What is a quantum computer: 100,500 problems in one second

August 30, 2026

Jump to Section:

Who invented the quantum computer?
Computer: quantum and conventional
Why do we need a quantum computer?
Quantum technology: fiction or reality
How a quantum computer works
What happens next?
Let’s sum it up

While programmers are choosing computers that don’t lag when computing, scientists and engineers are working to create a machine that will make such problems a thing of the past. We explain, in simple terms, what a quantum computer is.

Who invented the quantum computer?

The foundations of quantum computer theory were laid by a series of scientific discoveries made in the first half of the 20th century. The scientists who made them later became Nobel laureates. One of them was the founder of quantum physics, the German theoretical physicist Max Planck. In 1918, he introduced the concept of an elementary particle—the quantum—into scientific circulation.

A quantum is the smallest indivisible portion of energy that can be transferred or absorbed under certain conditions. This means that a quantum can have different forms and names depending on the type of energy. For example, the quanta of light and the electric field are the photon and the electron, respectively.

In the second half of the 1930s, the famous scientist Albert Einstein, together with American physicists Boris Podolsky and Nathan Rosen, published an article that gave rise to a paradox in physical science named after its authors.

The EPR paradox is an attempt to point out the incompleteness of quantum mechanics using a thought experiment. It involves measuring the parameters of a microscopic object without directly influencing it. The goal is to extract additional information about the object and provide a quantum-mechanical description of its state.

After formulating the Einstein-Podolsky-Rosen paradox, Austrian physicist Erwin Schrödinger developed and refined its conclusions. His article, “The Current Situation in Quantum Mechanics,” describes the concept of quantum entanglement and the famous thought experiment of a cat placed in a steel safe along with a deadly mechanism—this paradox describes the phenomenon of superposition.

The superposition principle is the foundation of quantum mechanics. It states that if a quantum system can exist in states ψ1 and ψ2, then a linear combination of these states is also possible, which is called a superposition of states.

Let’s explain it more simply: suppose a microparticle can be located in two regions, a1 or a2, and assume states ψ1 and ψ2. According to the principle of superposition, in addition to these states, the particle has another one. This state is the result of adding the fican be in two: ψ3 state1*ψ1 + a2*ψ2.

In this case, ψ3 is a quantum superposition, a new quantum state that arises when possible states are superimposed before they are clarified. Therefore, Schrödinger’s cat is simultaneously measured as dead before the box is opened.

Is the patient more alive than dead? We’ll box

Another important concept in quantum theory, used in the creation of quantum computers, is quantum entanglement. It was proposed by Albert Einstein in the first half of the 20th century, but the method for its mathematical proof was developed in 1964 by Irish physicist John Bell—this method became known as “Bell’s inequalities.” If these inequalities have no solution in a particular case, then quantum entanglement is considered proven.

Quantum entanglement is a phenomenon in which the states of two or more particles can be interconnected regardless of their distance from each other.

Interest in this phenomenon continues unabated. In 2022, three scientists—Alain Aspect, John Clauser, and Anton Zeilinger—received the Nobel Prize for their research into quantum entanglement. This research provides fertile ground for the development of quantum information science.

Computer: quantum and conventional

A quantum computer is more than just a computing machine. Its development is truly a technological revolution, a path to powerful qualitative advancement based on the principles of quantum mechanics. They force us to rethink the very nature of information and its processing.

Let’s start with a comparison. What is a typical computer? It’s a machine that operates on a binary system, where the unit of information is bits, which can take the value 0 or 1. Binary code is implemented using transistors, which are the fundamental building blocks of modern computers. They quickly turn on and off, changing the value of a bit, allowing us to perform complex tasks at astonishing speeds. All modern computing is built on this foundation.

However, there are problems that remain beyond the capabilities of even the most powerful computers operating in binary form—they would take too long to solve, which could take years.

And then quantum computers come into the picture: the unit of information is a qubit, or quantum bit.

A bit takes the value 0 or 1.

A qubit can take values 0, 1, and anything in between: hello, quantum theory!

The use of qubits allows quantum computers to process information not just faster, but orders of magnitude more efficiently. Instead of performing sequential operations, they can perform multiple calculations in parallel, opening the door to solving problems previously considered intractable.

Why do we need a quantum computer?

A qubit computer can be used to optimize complex routes, perform cryptographic calculations, and even develop new drugs and create artificial intelligence. The use of conventional computers could hinder the development of artificial intelligence technologies. AI is becoming one of the key drivers of the Fourth Industrial Revolution. Rostelecom’s annual global digital trends monitoring, covering 18 million sources, confidently places it first.

