The year 2025 has been designated the International Year of Quantum Science and Technology by the United Nations, marking a century since the birth of quantum mechanics. It is a fitting tribute, as this year has witnessed the quantum computing industry pivot from theoretical promise to tangible, real-world impact. At the forefront of this transformation is quantum annealing, a specialized form of quantum computing designed to solve some of the most complex optimization problems facing industries today. Once a niche concept, quantum annealing has now demonstrated a clear advantage over even the most powerful classical supercomputers for specific, practical applications, heralding a new era of problem-solving (1)
Optimization problems are ubiquitous in modern civilization, from optimizing financial portfolios and streamlining global supply chains to discovering new drugs and designing advanced materials. As the scale and complexity of these problems grow, classical computers are increasingly hitting a wall, unable to find optimal solutions within a reasonable timeframe. Quantum annealers, by harnessing the principles of quantum mechanics, offer a fundamentally new approach.
This blog post explores the state of quantum annealing in 2025, detailing its underlying principles, recent breakthroughs, and the blooming landscape of practical use cases that are reshaping industries.
What is Quantum Annealing?
How Quantum Annealing Works: A Journey to the Global Minimum
Unlike the more general-purpose gate-based quantum computers, quantum annealers are specialized machines built for a single purpose: finding the lowest energy state, or “global minimum“, of a complex system. This process is analogous to the physical process of annealing in metallurgy, where a material is heated and then slowly cooled to remove defects and reach a strong, stable state.
In quantum annealing, a problem is first translated into a mathematical representation called a Quadratic Unconstrained Binary Optimization (QUBO) model. This model defines an “energy landscape” where the lowest point corresponds to the optimal solution of the original problem. (5)
What is QUBO?
Imagine you have a bunch of light switches, and each switch can be either ON or OFF. The goal is to turn some switches ON or OFF to make a special picture look the best or to get the most candies. Quadratic Unconstrained Binary Optimization is just a fancy way of figuring out the best way to turn those switches ON or OFF to get the best result, without any rules stopping you.
The quantum annealer then begins its process:
- Initialization: The system’s qubits (quantum bits) are prepared in a state of quantum superposition, where they represent all possible solutions simultaneously (5)
- Annealing: The system is slowly evolved according to the principles of the adiabatic theorem. During this evolution, a quantum phenomenon known as quantum tunnelling allows the qubits to pass through energy barriers in the landscape, escaping “local minima” (good, but not optimal, solutions) that would trap a classical algorithm (5)
- Final State: As the process concludes, the system naturally settles into the lowest energy state of the landscape – the global minimum – which represents the optimal solution to the problem (5).
Video: How Quantum Annealing Works?
What is Adiabatic Theorem?
The adiabatic theorem is an idea in physics that says if you change something very, very slowly, a system (like a ball in a valley) will stay in its smoothest, most stable state. Imagine you have a toy car on a track. If you move the track very slowly, the car will smoothly follow the track without jumping or falling off. But if you move the track too fast, the car might jump out of the track or wobble. So, the adiabatic theorem tells us that if we change things slowly enough, the system will stay in its simplest, most stable state the whole time.
This process, particularly the ability to tunnel through obstacles, gives quantum annealers a powerful advantage for navigating the incredibly complex and rugged landscapes of today’s optimization challenges.
The 2025 Breakthrough: Demonstrating Quantum Supremacy
A landmark achievement in 2025 solidified quantum annealing’s place in the computational landscape. A study led by researchers at D-Wave, a pioneer in commercial quantum annealing, demonstrated the world’s first instance of quantum computational supremacy on a useful, real-world problem (4).
In a paper published in Science, researchers detailed a simulation of a complex magnetic material. The simulation, performed on D-Wave’s sixth-generation Advantage2 quantum annealer, was completed in a matter of minutes. According to reports, classical supercomputers attempting the same simulation with equivalent accuracy would have required nearly one million years and consumed more electricity than the entire world uses in a year (4).
This was not merely a speed-up; it was a demonstration that for certain practical problems, quantum annealers can provide answers that are simply unattainable through classical means.
Further benchmarking studies in 2025 have reinforced this advantage. A study published in npj Quantum Information compared a state-of-the-art quantum solver against leading classical algorithms on over 50 large, dense optimization problems representative of real-world tasks.
The quantum solver was not only more accurate but also ~6561 times faster than the best classical alternative (6). These results underscore a critical turning point: the conversation has shifted from whether quantum advantage is possible to where it can be most effectively applied.
