Quantum computing is a multidisciplinary area that combines computer science, physics, and mathematics to tackle complicated problems more quickly than traditional computers. Quantum computing involves both hardware research and application development. Quantum computers can handle some sorts of problems quicker than classical computers by utilizing quantum mechanical effects like superposition and quantum interference.

Quantum computers can deliver such a speed improvement in applications such as machine learning (ML), optimization, and physical system simulation. Future use cases could include portfolio optimization in finance or chemical system simulation, which solve issues that are currently intractable for even the most powerful supercomputers on the market.

The quantum computing business is expected to develop at a 33% CAGR between 2023 and 2035, reaching $50 billion from $1.61 billion in 2022. The North American industry share is expected to be over 38% by 2035, owing to the introduction of sophisticated computers by major quantum computing businesses such as IBM and Microsoft, as well as government initiatives.

To capture a piece of that value, quantum technology startups saw investments totaling $2.35 billion for quantum computing applications, including companies in quantum computing, communications, and sensing.

Thus, market growth may be linked to rising demand for sophisticated computing capabilities as well as the potential economic impact across a wide range of businesses.

Let’s take a closer look at this technology and discover more about its uses in many industries.

What is Quantum Computing?

Quantum computing is a rapidly evolving technology that opens up entirely new areas of computing by addressing specific problems much faster than traditional computers. But let’s break this down a little to get a better picture.

Quantum computers use qubits, which can exist in several states of 1 and 0, a phenomena known as “superposition,” as opposed to classical computers, which store information in bits that can only be 1 or 0. Qubits can also affect one another through a process known as “entanglement,” even when they are not physically coupled.

Quantum computing and its applications are based on quantum mechanics principles. In classical physics, an item is in a well-defined state. However, in quantum mechanics, objects only exist in a well-defined state after being observed. Prior to our observation, the states of things and their connections were probabilistic.

From a computational standpoint, this means that data is recorded and stored in diverse ways using non-binary qubits of information, reflecting the quantum world’s multistate nature. This multiplicity allows for faster and less expensive calculations in arithmetic combinatorics.

As the name implies, combinatorics problems inquire, “How many ways can this set of objects be combined?” Whether if a specific combination is conceivable, or which combinations of things are “best” according to some criteria.

In other words, quantum algorithms provide a unique approach to these complicated issues, with the possibility of significantly reducing the time required to solve them. As quantum hardware scales and quantum algorithms progress, many critical issues may be solved.

What are the Principles of Quantum Computing?

A quantum computer operates on quantum principles. To properly understand quantum principles, a new glossary of terminology is required, including superposition, entanglement, and decoherence. Let’s look at these principles below.

Superposition

Superposition states that, much like waves in classical physics, you can add two or more quantum states and the result will be another valid quantum state. Conversely, you can also represent every quantum state as a sum of two or more other distinct states. This superposition of qubits gives quantum computers their inherent parallelism, allowing them to process millions of operations simultaneously.

Entanglement

Quantum entanglement occurs when two systems link so closely that knowledge about one gives you immediate knowledge about the other, no matter how far apart they are. Quantum processors can draw conclusions about one particle by measuring another one. For example, they can determine that if one qubit spins upward, the other will always spin downward, and vice versa. Quantum entanglement allows quantum computers to solve complex problems faster.

When a quantum state is measured, the wavefunction collapses and you measure the state as either a zero or a one. In this known or deterministic state, the qubit acts as a classical bit. Entanglement is the ability of qubits to correlate their state with other qubits.

Decoherence

Decoherence is the loss of the quantum state in a qubit. Environmental factors, like radiation, can cause the quantum state of the qubits to collapse. A large engineering challenge in constructing a quantum computer is designing the various features that attempt to delay the decoherence of the state, such as building specialty structures that shield the qubits from external fields.

What are the Types of Quantum Technology?

