solved by digital computers are as follows:
To summarize, it will suffice to say that with the limitations observed in current technology, it is time to explore new computing paradigms that can help solve the problems faced by the business fraternity at large and help the industry bring in innovations and creativity.
Quantum computers use principles and theories (such as quantum field theory and group theory) to describe the quantum mechanics phenomenon. Quantum mechanics principles, such as superposition, decoherence, and entanglement, have been utilized to build processors that process and relay information at exponential speed. The following section maps the quantum computer’s evolution journey and briefly describes quantum mechanics principles.
For a long time, advances in digital computers at economies of scale have suppressed the development of other computing paradigms. Moore’s law (Figure 1.3) has predicted exponential growth and advancement in the microprocessor. However, the presence of a large amount of data collected over decades of computing advancements has put a limitation on computing power, storage, and communication. To overcome the limits of the current architectures, we must overcome challenges such as finite memory, self-programmable computers, large number factorization, and faster microprocessors.
Figure 1.3 – Transistor number growth according to Moore’s law
Looking at the current limitations of digital computers due to their fundamental principles and assumptions, there is a need for new computing paradigms to emerge. To solve the problems related to various domains related to climate, process automation, industry mechanizations, and autonomous systems, there is a need to overcome the current challenges. Quantum computing, molecular computing, nature-inspired algorithms, and synergistic human-machine interaction (Computer’s Special Issue September 2016 Examines “Next-Generation Computing Paradigms,” IEEE Computer Society, https://tinyurl.com/4b5wjepk) are the current areas of interest and innovation in the pursuit of overcoming the aforementioned challenges. Figure 1.4 charts the journey and impact of the quantum-computing paradigm from theoretical to practical application:
Year |
Phenomenon |
Effect |
1905 |
Photoelectric effect was discovered by Albert Einstein and discovery of photon took place. |
Laid the foundation to discover quantum behavior in atomic particles. |
1924 to 1927 |
Max Born coined the term Quantum Mechanics and Heisenberg, Born, and Jordan discovered matrix mechanics. |
Discovery of quantum mechanics principles, which were harnessed to produce the quantum processor. |
1935 |
Erwin Schrödinger conceptualized and wrote his thought experiment known as Schrödinger’s cat. |
The principle of quantum entanglement was discovered. |
1976 |
Quantum information theory was proposed by Roman Stanisław Ingarden. |
Quantum Information science as a discipline was formulated, which laid the foundation for quantum algorithms. |
1981 |
Richard Feynman proposed that a quantum computer had the potential to simulate physical phenomena. |
The practical application of quantum mechanics was harnessed to develop working quantum computers. |
1994 |
Shor’s algorithm for factoring integers was discovered. |
Formulated the basis of cryptography for post quantum key distribution. |
1996 |
Grover’s algorithm was discovered. |
Laid the way for storing information in database form. |
2011 |
D-Wave offered the first quantum computing solution using quantum annealing. |
Opened up the possibilities of using quantum computers for commercial purposes. |
2019 |
Google claimed quantum supremacy. |
Showed a use case of quantum supremacy that can help in better encryption. |
2021 |
IBM unveiled the first 127-qubit quantum computer named Eagle. |
Facilitated faster processing of the complex NP-hard problem. |
Figure 1.4 – Journey from quantum mechanics to quantum computing
As you can see from the evolution point of view (Figure 1.4), quantum technologies are making rapid strides to overcome problems such as accurate simulation, efficient optimization, and correct pattern recognition. Once researchers can overcome the related problems that limit current users, and implement quantum technology to solve day-to-day problems, one can see how the industry-wide adoption of quantum technology can solve large-scale problems.
The next section describes some of the common terminologies and principles of quantum mechanics used in building and operating quantum computers.
Deciphering the quantum mechanics principles involved in quantum computing is an uphill task for a layperson. This section describes each quantum mechanics postulate in easy-to-understand language, explaining how it is involved in the quantum computing mechanism.