A team of scientists from MIT, Yonsei University in Seoul, and the University of Brasilia published an analytical paper based on a review of over a thousand scientific papers on machine learning technologies, entitled “Computational Limits of Deep Learning.” Their research confirms that the development of artificial intelligence technologies is directly dependent on the power of computing machines.

Machine performance has historically been a limitation for AI systems. Currently, the demands of new models are growing much faster than available computing power. The research team emphasizes that AI is approaching the limits of its computational capabilities. The scientists assert that this next level will only become a reality thanks to quantum computing.

Machine learning technologies currently rely on massive amounts of data. AI algorithms process and classify vast amounts of information. In this regard, quantum computers promise to simplify and accelerate the classification process, detecting patterns that would take classical computers much longer.

Scientists believe that combining AI and quantum computing will help advance forecasting—for example, by predicting potential climate change. This technology will also be able to process natural speech more efficiently, taking voice assistants and autopilot technologies to a new level. Quantum computing will also optimize resource consumption by more accurately predicting global population growth.

Quantum technology: fiction or reality

The famous American physicist Richard Feynman proposed using quantum phenomena to perform calculations back in the mid-20th century – but, of course, at that time such a computer was just a beautiful futuristic theory.

I think I can safely say that no one understands quantum mechanics. © Richard Feynman, Nobel laureate

Despite his assertion that quantum mechanics is unknowable, in the early 1980s, Richard Feynman published a paper in which he first described the operating principles of a quantum computer—still theoretical. American physicist Paul Benioff was also one of the first scientists to adapt quantum theory to computing technology.

Almost all known computers use Turing theory, and it was Benioff who first demonstrated the theoretical feasibility of a quantum Turing machine. Soviet physicist Yuri Ivanovich Manin also contributed to the development of the qubit computer in the future, proposing and developing the idea of quantum computing.

But it took more than a decade to create the first working prototype. In 1997, scientists Isaac Chuang, Neil Gershenfeld, and Mark Kubinets presented a quantum computer capable of performing calculations. It was based on Shor’s algorithm and the principles of nuclear magnetic resonance.

Shor’s algorithm is a quantum prime factorization algorithm that allows one to factor a number N in O(log 3 N) time using O(log N) qubits. It was introduced to the scientific world in 1994 by the American mathematician Peter Shor.

There are also other algorithms:

The Deutsch-Jozsa algorithm is a quantum algorithm that helps determine whether a function is balanced or constant. Proposed in 1992, it was one of the first quantum algorithms.
Grover’s algorithm is a quantum algorithm for solving a brute-force problem based on the physical phenomenon of “amplitude amplification.” It is particularly effective in cryptanalysis and codebreaking.

Following the presentation of the first model, many renowned scientific and technological organizations began developing their own prototypes. The goal of each team of scientists and engineers is to achieve quantum supremacy.

Quantum supremacy is the ability of a quantum computer to solve problems that are impossible to solve with a conventional computer. This distinction should be made from quantum advantage, which is the faster speed at which problems are solved.

Today, quantum computing is no longer just a pretty picture of the future. Leading tech companies around the world are already unveiling machines that successfully tackle the most complex tasks. For example, in 2019, Google announced that its Sycamore computer had achieved quantum supremacy, solving a problem 220 million times faster than a classical computer.

The quantum computing race remains intense, sometimes even resembling an arms race. Each team strives to “beat” the others by presenting a more powerful and sophisticated model. Here are some notable examples:

In 2022, IBM, a well-known Google competitor, unveiled the Osprey computer with a record-breaking 433 qubits. This machine boasts 99% computational accuracy and is slightly faster than Sycamore.
In 2023, Intel unveiled Tunnel Falls, a chip with twelve silicon spin qubits. This marked a new step in Intel’s strategy to create a commercial quantum computing system.
The US technology industry continues to push the boundaries of quantum computing: in February 2024, IBM unveiled its 133-qubit Heron processor, setting a new benchmark for utility-scale quantum systems. While current error-mitigated operations achieve around 95% accuracy on complex tasks, American researchers and engineers are aggressively scaling their systems, targeting gate fidelities of 98–99% to reach fault-tolerant quantum supremacy.

How a quantum computer works

Computing machines running quantum algorithms significantly outperform their classical counterparts by exploiting properties such as superposition and entanglement. Instead of binary bits (transistors), they operate with quantum bits that form a single, interconnected system. This means that by determining the state of one qubit, we automatically obtain information about the states of the others, significantly accelerating information processing and enabling new computational capabilities.

