A quantum computer operates based on the principles of quantum mechanics, using qubits that can simultaneously exist in multiple states thanks to superposition and entanglement. This enables it to solve problems inaccessible to classical computing machines, especially those involving factoring large numbers, optimization, and quantum system modeling.
The difference between a quantum computer and traditional computers lies in the fact that it operates not just with zeros and ones, but with quantum states, which greatly expands computational capabilities and speeds up solving complex problems. In this article, we will explore exactly how a quantum computer is built, the key principles underlying its operation, and the real-world problems it can solve in 2026, opening new horizons in science and technology.
| Technology | Number of Qubits | Operating Temperature | Example Device |
|---|---|---|---|
| Superconducting Qubits | up to 27 | around 15 mK | IBM Quantum System One |
| Ion Traps | up to 32 | room temperature | IonQ |
| Quantum Annealing | 5000+ | around 15 mK | D-Wave Advantage |
- 27 qubits number of qubits in IBM Quantum System One
- 15 million dollars approximate cost of IBM Quantum System One
- 5000+ qubits number of qubits in the D-Wave Advantage quantum annealer
- 100 microseconds approximate qubit coherence time
What Is a Quantum Computer and How Does It Differ from a Classical One?
A quantum computer is a device that uses qubits, which can exist in a superposition of states 0 and 1 simultaneously, unlike a classical computer where a bit holds only one of two values. This feature allows quantum machines to perform parallel computations and solve certain problems significantly faster.
Main Differences
- Classical bit: takes the value 0 or 1 and serves as the basic unit of information in traditional computers.
- Qubit: a quantum unit of information capable of existing in superposition, meaning simultaneously in states 0 and 1.
- Model example: IBM Quantum System One, introduced in 2019, equipped with 27 qubits and costing about 15 million dollars.
- Quantum effects: superposition and entanglement enable computational speed-ups compared to classical methods.
How Qubits Work
Qubits embody quantum states that allow a single qubit to represent many possibilities at once, rather than a single fixed value like a bit. Upon measurement, a qubit «collapses» into either 0 or 1, but until then it holds information as a superposition. Another important feature is quantum entanglement, where the state of one qubit is directly linked to the state of another regardless of the distance between them, expanding the possibilities for parallel information processing.
Which Tasks Can Quantum Computers Solve More Efficiently Than Classical Ones?
Quantum computers outperform classical ones in tasks like factoring large numbers, simulating molecular systems, and solving complex optimization problems thanks to quantum algorithms such as Shor’s algorithm and Grover’s algorithm, as well as quantum annealing for finding global minima.
Cryptography
Shor’s algorithm, implemented on quantum processors, can factor numbers on the order of 2048 bits in theoretically feasible times, threatening the security of RSA encryption widely used for data protection. In 2020, Google conducted an experiment with its 53-qubit Sycamore quantum processor, demonstrating quantum supremacy — an important milestone toward practical use of Shor’s algorithm. This means quantum computing can accelerate cracking cryptographic systems that would take classical computers millions of years.
Chemistry and Materials
Quantum computing is far more effective at modeling molecular interactions and chemical reactions, which are challenging for classical systems due to exponential complexity growth. For example, in 2020 Google simulated a hydrogen molecule (H₂) on a quantum processor, paving the way to calculating properties of complex materials and drug compounds. Additionally, quantum optimization algorithms implemented in D-Wave’s quantum annealers are used for routing and logistics problems, reducing computation time from days to just hours.
- Shor’s algorithm: factoring numbers of about 2048 bits
- Google Sycamore: 53 qubits, demonstration of quantum supremacy in 2020
- H₂ molecule modeling: Google experiment in 2020
- D-Wave Systems: quantum annealing for logistics and routing optimization
How Is a Quantum Computer Built? Examples of Popular Models
Quantum computers are constructed on different physical principles of qubit implementation, requiring ultra-low temperatures and precise control to work with quantum information. Models like IBM Quantum System One, D-Wave Advantage, and IonQ illustrate the main approaches: superconducting qubits, quantum annealing, and ion traps, each with its own technical specifics and application areas.
