What Is Quantum Computing? Working, Importance, and Uses | (2023)

Quantum computing is defined as a computational technology that uses the principles of quantum mechanics such as entanglement, superposition, and interference to process, store, and manipulate large amounts of data and perform complex calculations for conventional computing systems and supercomputers to fathom. This article explains quantum computing, its working, importance, and applications.

Table of Contents

  • What Is Quantum Computing?
  • How Does Quantum Computing Work?
  • Importance of Quantum Computing
  • Top 8 Applications of Quantum Computing

What Is Quantum Computing?

Quantum computing refers to a computational technology that uses the principles of quantum mechanics such as entanglement, superposition, and interference to process, store and manipulate large amounts of data and perform complex calculations for conventional computing systems and supercomputers to fathom.

Today’s ordinary computers run on chips that use bits for computations. These bits take either of the two values–zero or one–where zero represents the ‘off’ position, and one represents the ‘on’ position. Several such bits that reveal a combination of ones and zeroes are the fundamental units of every website, app, or photograph we use or access.

While bits are convenient to use, they do not essentially disclose that our universe’s nature beyond just the on and off conditions. Uncertainty is intrinsic to our world. However, even the most powerful supercomputers today cannot process this uncertainty, thereby giving rise to a computing void.

The uncertainty factor came to light over the last century when scientists discovered that physical laws do not apply at the sub-atomic level and are distinctly different from the ones we observe daily. This led to the development of ‘quantum mechanics’, which decoded the science of sub-atomic elements. It laid the foundation for physics, chemistry, and biology.

Now that the uncertainty phenomenon was visible, technologists needed a tool to undertake calculations while managing the uncertainty. Thus, ‘quantum computing’ was born. It is based on physical laws that govern the subatomic world, where elementary particles can simultaneously be in several states and places. The technology observes the behavioral pattern of matter and energy at the quantum level and exploits it in a quantum computing model.

Thus, quantum computing technology uses, manipulates, and controls these quantum theory laws to carry out complex tasks and computations with some uncertainty. Although quantum computing is a relatively new technology, companies such as IBM, Google, D-Wave, Microsoft, and others are making significant progress in this domain.

In Jan 2019, IBM took a giant leap in quantum computing when it announced the launch of its first commercial quantum computer. On the other hand, in Oct 2019, Google announced that it had developed a quantum machine that could solve a typically complex problem in 200 seconds, which would otherwise take around 10k years for the world’s fastest supercomputer to compute.

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How Does Quantum Computing Work?

Today’s computers use binary systems to encode data. Such a binary framework runs on processors that use transistors for computations. The transistors act as switches in the computer’s circuitry and create 0s and 1s to process computing logic. However, when it comes to quantum computers, these 0s and 1s are replaced by quantum bits, also called qubits, which encode quantum information and process different quantum states.

Key components that drive the quantum computing model are:

1. Qubits

Quantum computers work on qubits. These represent quantum-mechanical systems that can take up different quantum values and scale exponentially beyond the conventional ones and zeros. For example, a two-qubit system can perform four concurrent computations, while a three-qubit does eight, and a four-qubit system does 16.

Let’s look at the qubit representation:

Consider the above figure where a bit takes up values of 0s or 1s and is represented by A-B. On the contrary, the sphere representation shows that the qubit can take up multiple values identified on the sphere’s surface. Each point has a subsequent latitude-longitude pair associated with it, representing 0 or 1 and phase values, respectively.

2. Superposition

Superposition implies that the quantum system is capable of being in several different states at the same time. For example, consider a coin toss scenario. When you flip the coin, it ends up as heads or tails. However, if we consider the state of the coin when it is suspended in the air, it holds both heads and tails simultaneously. Similarly, quantum particles such as electrons are in a state of quantum superposition until they are measured. As a result, the ‘uncertainty’ factor is taken care of in quantum computers.

3. Entanglement

Entanglement refers to the entangling two or more qubits by establishing a correlation between them. When qubits are entangled, any change to one of the qubits invariably impacts the others without exception. For example, let’s say you introduce an additional qubit to a 60-qubit computer. In such a case, the quantum computer can evaluate 260 states concurrently. Adding a qubit along with the entanglement property allows the computer to perform computations faster than usual. Hence, quantum computing algorithms use quantum entanglement for faster data processing.

4. Interference

Interference is a method of controlling the quantum states in a quantum machine by reinforcing or diminishing the wave functions of quantum particles. As a result, quantum states leading to a correct output can be amplified, while one can subsequently cancel the states yielding a wrong output.

5. Coherence

Quantum machines do not perform well in noisy environments. They are affected by external noise as the ‘superposition’ state maintained by qubits is disrupted, thereby leading to errors in computation. Moreover, quantum states retain information only for a short duration. Hence, knowing that the data can go out of coherence in no time, it is vital to perform the computational tasks while the information is still alive.