No one has demonstrated the optimal technique to develop a fault-tolerant quantum computer, and numerous firms and academic groups are looking into various sorts of qubits. We provide a quick overview of several of these qubit technologies below.

Gate-based ion trap processors

A gate-based quantum computer is a device that takes input data and transforms it according to a predefined unitary operation. The operation is typically represented by a quantum circuit and is analogous to gate operations in traditional electronics. However, quantum gates are totally different from electronic gates.

Trapped ion quantum computers implement qubits using electronic states of charged atoms called ions. The ions are confined and suspended above the microfabricated trap using electromagnetic fields. Trapped-ion based systems apply quantum gates using lasers to manipulate the electronic state of the ion.  Trapped ion qubits use atoms that come from nature, rather than manufacturing the qubits synthetically.

Gate-based superconducting processors

Superconductivity is a set of physical properties that you can observe in certain materials like mercury and helium at very low temperatures. In these materials, you can observe a characteristic critical temperature below which electrical resistance is zero and magnetic flux fields are expelled. An electric current through a loop of superconducting wire can persist indefinitely with no power source.

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Superconducting quantum computing is an implementation of a quantum computer in superconducting electronic circuits. Superconducting qubits are built with superconducting electric circuits that operate at cryogenic temperatures.

Photonic processors

A quantum photonic processor is a device that manipulates light for computations. Photonic quantum computers use quantum light sources that emit squeezed-light pulses, with qubit equivalents that correspond to modes of a continuous operator, such as position or momentum.

Neutral atom processors

Neutral atom qubit technology is similar to trapped ion technology. However, it uses light instead of electromagnetic forces to trap the qubit and hold it in position. The atoms are not charged and the circuits can operate at room temperatures

Rydberg atom processors

A Rydberg atom is an excited atom with one or more electrons that are further away from the nucleus, on average. Rydberg atoms have a number of peculiar properties including an exaggerated response to electric and magnetic fields, and long life. When used as qubits, they offer strong and controllable atomic interactions that you can tune by selecting different states.

Quantum annealers

Quantum annealing uses a physical process to place a quantum system’s qubits in an absolute energy minimum. From there, the hardware gently alters the system’s configuration so that its energy landscape reflects the problem that needs to be solved. The advantage of quantum annealers is that the number of qubits can be much larger than those available in a gate-based system. However, their use is limited to specific cases only.

What is an Example of Quantum Computing in Real Life?

While only modest quantum processors are currently accessible, and large-scale quantum computers are still in their early phases of development, researchers and businesses are looking into a variety of intriguing quantum computing applications. Some of these applications are:

1. Drug discovery

New medications cost an R&D department an average of $2 billion and take more than ten years to reach the market after being discovered. Quantum computing has the potential to significantly accelerate the development of novel pharmaceuticals by enhancing target discovery, drug design, and toxicity testing. These techniques would rely less on trial and error, potentially resulting in a speedier time to market.

The advantages of this technique are already evident at Polaris Quantum Biotech, where quantum computing is being used to expedite drug discovery. Even though they had to figure out how to value seemingly conflicting goals, such as solubility and protein interactions, the team began with modest chemical libraries to demonstrate that the code worked before scaling up to a multi-billion search library area.

2. Cybersecurity

Today’s digital economy operates on principles of trust and safety; however, the most widely used cybersecurity tools and techniques, particularly RSA cryptography, will not be secure against mature quantum technology. Assuming quantum computers can overcome some inherent limitations to their performance (namely superposition and entanglement), they could make it considerably easier for hackers to bypass algorithmic trapdoors.

Financial institutions must shift their data-security strategies and consider adopting RSA alternatives, such as Quantum Key Distribution (QKD). QKD can be used today to distribute ultra-secure encryption keys, creating networks that respond to the quantum threat digital businesses face.

In 2022, Ernst & Young (EY) became the launch customer for the Quantum-Secured Metro Network (QSMN), the trial of a commercial QKD network built using Toshiba QKD hardware. The QSMN offers a range of quantum-secured services, such as dedicated high bandwidth end-to-end encrypted links spanning a large metropolitan area.