Superconducting Qubits
IBM Quantum System One uses superconducting qubits made from aluminum, cooled down to about 15 millikelvin to achieve superconductivity and minimize thermal noise. These qubits are controlled by microwave pulses, and their stability depends on the cryogenic environment and complex electronics. Meanwhile, D-Wave Advantage offers over 5000 qubits implementing quantum annealing — a method for solving optimization problems by evolving the system toward minimal energy.
Ion Traps
IonQ employs a different principle: individual ions are held in electromagnetic traps and controlled by laser pulses with microsecond precision. This approach provides high-quality qubits and precise control, which is important for quantum data processing algorithms. However, ion traps require complex laser stabilization equipment and vacuum conditions.
- IBM Quantum System One: temperature around 15 millikelvin, superconducting aluminum qubits
- D-Wave Advantage: 5000+ qubits, focused on quantum annealing optimization tasks
- IonQ: ion traps with laser control accurate to microseconds
- All models require cryogenic environments and advanced electronics for qubit readout and control
What Limitations and Challenges Do Quantum Computers Face?
Quantum computers face a number of serious limitations, including high error rates, short qubit coherence times, and significant costs and technical difficulties in scaling. These factors greatly restrict the practical use of quantum machines in 2026.
Errors and Correction
Error rates during quantum operations can reach several percent, necessitating complex quantum error correction methods for reliable computations. For example, IBM Quantum System One, costing about 15 million dollars, is equipped with specialized error correction systems, but fully eliminating errors remains impossible. Scaling quantum systems to thousands of qubits is challenging due to exponential error growth and instability, requiring new technical solutions and correction algorithms.
Coherence
Qubits maintain coherence for only a very short time — about 100 microseconds — limiting the duration of quantum calculations. This timescale forces engineers to optimize algorithms and hardware for the fastest task execution possible. Additionally, the high cost of quantum hardware and the complexity of scaling add further barriers to widespread adoption.
- Operation error rate — up to several percent
- Qubit coherence time — around 100 microseconds
- IBM Quantum System One cost — approximately 15 million dollars
- Technical scaling challenges — instability when increasing qubit numbers to thousands
Where Are Quantum Computations Already Used in Science and Industry?
Pharmaceuticals
In the pharmaceutical industry, quantum computing is already applied to simulate complex molecular interactions, reducing new drug development times to months instead of years with traditional methods. For example, Google Quantum AI works on projects simulating molecules to find effective compounds for treating diseases, which helps optimize drug synthesis and reduce testing costs.
Quantum simulations in pharmaceuticals help accurately calculate molecular energy levels and reactivity, crucial for developing drugs against complex diseases. This reduces the risk of costly errors during preclinical research, cutting expenses by millions of dollars per project.
Finance
In finance, quantum algorithms are used to optimize investment portfolios and manage risks, increasing returns and lowering investment volatility. Companies like Volkswagen and Daimler utilize quantum methods for route planning and traffic management, saving up to 15% on fuel and reducing travel times by 10–20%.
- Portfolio optimization: quantum algorithms reduce loss risk and improve investment returns.
- Route planning: quantum computing cuts travel time by 10–20%.
- Cost reduction: fuel savings up to 15% through more efficient traffic management.
Frequently Asked Questions
How many qubits are needed for practical tasks?
Why are quantum computers so expensive?
When will quantum computers surpass classical ones in speed?
Which programming languages are used for quantum computers?
Key Takeaways
- Qubit is the basic unit of quantum information, enabling superposition
- Shor’s algorithm potentially breaks classical cryptography
- Superconducting and ion qubits are key quantum processor technologies
- Quantum error correction is essential due to high operation error rates
- Quantum computing is already applied in pharmaceuticals and finance