To overcome such problems, quantum computers are maintained at low temperatures (close to absolute zero). As heat is susceptible to noise and errors, colder temperatures are preferred to allow qubits’ longer quantum state retention, including superposition and entanglement.

Let’s understand the working of the quantum computer through an example:

Let’s say you invite five colleagues to your wedding, and you need to plan their seating arrangements. The total number of ways to do so is 5! = 120. Now, a conventional computing system tends to evaluate each of the 120 possibilities, compare them, and then decide on the final optimization.

However, a quantum computer undertakes the following steps for optimizing seat allocation:

  1. Considers qubits and creates quantum superposition for all possible quantum states.
  2. The encoder applies phases to each quantum state and configures the qubits. For the possible sitting ways that fall in phase, the amplitudes add up, while for the out-of-phase ways, the amplitudes cancel out. The process is similar to the noise cancellation technology used in speakers and headphones, wherein noise is created or applied to phase out external noise.
  3. The quantum computer then uses interference to reinforce or amplify some answers and cancel or diminish the others. As a result, a single solution for optimized seat allocation is finally reached.

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Importance of Quantum Computing

Quantum computing promises to revolutionize several industry verticals, including medicine, machine learning, artificial intelligence, cryptography, finance, etc., over the coming decade. Advancements in quantum computing are fueled mainly by the money pumped in by investors, governments, and companies that are aiming to gain ultimate quantum supremacy.

In 2019, the U.S. government launched the ‘National Quantum Initiative’ that focuses on broadening the quantum computing field. Moreover, the government also allocated $1.2 billion that would be used for the promotion of the quantum realm. Similarly, China is boosting its quantum dreams by investing $10 billion in building the ‘National Laboratory for Quantum Information Sciences.’

As quantum computing inches closer to reality, it is crucial to understand its importance.

Let’s look at the reasons why quantum computing is preferred today:

1. The quest to solve rising complex problems

The problems faced by earthlings today are far more complicated than what advanced tech can address. Such concerns have high complexity, which means it would take centuries for today’s supercomputers to solve these problems.

Some examples are modern cybersecurity problems, optimization problems, stock profile management, problems related to aerospace, molecular study, and others. Another example is associated with protein modeling. During the COVID-19 pandemic, the scientific community struggled to identify a computing tool that could model and deactivate a single protein in lesser time. If such a tool was available, it could have saved the world from this global health crisis.

Today, energy usage is another critical area. With the exponential growth of the global population, energy consumption has increased significantly. This has created a problem of ‘energy source optimization’, which is difficult for current computers to tackle. With quantum computing coming to the fore, there is a silver lining in the computing world that one can finally solve such complex problems.

2. The commercial potential of quantum computing

Let’s understand an example of an application that benefits farmers, agricultural companies, and allied industries.

It is a well-known fact that around 50% of global food production depends on ammonia fertilizers. This ammonia is produced through a chemical process called the ‘Haber-Bosch process’, which requires high temperature and pressure. The physical constraints of the process are a bit difficult to tackle as they cause considerable energy usage, which is one of the big problems.

This is where quantum computers come into play. It has already been identified that our planet can produce ammonia fertilizer at standard temperature and pressure with the help of an enzyme called ‘nitrogenase’. However, this enzyme is developed through a complicated catalytic procedure that today’s computers cannot handle. The process involves molecular modeling where nitrogenase is mapped by traversing the path through nearly 1,000 atoms of carbon. Thus, it limits the industrial production of nitrogenase, which affects the overall industrial output of ammonia-based fertilizers.

Here, quantum computers can come to the rescue by generating molecular models of nitrogenase. The computing can further design molecules similar to the enzyme and help produce low-cost and low-energy ammonia.

With quantum computing, ammonia-based fertilizers would be readily available and at affordable pricing. The technology would also reduce the burden on energy usage generally observed in the nitrogenase development process.

3. Supports non-linear problems

Classical computing best suits linear problems, where sequential operations are the prime focus. Such computing systems are built on the foundations of linear mathematics, which studies linear equations and transformation properties.

However, nature is fundamentally non-linear with a pinch of uncertainty. Such nonlinear problems are not addressed well by classical systems. However, quantum computers can process non-linear data. Examples of such non-linear problems include traffic equilibrium optimization, probability of moon landing, etc.

4. Handling huge rise in the amount of data

We live in a digital age and a big data world where a phenomenal amount of data is generated daily. With the internet of everything coming to light, every IoT device, wearable, gadget, and sensor is interconnected to a computing network, thereby contributing to the generated data. According to Domo, computing devices generate around 2.5 quintillion bytes of data daily.

Modern computers and supercomputers are prone to errors when handling such a massive quantity of data, affecting performance. Moreover, computational tasks such as testing the effects of drugs at the molecular scale are complex for classical computers to manage. Instead, quantum computers are better suited for such tasks as they can process significant volumes of data faster.

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Top 8 Applications of Quantum Computing

Quantum computing has opened up opportunities across several industries and disciplines, from pharmaceuticals, chemical engineering, and information and communications technology to finance, automotive, and aerospace.