Postulate |
Definition |
Usage |
Further Reading |
Qubits |
The qubit is a basic unit of quantum information stored on a two-state device encoding information in 0s and 1s) · |
Facilitates faster processing of information for complex processes like simulation and optimization. |
What is a qubit? (quantuminspire.com) |
Quantum State |
Quantum state is the position and value of attributes (change and spin) of atomic particles obtained naturally or induced by creating physical environments (e.g. laser and heat). |
Used in processing and transforming information using qubits in a controlled environment. |
Superposition and entanglement (quantuminspire.com) |
Quantum Superposition |
It refers to a phenomenon that tells us that quantum superposition can be seen as the linear combination of quantum states. |
This property makes it hard for a system to decrypt quantum communication and thus provides a safer way to transfer information. |
Superposition and entanglement (quantuminspire.com) |
Quantum Entanglement |
Quantum entanglement refers to the linking of two particles in the same quantum state and the existence of correlation between them. |
Facilitates the ability of a system to do calculations exponentially faster by more and more qubits. |
Superposition and entanglement (quantuminspire.com) |
Quantum Measurement |
A set of mathematical operators to understand and measure the amount of information that can be recovered and processed from qubits. |
Useful in understanding the complexities of quantum mechanics. |
Quantum measurement splits information three ways - Physics World. |
Quantum Interference |
It refers to the ability of atomic particles to behave like wave particles, thus resulting in information or the collapse of qubit state thus leading to quantum coherence or dechorence. |
It measures the ability of quantum computers to accurately compute and carry the information stored in them. |
What is quantum mechanics? Institute for Quantum Computing (uwaterloo.ca) |
No Cloning Theorem |
The “no cloning theorem” is a result of quantum mechanics that forbids the creation of identical copies of an arbitrary unknown quantum state. |
The no cloning theorem is a vital ingredient in quantum cryptography, as it forbids eavesdroppers fom creating copies of a transmitted quantum cryptographic key. |
The no cloning theorem – Quantiki |
Figure 1.5 – Quantum computing glossary
The postulates mentioned in Figure 1.5 have enabled computer scientists to migrate from classical to quantum computers. As we will see in subsequent sections, postulates such as quantum interference and the no-cloning theorem have enabled quantum technologies to come to the fore, and laid the basis for achieving faster, more efficient, and more accurate computational power. The following section will look at technologies fueling innovations in quantum computing paradigms.
In its current form, quantum computing relies on a plethora of technologies to expand its footprint. It will take years for quantum computers to fully reach their commercial potential. However, when they work in hybrid mode (in tandem with classical computers), they are expected to produce much better results than in standalone mode. Let’s have a look at the technologies that make them tick:
To understand the milestones achieved by each technology, we will take the help of DiVincenzo’s criteria. In the year 2000, David DiVincenzo proposed a wish list of the experimental characteristics of a quantum computer. DiVincenzo’s criteria have since become the main guidelines for physicists and engineers building quantum computers (Alvaro Ballon, Quantum computing with superconducting qubits, PennyLane, https://tinyurl.com/4pvpzj6a). These criteria are as follows:
Figure 1.6 helps evaluate the promises and drawbacks of each kind of quantum technology based on DiVincenzo’s criteria:
Superconducting |
Trapped Ions |
Photonics |
Quantum Dots |
Cold atoms |
|
Well-characterized and scalable qubit |
Achieved |
Achieved |
Achieved |
Achieved |
Achieved |
Qubit initialization |
Achieved |
Achieved |
Achieved |
Achieved |
Achieved |
Extended coherence durations |
99.6% |
99.9% |
99.9% |
99% |
99% |
Universal set of gates |
10-50 ns |
1-50 us |
1 ns |
1-10 ns |
100 ns |
Quantification of individual qubits |
Achieved |
Achieved |
Achieved |
Achieved |
Achieved |
Figure 1.6 – DiVincenzo’s criteria
On various parameters, technologies such as superconducting and trapped ions are showing the most promise in overcoming the challenges of quantum technology. While supergiants such as IBM and Google are betting on such technology to develop their quantum computers, new-age start-up technologies, including IQM and Rigetti, are exploring others that are more compatible with the current infrastructure.
In the next section, we will detail the applications and technologies associated with the quantum computing ecosystem.
