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.

The evolution of computing paradigms

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:

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.

Business challenges and technology solutions

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:

• Faster task completion: In the current era, where manufacturing firms are looking to achieve super-large-scale production capacity and efficiency, there is a need to build faster and more reliable systems. For instance, according to an exciting study by Artificial Brain (How Artificial Brain is Building an Optimal Algorithm for EV Charger Placement Using Quantum Annealing and a Genetic Algorithm, Quantum Zeitgeist, https://tinyurl.com/bdep5eze) regarding setting up charging stations within a 50-mile radius in the San Francisco Bay Area, around 8,543,811,434,435,330 combinations were possible. Now, how can this distribution be optimized when such a large number of combinations is possible? A quantum computer theoretically solved this problem in less than 3 seconds.
• Content discovery: With the advent of social media websites, a plethora of content is available to analyze. This content is available in different sizes and shapes, in the form of text and images. An organization would need a computer with superior computing power to explore this content. This special computing prowess was achieved through parallel computing and local optimization of the machines. However, much needs to be achieved in this field in order to mine real-time business insights from the underlying data. Quantum natural language processing (QNLP) is a promising technique to resolve problems in real time.
• Lower administration costs: It is always a good strategy to optimize costs. Automation of mega factories has provided the owners with a solution in the right direction. Large-scale automation comes with a set of problems of its own, but precision and real-time decision-making help to make it more accurate and reliable. Recently, BMW has come up with a challenge where competitors have to focus on solving problems based on pre-production vehicle configuration, material deformation in production, vehicle sensor placement, and machine learning for automated quality assessment. Based on the results obtained, Dr. Peter Lehnert, BMW Group’s Vice President of Research and New Technologies Digital Car, commented: “We at the BMW Group are convinced that future technologies such as quantum computing have the potential to make our products more desirable and sustainable” (Winners announced in the BMW Group Quantum Computing Challenge, AWS Quantum Computing Blog, https://aws.amazon.com/blogs/quantum-computing/winners-announced-in-the-bmw-group-quantum-computing-challenge/).
• Remote working: The year 2020 played a pivotal role in the history of humankind. Due to the advent of COVID-19, humans have discovered that they can work from anywhere in the world. This has given rise to the demand for remote working from management and employees. Since there are some instances where you need higher computing power, remote working might not be feasible at all times. However, with most technologies going online and providing a real-time experience of working in the office environment through virtual and augmented reality and better connectivity, businesses can overcome this particular challenge. At the same time, it lowers the administration costs for management. It also helps in reducing storage costs further, which helps in reducing the unit cost for the company.

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.

Current business challenges and limitations of digital technology

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:

• Simulation: Computation simulation is modeling a natural world or physical system into a virtual scenario to understand its outcome and impact in advance. For instance, after the subprime crisis of 2008, financial institutions must run a stress test on their underlying assets and their crossholdings to predict the scenario in which the next financial crash could occur. According to one estimate, assessing the probability of a financial crash for a simple network of 20-30 institutions, having exposure in equity, derivatives, fixed income securities, and risk exposure to each other, would take 13.7 billion years, as calculated by a digital computer. This is the estimated time of running a simulation problem that is deterministic in nature and hints at solving a problem’s complexity in n steps in p time, which will not work using current digital technology and thus requires an advanced system to give a faster turnaround.
• Optimization: Optimization refers to improving the efficiency of an existing algorithm to reduce time complexity. Suppose you have to build a portfolio of 1,000 stocks belonging to 10 sectors. Your client, an international hedge fund, will have to generate several scenarios based on market conditions and thus look for an efficient frontier. These scenarios need to be updated in real time, adjusting themselves based on the risk tolerance limit defined for the portfolio. The classical computer may be able to solve the puzzle using parallel computing, but this might not be the most cost-effective and time-effective strategy. This problem underlies a need for an efficient computer to solve the puzzle in real time.
• Pattern recognition: The pattern recognition method uses underlying data to discover hidden patterns and trends using machine learning algorithms. However, recent advances in GPU and related technology have enabled programmers to meet with decent success in understanding and uncovering hidden patterns in the given data. In financial fraud, however, the complexity of human behavior makes it difficult for machine learning algorithms to understand the patterns. Theoretically, a computer able to comprehend data in real time can help decode the patterns of financial fraud more successfully.
• Cryptography: Providing a secure channel for customers to do transactions online in this e-connected world is a foremost priority for banks in the 21st century. All over the world, banks use Rivest, Shamir, and Adleman (RSA) technology based on linear factorization. The recent development of computing prowess hints that such encryption can be easily broken using quantum computers.

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.

The emerging role of quantum computing technology for next-generation businesses

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:

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.

From quantum mechanics to quantum computing

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.

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.

