Optimization, the Quantum Approach

If you think you understand quantum mechanics, you don’t understand quantum mechanics. 
– Richard Feynman, Nobel Prize-winning physicist

As confusing as this sounds, the strange rules of quantum physics are exactly what make quantum computing, and quantum optimization, so powerful. They turn the weirdness of nature into a new way to solve problems faster than ever before. Let’s break it down from the beginning.

Defining Quantum (Science and Computing)

The word quantum literally means “how much.” Quantum physics studies how energy and matter behave at the smallest possible scale, where the normal rules of nature no longer apply. At that level, everything is made up of incredibly small particles, photons (light), electrons and quarks that behave in ways that seem random and unpredictable to us. These particles can act like both waves and solid objects, exist in several states at once and even instantly affect one another from a distance. These strange principles – superposition, entanglement and uncertainty – have opened the door to new technologies, including quantum computing, which uses these same natural behaviors to process information in ways classical computers never could [1].

Quantum computing takes the principles of quantum physics and turns them into a new way of processing information. A classical computer uses bits (0s and 1s) to represent data. Every operation, whether adding numbers, comparing routes or finding the best option, happens one step at a time, following a clear path through all possible answers. A quantum computer, on the other hand, doesn’t follow a single line of reasoning. It uses qubits, which can exist in a superposition of 0 and 1, meaning each qubit can represent multiple possibilities at once. When many qubits are connected through entanglement, they start to influence each other, creating a web of possibilities that the system can explore simultaneously. 

Imagine you’re at Disneyland, trying to find the fastest route through all the rides. A classical computer would test one path, time it, then try the next and so on, checking every possible route until it finds the best one. A quantum computer, by contrast, could consider all possible routes at the same time, using superposition and entanglement to compare outcomes in parallel. That said, quantum computing isn’t about magically finding all the answers at once; it’s about guiding probabilities toward the most likely solution. While the qubits are in their superposed, in-between states, they represent many potential answers, each with a probability of being correct. Quantum algorithms are designed to nudge those probabilities so that when the system finally “collapses” into a single answer (when we measure it), the most probable outcome is also the best, most optimal one [2].

Quantum in Everyday Lives

The idea of quantum physics has been around for decades. What began as abstract equations is now beginning to shape the world around us. For example, GPS technology allows us to coordinate navigation, logistics and delivery routes across the globe down to the second.

Across industries, researchers are already testing what happens when quantum thinking meets real-world challenges. For example, in finance, quantum computing is used to analyze risk or optimize large portfolios; in machine learning, it’s used to help algorithms uncover patterns faster in massive datasets. But one of the most promising, and relatable, areas of all is optimization. Optimization problems are everywhere: finding the best route for a delivery truck, scheduling flights, balancing inventory or designing energy-efficient supply chains. Meeting the kinds of challenges that grow exponentially more complex as the number of variables increases is where quantum computing shows its greatest promise: not by replacing classical systems but by working alongside them to explore the vast numbers of possibilities in parallel and guide us more quickly toward the best answers [3, 4].

Defining Optimization

Optimization is about finding the best option among many, while balancing constraints such as time, cost or resources. It’s what we do every day without realizing it, from planning the fastest route to work to deciding the most efficient way to run errands on a busy afternoon. At its heart, quantum optimization doesn’t just compute faster – it thinks differently. Rather than processing one option after another, it reframes the entire challenge as a physical system that naturally seeks balance.

Optimization

Using Quantum Computers  In quantum optimization, the aim is to find the best solution from among many possible options. This could be the most efficient delivery routes, the optimal production schedule or the most balanced resource allocation. To do this on a quantum computer, the problem is translated into a form the hardware can use: Each possible configuration of variables becomes a quantum state, and each state is assigned an energy value that corresponds to how good (or bad) that configuration is.

The goal is then to find the configuration with the lowest energy – that is, the best solution under the constraints of cost, time, resources, etc. The quantum system evolves so that lower-energy states become more probable. When we measure the system, we get a candidate solution. Because quantum results are inherently probabilistic, we repeat this process, and frequently, a classical computer then refines the most promising results. This hybrid quantum-classical loop is what allows quantum optimization to be practical right now even though the hardware is still maturing.

The Algorithms Driving Quantum Optimization

Now that we understand quantum optimization as a way of reframing problems as energy landscapes, the next step is to look at how those landscapes are actually explored. Quantum annealing is a method designed to drive a system toward its lowest-energy state by mapping the problem onto a physical system that naturally seeks low energy. According to the vendor D-Wave Systems, quantum annealing “uses quantum physics to find low‐energy states of a problem and therefore the optimal or near-optimal combination of elements” [5]. In practice, this method is especially suited for combinatorial optimization, characterized by problems where many discrete decisions interact (e.g., route choices, scheduling orders or binary allocations). The quantum annealer makes use of quantum fluctuations or tunneling to escape suboptimal local minima and aim for the true global minimum.

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm that approaches optimization by alternating between quantum operations and classical parameter tuning. The quantum computer encodes the cost function (the measure of how good each solution is) into a Hamiltonian and then applies quantum operations; next, a classical optimizer reviews the result and adjusts parameters for the next cycle. This iterative loop continues until a desirable solution is found.

Because of this structure, QAOA is well suited to near-term quantum hardware (often called noisy intermediate-scale quantum devices) and is applied for combinatorial optimization problems in which an approximate but high-quality solution is acceptable.