Quantum computing technology is still in its infancy. If we have to draw parallels from a technology point of view, in 1975, most of the investors were investing in hardware firms such as IBM, HP, and later Apple, to make sure that people would be able to migrate from mainframe to personal computers. Once the value from hardware had been derived, they started paying attention to software, and firms such as Microsoft came into prominence. According to a report published by BCG, 80% of the funds available are flowing toward hardware companies such as IonQ, ColdQuanta, and Pascal. Key engineering challenges that need to be overcome are scalability, stability, and operations.
Several companies and start-ups are investing in quantum computing. Countries such as the USA ($2 billion), China ($1 billion), Canada ($1 billion), the UK (£1 billion), Germany (€2 billion), France (€1.8 billion), Russia ($790 million), and Japan ($270 million) have pledged huge amounts to achieve quantum supremacy. It has been speculated that quantum solutions, including quantum sensors, quantum communication, and quantum internet, need huge investments to help countries in achieving quantum supremacy. McKinsey has pegged the number of quantum computing start-ups at 200. Also, according to PitchBook (market data analyst), global investment in quantum technologies has increased from $93.5 million in 2015 to $1.02 billion in 2021. A few well-known start-ups that have attracted huge investments recently are Arqit, Quantum eMotion, Quantinuum, Rigetti, D-Wave, and IonQ.
Figure 1.7 shows the potential application of quantum technologies in different fields based on the types of problems solved by quantum computers:
Figure 1.7 – Application of quantum computing
The following technologies are helping companies to create the value chain for end users in the quantum realm:
Figure 1.8 – Quantum technology
As observed in Figure 1.8, the quantum computing ecosystem is vast. It has multiple facets such as quantum materials, memories, and sensors, empowering the user to collect and analyze data more effectively.
In the following section, we will look at the companies powering the revolution in quantum technologies.
Although it is still in its infancy and yet to achieve a commercial application, quantum technology holds much promise. In the near future, quantum computers will be able to help accelerate the pace of solving complex problems in conjunction with classical computers. In this section, you will learn about the business applications of this wonderful technology.
According to a McKinsey report (Quantum computing funding remains strong, but talent gap raises concern, https://tinyurl.com/5d826t55), quantum technology has attracted a total investment of $700 million from various governments and funding agencies. The promise shown by this technology has prompted the industry to fund ongoing research in various universities and labs. D-Wave was the first company to pioneer the quantum computing solution in 1999, through quantum annealing. Since then, other companies such as IBM have built a robust community of researchers and end users alike to propagate the use of quantum computers. The following is a brief list of the companies doing pioneering work in the field of quantum technology:
Global players across the value chain in the quantum computing domain include giants such as IBM, Microsoft, and Google, and well-funded start-ups such as Rigetti, IQM, and Quantinuum. These companies have invested in different types of technologies (hardware as well as software) to catapult the research in this technology domain.
In the subsequent segment, we will evaluate the roadmap provided by different technology giants to achieve full-scale quantum supremacy.
Building quantum advantage to solve real-time business problems is the end goal of many companies operating in the quantum realm. This technology is perceived to aid companies in solving large-scale problems. Recently, BMW has commissioned a million-dollar challenge to discover a solution to its inventory scheduling problem using the AWS Amazon Braket platform. In Figure 1.9, you can chart the route that could potentially lead to the era in which quantum supremacy can be achieved, and see how we can solve more problems using quantum computing:
Figure 1.9 – Quantum computing era
Broadly, the quantum era can be divided into three parts:
Quantum technology in the near term is available for end users in the form of hybrid computing. To harness the full potential of existing quantum computers, players such as D-Wave and Rigetti have started providing an interface between classical and quantum computing via microprocessors. While classical components take care of communication with end users, quantum microprocessors are used in solving NP-hard problems. Quantum technology, through quantum annealers and universal quantum computers, and using technologies such as superconducting and ion trap, will be able to harness its full potential in the near future.
In the next section, let’s have a look at what kind of people are needed to build quantum technology and its ecosystem.