Trial networks such as the QSMN offer a vital platform for real-world learning and a hub for financial organizations to build quantum-aware teams and test their market fit.

3. Cryptography

Few of us would make the association between quantum computing applications and the tiny padlock icon next to the URL in our web browser every time we send and receive emails, use an eCommerce site, or check our bank and credit card accounts.

This icon indicates that the online services use HTTPS, a web protocol that encrypts the information we send and receive over the internet. This and other types of encryption protect passwords, digital signatures, health information, and other electronic communications.

There are two basic ways that quantum computing could alter our understanding of encryption:

• Post-quantum cryptography — also known as quantum-proof cryptography, aims to develop encryption methods that algorithms or calculations cannot break and are carried out by future quantum computers.
• Quantum key distribution (QKD) — uses a series of photons to transmit a secret, random sequence known as the key. Users can determine whether the key has been compromised by comparing measurements at both transmission ends.

Due to significant technological limitations, scientists have demonstrated that QKD works but cannot be widely used. For example, researchers in China have demonstrated QKD over a distance of 1 km long, and even though such efforts open up new possibilities for long-distance QKD, more research is needed to create systems that transmit keys reliably and efficiently.

4. Financial modeling and calculations

In financial markets, computing speed has long been a competitive advantage. Solutions for finance are one of the earliest domains to embrace quantum computing applications due to the expected improvement of financial modeling and its speed, thus improving market predictions and risk management.

In a recent study conducted by quantum computing companies Multiverse Computing, Pasqal, and one of France’s largest banks Crédit Agricole, it was concluded that “quantum computing techniques demonstrated a significant improvement in computing time while requiring a smaller memory footprint, opening the door for their use in practical applications in derivatives valuation.”

For the quantum computer, the chosen problem was tackled in real-world settings. With a quantum processor of only 50 qubits, the results were as accurate as those in production. The projections also indicated that this performance could be bettered at 300 qubits, a power that should be available industrially in 2024.

5. Material science

Quantum computing applications in material science revolve around discovering and manipulating molecules and material behavior. This involves the motion and interaction of subatomic particles and, therefore, requires quantum mechanics.

While Airbus, Volkswagen, and JP Morgan Chase have dedicated research areas to quantum computing, companies like Google, IBM, Microsoft, and Intel actively seek solutions to some of these emerging issues. Meanwhile, smaller businesses and even startups are also entering the race.

As reported by IBM’s Institute for Business Value, exploring quantum use cases in the chemicals and petroleum sectors illustrates how quantum computing can accelerate the development of new methods and materials in these industries.

6. Artificial intelligence and machine learning

Quantum linear algebra is a broad field with diverse quantum techniques and approaches, primarily applied in AI and machine learning. In some applications, critical steps in conventional machine learning pipelines are replaced by quantum algorithms with a proven quantum speedup to shorten training time.

The current limitations of the technology require stringent requirements on hardware (quantum memory, fault tolerance) and mathematically well-defined problems.

Nevertheless, the potential of quantum AI and machine learning apps across various industries, including pharmaceuticals, automotive, and finance — for tasks such as autonomous driving, automated trading, and predictive maintenance — has captured the attention of one of Germany’s leading research centers, the Fraunhofer Cluster of Excellence Cognitive Internet Technologies.

The center is working on a QML project to develop quantum algorithms for combinatorial optimization problems fundamental to AI and machine learning.

Finally

If you want to experiment with quantum computing, you can start by running a quantum hardware emulator on your local system. Emulators are ordinary programs that simulate quantum phenomena on a classical computer. They are predictable and allow you to see quantum states. They’re useful for testing your algorithms before investing on quantum hardware time. However, they are unable to reproduce true quantum behavior.

You can also use a cloud quantum computing service to code on a genuine quantum computer without having to purchase pricey hardware.