Video: Introducing the D-Wave Advantage2™ System:
Practical Use Cases: Quantum Annealing at Work
The specialization of quantum annealers in optimization has led to a rapidly expanding portfolio of practical applications across numerous sectors. The ability to solve complex QUBO problems translates directly into tangible business value.
Practical Applications of Quantum Annealing
- Finance: Portfolio Optimization. Constructing investment portfolios that maximize returns for a given level of risk, a task that becomes exponentially harder with more assets (2) (5). Fraud Detection: Identifying complex patterns in financial transactions that may indicate fraudulent activity. Optimal Trading: Determining the best execution strategies for large trades to minimize market impact.
- Logistics & Supply Chain: Vehicle Routing. Solving the “traveling salesman problem” on a massive scale for delivery fleets, reducing fuel costs and delivery times (1) (5). Production Scheduling. Optimizing manufacturing schedules to maximize throughput and minimize downtime (1) (5). Cargo Loading: Efficiently packing containers and vehicles to maximize space utilization.
- Life Sciences & Healthcare: Drug Discovery. Identifying promising drug candidates by simulating molecular interactions and protein folding, a notoriously difficult optimization problem (1) (5). Genome Assembly: Piecing together fragmented DNA sequences to reconstruct a complete genome. Radiation Therapy: Optimizing the angles and intensities of radiation beams to target tumors while sparing healthy tissue.
- Energy & Utilities: Electrical Grid Optimization. Balancing power generation and distribution in real-time to prevent outages and improve efficiency (1) (5). Refinery Scheduling: Optimizing the complex processes within an oil refinery to maximize the output of valuable products.
Video: D-Wave Webinar: Quantum Meets Logistics: A Real-World Routing Case Study
The Hybrid Approach and Future Outlook
While quantum annealers have proven their worth, the most powerful solutions emerging in 2025 often involve a hybrid approach. This model combines the strengths of both classical and quantum processors. Large, complex problems are broken down, with the most computationally intensive optimization portions being sent to the quantum annealer, while the rest of the workflow is handled by classical machines (1). This synergy allows organizations to tackle problems of unprecedented scale and complexity.
The future of quantum annealing is bright. Hardware continues to improve, with processors featuring more than 5,000 qubits and increasingly sophisticated connectivity, such as D-Wave’s Pegasus topology (6).
This allows for the mapping of larger and more complex problems onto the quantum hardware. The market reflects this optimism, with projections showing the quantum computing market growing from $4 billion in 2024 to as much as $72 billion by 2035 (3).
However, challenges remain. Quantum annealers are not universal quantum computers and cannot solve every type of problem. They are sensitive to environmental “noise” and building more robust, error-corrected systems is a key area of ongoing research (2).
Despite these hurdles, the trajectory is clear. We are likely heading towards a future where specialized quantum annealers work alongside gate-based quantum computers and classical supercomputers, each tackling the problems for which they are best suited (5).
Quantum annealing has transitioned from a fascinating scientific curiosity to a powerful, commercially viable tool that is actively solving real-world problems in 2025. The demonstration of quantum supremacy on a practical application has silenced many sceptics and opened the floodgates for industrial adoption. By providing a new and powerful way to solve optimization problems, quantum annealing is not just accelerating computation; it is enabling us to find better solutions to some of the most important challenges facing business and society today. The quantum revolution has begun, and it is being annealed.
Alright, at the end of this article, let’s explore something more fun and exciting: here is an entertaining prediction of what quantum computing might be like over the next hundred years.
Video: QUANTUM COMPUTERS: The Next 100 Years (Quantum A.I.)
References
[1] Quinton, F. A., et al. (2025). Quantum annealing applications, challenges and limitations for optimisation problems compared to classical solvers. Scientific Reports, 15(12733).
https://www.nature.com/articles/s41598-025-96220-2
https://arxiv.org/abs/2409.05542
[2] Swayne, M. (2025, May 1). Quantum Computer Outperforms Supercomputers in Approximate Optimization Tasks. The Quantum Insider.
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.160601
[3] Soller, H., et al. (2025, June 23). The Year of Quantum: From concept to reality in 2025. McKinsey & Company.
[4] Ladizinsky, E. (2025). Annealing quantum computing’s long-term future. Nature Portfolio.
https://www.nature.com/articles/d42473-025-00236-1
[5] Colwell, B. D. (2025, October 1). Quantum Annealing In 2025: Achieving Quantum Supremacy, Practical Applications And Industrial Adoption. Brian D. Colwell.
[6] Kim, S., et al. (2025). Quantum annealing for combinatorial optimization: a benchmarking study. npj Quantum Information, 11(77).
https://www.nature.com/articles/s41534-025-01020-1
https://arxiv.org/abs/2504.06201