Quantum technology needs a variety of people in the workforce to harness its true potential. The entire technology stack can be divided into hardware, software, and related technologies. Currently, the technology has called for scientific research and technology implementation experts. According to a survey report prepared by Forbes, a graduate must have a primary degree in STEM to understand the basic workings of quantum computers. A research-oriented mindset is essential to further investigate the development of quantum computers. To achieve scientific breakthroughs related to the development of computer hardware, a researcher must have a deep understanding of the underlying technologies such as annealing, superconducting, and ion trap. These technologies are at the forefront of the scientific breakthroughs that can be achieved with the help of a knowledgeable workforce.
In addition to building a quantum computer, it is also challenging to operate one. The current focus of software development is to write low-level programs that can interface with the memory core of the quantum computer. IBM and Google are among the companies that have developed Python-based software development toolkits (SDKs) such as Qiskit and Cirq. Programs such as IBM Summer School are good starting points for developers to get acquainted with the methodology of software interfacing with quantum memory processors. Due to the limitations of the current technology in the quantum field, a lot of emphasis is given to developing a hybrid computer. A software developer needs to know about cloud computing to operate a quantum computer. Most quantum computers are nested in big rooms at below-freezing temperatures, and can be accessed remotely using cloud computing. The algorithms written for quantum computers are also used to boost the performance of existing machine learning algorithms.
Quantum solutions also include aided technologies, making quantum technologies an exciting field to work in. Quantum sensors, annealers, and the internet are the potential applications of quantum mechanics. In addition, quantum algorithms have also shown promise in solving problems related to finance, supply chain, healthcare, and cryptography. Figure 1.8 summarizes the discussion related to the aptitudes and qualifications related to starting a career in the field of quantum technologies:
Research Areas |
Application |
Potential Qualification |
|
Hardware |
Quantum Mechanics, Theoretical Physics, Applied Physics |
Superconducting Ion Traps, Quantum Dot |
PhD, Master’s in Quantum Physics |
Software |
Quantum Information Science |
Quantum Algorithms, Quantum Machine Learning |
Software Development, Master’s in Computer Science |
Quantum Business Technologies |
Optimization, Simulation and Cryptography |
Finance, Supply-Chain, Healthcare |
Business Evangelist, Domain Expert |
Figure 1.10 – List of qualifications
From Figure 1.10, it can be observed that a potential candidate for quantum technologies needs some background in STEM. Research aptitude and a capacity to learn, unlearn, relearn, and apply new concepts are a must to sustain in this dynamic field. Since it’s a research-oriented field, companies’ inclination is more toward inducting doctoral candidates from relevant fields. However, there is a significant demand for software engineers who can write code for hybrid computers to solve problems in a faster and more accurate way.
Computing paradigms, such as calculators and analog and digital computers, have evolved over the years to assist humans in making rapid strides in technological developments and reaching new knowledge frontiers. The contributions of Jon von Neumann, Alan Turing, and Graham Moore have been immense in achieving superior computing power.
The current business environment has given rise to the need to make faster and more accurate decisions based on data. Hence, there is a need for faster, optimized computers to process large amounts of data.
Digital computers cannot solve NP-hard problems, including simulation, optimization, and pattern-matching problems, thus emphasizing the need for new computing technologies to do faster and more accurate calculations.
Emerging computing paradigms, such as quantum computing and molecular computing, promise to solve large-scale problems such as portfolio optimization, protein foldings, and supply chain route optimization more effectively and efficiently.
Quantum computing is based on the underlying principles of quantum mechanics such as qubits and the quantum states, superposition, interference, entanglement, and quantum measurement.
Current quantum hardware and microprocessors are based on technologies such as superconducting, trapped ions, annealing, cold atoms, and simulators.
The quantum computing value chain is based on the innovations achieved using quantum solutions and technologies such as quantum sensors, quantum communication, and the quantum internet.
Global players across the value chain in the quantum computing domain include giants such as IBM, Microsoft, and Google, and well-funded start-ups such as Rigetti, IQM, and Quantinuum.
Aligning business strategy with quantum computing involves developing the strategy roadmap for companies based on quantum computing eras such as NISQ, broad quantum advantage, and full-scale fault tolerance.
The future quantum workforce needs to work on three dimensions, concerning the development of hardware, software, and related quantum technologies.
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