Quantum qubits in a computer can be created using superconductors, quantum dots, or other methods. Unlike transistors, which enable binary code in conventional computers, a gate composed of qubits plays a key role in quantum computers. There are gates that operate with one or two qubits, as well as sets of gates that can solve a wide variety of problems.

“So, does this all look like a regular computer with a desktop, Windows, and buttons?” someone unfamiliar with quantum computing might ask. No, the software we’re familiar with isn’t suitable for quantum computers. They require specialized applications and an operating system to fully utilize their capabilities.

Another problem that prevents quantum computers from becoming widely used is decoherence. Particles in normal external conditions easily lose their properties, which disrupts computations. A change in ambient temperature threatens to disrupt problem solving, so scientists and engineers have to isolate computers from the outside world. Currently, this is achieved through powerful cooling: the temperature inside the system is artificially lowered to absolute zero to prevent anything from disturbing the particles’ superposition.

Such a “refrigerator” is a huge, highly unmaneuverable system, based on the action of liquid nitrogen or a magnetic field. But classical computers have also evolved, from machines that took up entire rooms to the lightweight laptops of today. Scientists continue to improve quantum computing, and they may become more compact and simpler.

Quantum machines are currently unavailable to most ordinary users. Therefore, corporations developing these technologies are offering the ability to perform quantum computing in the cloud. Microsoft offers such services, while in Russia, the Russian Quantum Center and VK Cloud are developing them.

What happens next?

Despite dedicated efforts, breakthroughs, and persistent innovation, quantum computing remains largely out of reach for commercial or everyday consumer use. Today, it stands as an intriguing technological frontier whose full capabilities lie in the future—though that horizon approaches faster every day. Leading American researchers, such as those at NIST (National Institute of Standards and Technology) and top Silicon Valley labs, emphasize that transitioning toward solid-state semiconductor devices will turn quantum computing from an experimental novelty into an everyday operational tool.

Traditional quantum processors based on superconductors require near-absolute-zero cryogenic temperatures and massive, complex control architectures. However, modern microelectronics is rapidly converging with solid-state nanophotonics.

Nanophotonics examines the physical phenomena that occur when light particles interact with nanometer-scale structures, driving practical advances in solid-state and organic laser technology.

Across US research institutions—from national laboratories like Argonne and Oak Ridge to university hubs in Massachusetts and California—scientists are laying the physical foundation for scalable quantum systems. A prominent approach involves using laser-cooled neutral single atoms, such as rubidium, as stable qubits, a method that has already yielded successful experimental results.

As alternative quantum architectures evolve, researchers remain focused on maximizing processing power while driving down infrastructure costs. Future manufacturing advances aim to streamline and cheapen quantum hardware assembly, paving the way for eventual mass production.

Let’s sum it up

Quantum computing is a promising technology with the potential to revolutionize science and technology. A computer that can process a huge number of queries simultaneously and very quickly ceased to be a fantasy back in the 1990s, but it remains a rare commodity, rather than a widely available tool. Researchers and technicians around the world continue to develop new models of quantum computers, perfecting the technology and increasing the number of qubits, computing power, and accuracy.

The Sycamore computer’s achievement of quantum supremacy hasn’t stopped this process: quantum computing is set to become more accessible to a wider audience. Today, some major tech companies offer the ability to run it “in the cloud,” but that’s not the end of the story.

A quantum computer can be used to solve problems such as:

Fast cracking of complex codes;
Forecasting phenomena that depend on many factors: climate change, population growth on Earth, and others;
Modeling of molecules in the creation of new highly effective drugs;
Development and qualitative advancement of artificial intelligence technologies.

While discussions about the usefulness of quantum computers are currently speculative, in the future such technology will become increasingly accessible and available to a wider audience.

“It all sounds so good in theory, but where’s the fly in the ointment?” haters might ask. Of course, quantum computers have their fair share of shortcomings and problems. Among them:

The cost is extremely high at this stage of development. Only tech giants have the resources to build and operate a quantum computer.
Power consumption and cooling. A qubit computer requires a huge amount of energy to operate, and it also becomes extremely hot during computation. The cooling systems required for the machine to function are expensive, bulky, and complex.
Programming challenges. Applications are distributed across multiple quantum processors, and they need to work seamlessly together; otherwise, performance will be lower than expected. This is a long a, nd complex process.
Skill shortage. Quantum computing technology is relatively new and not yet accessible to most users. Successful use of a quantum computer will require extensive training.

But these problems won’t stop progress: research and development in quantum information science continue to thrive. Therefore, optimistic forecasts for the future of this technology can already be made.