Approaches to quantum innovation

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:

• Superconducting: This technology takes advantage of the superposition property of quantum physics. Information is circulated by two charged electron currents flowing in opposite directions around the superconductor, and then exchanging the info stored in the qubit while entangling each other. This technology needs the quantum computer to be operated at extremely low temperatures.
• Trapped ions: An ion is a charged atom (Ca+ or Br+). Suppose a piece of information is coded on this charged atom. The atom is transported from state 0 to state 1 by emitting an energy pulse. This charged atom will carry the information and be decoded with the help of lasers. These ions are trapped in electric fields. Information coded is interpreted using a photonic unit and then passed on using optic fibers.
• Photonics: This technology uses photons to carry information in a quantum state. Using current silicon chips, the behavior of photons is controlled, and the information is transmitted over the circuit. Due to its compatibility with existing infrastructure and chip-making capabilities, it shows promise to achieve great success.
• Quantum dots: Quantum dots are small semiconducting nanocrystals made up of elements such as silicon and cadmium. Their size ranges from 2 to 10 nm. The physical implementation of a qubit involves exchanging information via charged qubits in capacitive states. Due to its conducive conditions, photonics is less error-prone.
• Cold atoms: Cold atoms use a ploy similar to trapped ions, where atoms are cooled below 1 mK and then used as an information highway to bounce off the information. Lasers are programmed to control the quantum behavior of cold atoms, and to then leverage them to transfer data.

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:

• Well-characterized and scalable qubits: Numerous quantum systems seen in nature are not qubits; thus, we must develop a means to make them act as such. Moreover, we must integrate several of these systems.
• Qubit initialization: We must be able to replicate the identical state within an acceptable error margin.
• Extended coherence durations: Qubits will lose their quantum characteristics after prolonged interaction with their surroundings. We would want them to be durable enough to enable quantum processes.
• Universal set of gates: Arbitrary operations must be performed on the qubits. To do this, we need both single-qubit and two-qubit gates.
• Quantification of individual qubits: To determine the outcome of a quantum computation, it is necessary to precisely measure the end state of a predetermined set of qubits.

Figure 1.6 helps evaluate the promises and drawbacks of each kind of quantum technology based on DiVincenzo’s criteria:

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 value chain

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:

• Quantum computing: Quantum computing refers to developing software and hardware technologies using quantum mechanics principles.
• Quantum key distribution (QKD): QKD, or quantum cryptography, provides a secure way for banks and other institutions to exchange encryption keys. It uses principles of quantum mechanics to secure the communication channels.
• Quantum software and quantum clouds: Quantum software, or programming languages such as Qiskit, provide a medium for end users to interface with system hardware and perform complex computing operations including simulation, optimization, and pattern recognition.

Figure 1.8 – Quantum technology

• Post-quantum encryption: One of the key research areas that have prompted countries to invest billions of dollars is the hunch that current encryption software will be susceptible to quantum algorithms. They need algorithms that can secure these channels further.
• Quantum sensors and atomic clocks: These terms refer to the development of laser and trapped-ion technologies to control the atomic behavior of molecules. This has prompted researchers to develop use cases where next-gen technologies such as quantum sensors will be useful in the early detection of natural calamities, including tsunamis and earthquakes.
• Quantum materials: Quantum materials refers to the cluster of world-class technologies that help capture and manipulate elements’ quantum properties for industrial usage.
• Quantum memories and other quantum components: These devices carry information in qubit form via photons. It is complex technology that is still under development and is expected to overcome the memory barriers defined by current limitations.

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.

Global players in the quantum computing domain across the value chain

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:

• IonQ (NASDAQ: IONQ): IonQ was founded in 2015 by Christopher Monroe and Jungsang Kim. IonQ has received total funding of $432 million. IonQ builds quantum computers based on ion trap technology. It provides quantum computers as Platform as a Service (PaaS) to service providers.
• Rigetti (NASDAQ: RGTI): Rigetti Computing was founded in 2013 by Chad Rigetti. It has currently received funding of $500 million. Rigetti has developed a quantum computer based on superconducting technology.
• Quantum Computing Inc.: Quantum Computing Inc. focuses on providing software and hardware solutions to the end user. It is also focusing on developing business use cases for companies, thus showcasing the potential of quantum computing in the near future.
• Archer (ASX: AXE): Archer is an Australian company that is conducting research on developing a quantum computer at room temperature. It was founded by Dr. Mohammad Choucair. It aims to produce a quantum computer that can have a widespread reach.
• D-Wave (coming soon via SPAC merger): D-Wave is credited with introducing the world’s first quantum computer for commercial use. It uses the quantum annealing technique to develop quantum solutions for the end user. It offers a limited but powerful piece of technology with 5,000 qubits of quantum computer at its disposal, which has a lot of potential business applications.
• Quantinuum: Quantinuum was formed as a result of a merger between Cambridge Quantum and Honeywell Quantum Solutions. While the primary focus of Cambridge Quantum was on developing the operating system and software for quantum computers, Honeywell has focused primarily on developing the quantum computer using ion trap 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 a quantum computing strategy implementation roadmap

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:

• Noisy intermediate-scale quantum (NISQ): This era is marked by the availability of a lesser number of good-quality qubits (<50 to 100) to solve real-world problems. The word noisy refers to the tendency of the qubits to lose their quantum state due to disturbances. It is expected that the current technology setup will be able to come out of the NISQ era by 2030.
• Broad quantum advantage: IonQ has defined the broad quantum advantage as the advent of the era where quantum computers are available for developers and end users to solve real-life problems. Based on the consensus developed by industry practitioners, 72-qubit systems will start aiding the industry in solving commercial-grade problems. Thus, it will be possible in the future to access the platform enabled by demonstrating high-level application programming and HMI functions.
• Full-scale fault tolerance: This era refers to large-scale quantum computers that have achieved two-qubit gate fidelity of 99.5%. By 2040, it is expected that the existing efforts will help in solving the problem of decoherence (leakage of information due to large numbers of qubits), and will enable organizations to take full advantage of this amazing technology.

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.

Building a workforce for a quantum leap

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:

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.