Quantum Annealing in 2026: Practical Quantum Computing
Quantum annealing is a promising quantum technology for companies with urgent optimization problems that traditional computers cannot solve quickly. It can be used to solve optimization problems more effectively than traditional computers. However, it is still mostly used in academia, and more R&D is required to build commercial quantum annealers.
There are different approaches to building quantum computing hardware, such as universal gate-model quantum computers and quantum annealers. Universal gate model quantum computing, also called general-purpose quantum computing, is the most powerful and flexible type of quantum computer, but it is hard to build and maintain qubit stability.
Quantum annealers are the least flexible type of quantum computer in terms of application, but it is easier to build a stable quantum annealing processor and qubits.
What is quantum annealing?
Quantum annealing (which also includes adiabatic quantum computation) is a quantum computing method used to find optimal solutions to problems with a large number of solutions, by leveraging properties specific to quantum physics, such as quantum tunneling, entanglement, and superposition.
An adiabatic process is a term commonly used in thermodynamics. For example, to harden iron, the temperature can be raised, increasing molecular speed and forming stronger bonds. The process of stabilizing these bonds by slowly cooling them is called “annealing” in metallurgy. Quantum annealing works in a similar way, where the temperature is replaced by energy, and the lowest energy state, the global minimum, is found via annealing.
How does it compare with other QC approaches?
There are three basic quantum computing methods: Analog Quantum Model, Universal Quantum Gate Model and Quantum Annealing. Although these approaches seem to be completely separate, the intersection sets are not empty. These three models offer different perspectives on the practical applications of quantum computing.
The universal quantum gate model is based on creating quantum structures using stable qubits and solving today’s problems with quantum circuits. However, qubits are difficult to stabilize. This problem grows as the number of qubits increases. As a result, universal quantum gate model computers are confined to labs and do not yet have practical applications.
On the other hand, quantum annealing provides an approach that focuses on solving NP-hard problems and is less affected by noise than gate-model quantum computing. This feature allows more qubit usage and, therefore, more parameters for specific problems.
How does quantum annealing work?
In quantum annealers, each state corresponds to an energy level. These states are simulated in a short time by taking advantage of the superposition and entanglement properties of qubits, and the lowest energy result is obtained. The lowest-energy state gives the optimal or most likely solution.
For example, let’s consider a travelling salesman problem. Imagine a salesman who needs to stop by 50 different cities and come back to the starting point with the minimum distance. Mathematically, there are 50 factorial different solutions to this problem. Since we are looking for the shortest distance, we need to find the least energy situation, in other words, the global minimum.
Finding the shortest path by calculating all the possibilities is a costly method in terms of time and energy; for many complex problems, it is almost impossible.
Using quantum annealing, this problem is formulated using the coupling-qubit method. Thanks to the different magnetic fields applied to the qubits, the distance between each city is added to the design as an energy parameter.
With the entanglement property, each qubit can affect the other’s state, and each solution creates a new state. A quantum annealer calculates the lowest-energy state among these states, which equals the optimal solution.
Another important point here is the quantum tunneling feature. With this feature, the transition between states is instantaneous. This means that the transition between the energy levels does not require electrons to climb the barrier; they just pass it.
Why does it matter now?
Quantum annealing outperforms classical computational methods for solving some optimization problems, which are important across numerous industries such as healthcare and finance. Additionally, quantum annealing is likely to become commercially available before other quantum technologies.
The volume of data is increasing, and quantum annealing offers exceptional methods for solving optimization problems that grow in complexity with greater data availability. It promises to be the right tool for rapid development in science, mathematics, and engineering.
Although quantum annealing is not as widely applicable as quantum computers, it is promising in the short term. This is because it is inherently more resilient against noise than the quantum gate model and analog quantum approaches. Currently, vendors offer commercial quantum annealing solutions, while a universal quantum computing solution is still in early stages of research.
What are its potential applications/use cases?
Quantum annealing is generally used to solve combinatorial optimization problems, such as machine learning, portfolio optimization, and route optimization. This is because optimization problems aim to find the minimum of a function, and quantum annealing can be used to compute the minimum of a function with many variables.
Considering the potential uses, we can list the main topics as follows.
- Machine Learning
- Optimization
- Financial modelling
- Security
- Healthcare
- Material Science & Chemistry
What are its alternatives?
Quantum annealing can be compared with a few related methods, such as digital annealing and gate model quantum computing.
Classical computing
For problems with limited complexity, classical computing can be used to find optimal solutions or near-optimal solutions using heuristics.
Digital annealing
Fujitsu’s digital annealing method emulates quantum annealing using a digital computer architecture. This method, which is not exactly quantum annealing, is a quantum annealing simulation using C-mos circuits.
Digital Annealer is a specialized chip designed to calculate possible states in parallel using weight matrices and bias vectors. In order to solve a combinatorial optimization problem, “Each bit block uses one-to-one connections over 1,023 weights stored in memory,” says Fujitsu fellow, Tamura.
This method does not promise the speed that quantum annealers offer, but it provides stability that qubits cannot provide today.
Which are the companies that are closest to bringing this to market?
In March 2025, D-Wave published a paper in Science titled “Beyond-Classical Computation in Quantum Simulation,” demonstrating the world’s first quantum computational supremacy on a useful, real-world problem.
- D-Wave’s Advantage2 prototype performed a magnetic materials simulation in minutes that would take the Frontier supercomputer (Oak Ridge National Lab) nearly one million years.
- The classical approach would require more than the world’s annual electricity consumption.
- This validated quantum annealing as offering fundamental advantages over classical methods.1
The other player for quantum annealing is a Japanese company NEC Corporation. In December 12, 2018 the company introduced a research program for the development of a quantum annealing machine. However, there is no additional information about any product or research especially in quantum annealing.
In 2018, the New Energy and Industrial Technology Development Organization began funding projects on quantum annealing technologies with superconducting devices.
For more on quantum computing
To learn more about quantum computing, read:
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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