Let’s look at the top eight applications that are a consequence of quantum computing:

1. Machine learning

Quantum computing can fine-tune algorithms such as neural networks that are extensively used in machine learning. Moreover, popular deep learning and machine learning algorithms, broadly used for optimization problems, can be further optimized using quantum computers as they combine classical and quantum simulations to solve problems.

As quantum computing can process large quantities of data, it can aid in making better decisions and predictions, such as in applications such as facial recognition, object recognition, and fraud detection. Also, research in quantum-enabled machine learning is leading to the development of quantum algorithms that can speed up artificial intelligence (AI) processes.

Quantum computing-inspired machine learning can also aid in designing methods to neutralize cybersecurity threats. It can also help develop encryption techniques, thereby driving the quantum cryptography field.

2. Drug development

Quantum computing can play a crucial role in drug development, wherein drugs can be tested for stable molecular configuration using molecular modeling processes. Also, quantum computers can run advanced simulations on various participating organic molecules that help decide the suitability of the organic molecules for the drug.

Quantum computing is typically suitable for addressing combinatorial optimization problems when specific molecules necessary for drug development aren’t known or available. Further, simulation-based trials can help deliver drugs to the market in time and save a lot on their research & development.

3. Model chemical processes

Quantum computing plays a pivotal role in designing molecular structures, such as the nitrogenase enzyme observed in ammonia-based fertilizers. Quantum-based simulations can be used to model chemical processes and complex atomic interactions, which are processed faster than traditional methods of lab experimentation that apply trial and error tactics. Thus, quantum computing can revolutionize the field of chemical engineering.

4. Finance

Quantum computing can overcome the drawbacks of traditional algorithms that perform poorly in time-sensitive financial transactions. It can help in stock-portfolio management, investments, and financial trading.

Let’s consider an example of a standard ‘high-frequency trading model’. A significant time lag exists between two consecutive financial transactions in such models. Investors may incur losses due to this time lag as they cannot re-balance often. One can address this problem with quantum computing as it speeds up the financial calculations involved in trading.

Quantum computing can be used for portfolio optimization by banks for processing, scheduling, and prioritizing several financial transactions. One can harness quantum computing through quantum processors, a combination of multiple qubits that use quantum properties to determine the best possible answer.

Quantum computing is also valuable for determining financial risks as it speeds up the Monte Carlo simulation by developing models that yield potential results through probability distribution for uncertain factors. It calculates results multiple times using different values and probability functions.

5. Aircraft development

Companies can use quantum computing in the process of aircraft design. The entire aircraft can be modeled and digitized, which facilitates faster simulation. Today, aerospace engineers take ample time- generally months or years- to model the flow of air currents over the aircraft wing. However, quantum computing can perform mathematical calculations faster, thereby boosting aircraft design efficiency. They can also use quantum principles for other purposes, such as optimizing fuel consumption and managing the aircraft’s speed, which will fulfill the sustainability targets of the industry.

6. Automotive industry

The automotive industry is already benefiting from the quantum computing paradigm. For example, Volkswagen, a leader in this industry, has partnered with quantum computing provider ‘D-Wave’ to leap into the quantum-enabled world. In 2019, a few Volkswagen buses were fitted with a navigation app designed by D-wave that offers real-time quantum services, such as information on congestion data or the best possible routes to a destination scaled down to the millisecond time frame.

Similarly, Mercedes-Benz Group (Daimler AG) partnered with IBM in Jan 2020 to develop next-generation lithium batteries with the help of a quantum computer.

7. Development of climate models

Today’s climate models used to make weather forecasts aren’t as accurate as they need to be. This is primarily because several inputs representing real-world conditions are required to be simulated. Classical computing systems cannot handle large quantities of data inputs. Here, quantum computers that can accommodate as many inputs can help develop accurate climate models.

This implies that quantum computers can simulate the minutest of environmental variables such as wind, temperature, humidity, pressure, etc., and understand how they respond to different weather conditions in the atmosphere. Such granular details can allow researchers to calculate and track several environmental parameters in real-time, which will help in accurate weather prediction.

8. Solid material development

Researchers can use quantum computing to design quantum-mechanical simulations that determine the characteristics and properties of advanced materials. The Quantum Monte Carlo model can be used where the internal structure of solid materials may be modeled, enabling the discovery of entirely new materials that are far more difficult for conventional computing models. For example, in 2020, researchers at the University of Chicago used IBM’s 53-qubit Hummingbird quantum processor to develop a new quantum material, ‘exciton condensate’, through quantum simulations.

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Quantum computing is still in its infancy stage. The quantum computers that are currently available have specific requirements regarding hardware and cooling temperature conditions. However, as several large companies, governments, and academic institutions continue to invest millions of dollars into the research and development of quantum computing, it won’t be long before quantum computing becomes a ubiquitous computational technology.

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