Although both methods aim to find low-energy, high-quality solutions, they work very differently in practice. For example, if you were optimizing delivery routes for a fleet of trucks, a quantum annealer would encode the entire routing problem into a physical energy landscape and let the system relax into a good solution QAOA would approach that same problem iteratively, running a quantum circuit, checking the result, adjusting parameters and repeating until the solution improves. This difference makes annealing better suited for large, highly structured combinatorial problems, whereas QAOA offers more control and flexibility on today’s gate-based quantum devices [5-7].

What Does the Industry Think? 

Quantum computing is emerging as the next frontier in operational efficiency, promising speed and scale far beyond even today’s most powerful supercomputers. According to a 2025 survey conducted by Wakefield Research on behalf of D-Wave [8], the vast majority of business leaders recognize both the urgency and opportunity in optimization: 81% believe they’ve already reached the limits of what classical optimization can deliver, and 88% say their companies would go above and beyond for even a 5% improvement in efficiency. Yet many remain held back by outdated technology (39%), budget constraints (38%) and reliance on classical methods (36%).

Despite these hurdles, momentum is building. Nearly three in four organizations (72%) that have invested in or plan to adopt quantum optimization within the next two years anticipate returns of at least $1 million, and 85% believe they risk falling behind if they fail to act soon. Already, 22% of executives surveyed report quantum optimization making a huge impact for early adopters, whereas half (50%) expect it to be disruptive within their industries. These figures underscore that the value of quantum optimization is no longer theoretical; it’s being recognized as a differentiator. That shift from potential to practice is becoming visible across industries today. Quantum optimization is one of the fastest-growing domains of quantum computation, with both academia and business investing heavily in early solutions. 

The clearest evidence of this shift comes from the companies already experimenting with quantum optimization in their day-to-day operations:

• BMW Group used recursive QAOA to tackle complex supply chain “partition problems,” achieving results comparable to leading heuristic algorithms.
• Volkswagen applied quantum-based routing in Lisbon to dynamically adjust public transport routes based on real-time traffic data.
• Toyota Central R&D Labs developed quantum-inspired traffic control methods to ease congestion by optimizing signal systems.
• Coca-Cola Bottlers Japan Inc. leveraged quantum optimization to streamline deliveries to more than 700,000 vending machines, improving efficiency and reducing fuel use.
• ExxonMobil explored quantum algorithms for maritime routing, reducing travel time and energy consumption.

Together, these insights and examples illustrate a clear reality: Although large-scale quantum advantage is still developing, quantum optimization is already producing tangible returns and performance gains for early adopters willing to experiment [8, 9].

But Is It Perfect?

Quantum optimization holds incredible promise, from solving complex logistics to reshaping manufacturing and design, but the road to practical, scalable use is still full of hurdles. Even as early pilots show encouraging results, several challenges continue to slow real-world adoption.

Current quantum hardware is still constrained by noise, limited qubit counts and instability, making it difficult to run large-scale commercial workloads. Competing approaches, from annealing to gate-based to hybrid architectures, also create uncertainty, with no clear winner yet. As Gurobi notes, quantum optimization still lacks a definitive, exact algorithm, and reproducible results remain difficult because of the technology’s probabilistic nature. In practice, this means that although quantum optimization is beginning to deliver value in controlled pilots, most applications today remain hybrid, experimental and domain specific. As McKinsey (via Gurobi) observes, quantum computing is advancing quickly but remains “largely experimental and hypothetical at this early stage” [10].

Quantum optimization is still in its early stages, but its direction is unmistakable. As classical systems reach their computational limits, quantum approaches are emerging as the next step forward – not to replace traditional computing but to extend it. Over the next few years, most real progress will come from hybrid systems, where quantum processors explore vast spaces of possibilities and classical computers refine the results into precise, actionable solutions. The evolution will be gradual. Algorithms will improve, hardware will stabilize and industries will learn how to express their challenges in quantum terms. Companies that start building familiarity, experimenting with pilot projects or developing internal expertise now will be best positioned when quantum capability scales. At its heart, optimization is about making better decisions, balancing what’s possible with what’s practical. Quantum optimization simply expands that horizon. It allows us to look beyond the limits of sequential thinking and glimpse the full landscape of potential outcomes. In the end, quantum optimization isn’t just a technological shift: It’s a 
new way of thinking about problem-solving itself. Step by step, it’s redefining how we understand efficiency, possibility and the very nature of choice.

References

1. https://scienceexchange.caltech.edu/topics/quantum-science-explained/quantum-physics
2. https://www.ibm.com/think/topics/quantum-computing
3. https://www.geeksforgeeks.org/physics/real-life-applications-of-quantum-mechanics/
4. https://pubsonline.informs.org/do/10.1287/orms.2025.02.16/full/
5. https://docs.dwavequantum.com/en/latest/quantum_research/quantum_annealing_intro.html
6. https://milvus.io/ai-quick-reference/how-does-quantum-annealing-work-in-solving-optimization-problems
7. https://quantum.cloud.ibm.com/docs/tutorials/quantum-approximate-optimization-algorithm
8. https://www.dwavequantum.com/media/sdylxhd0/quantum-computing-the-key-to-addressing-today-s-complex-business-problems.pdf
9. https://lingarogroup.com/blog/when-quantum-computing-meets-supply-chain-management
10. https://www.gurobi.com/faqs/quantum-optimization/