The core value of current quantum computing lies not in replacing traditional computers, but in solving “specialized problems” that traditional computing power cannot break through. Traditional computers process data sequentially, making them inefficient when dealing with complex problems involving multiple variables and probabilities, such as large-scale optimization, molecular simulation, and cryptography. Quantum computing, on the other hand, leverages the principles of superposition and entanglement to process multiple states simultaneously, enabling it to tackle these problems in a fraction of the time. In the financial sector, this advantage is already being put to practical use. The quantum platform jointly developed by Goldman Sachs and Rigetti can process investment portfolio optimization for more than 1,000 assets in just a few minutes, 200 times faster than traditional methods, with a 12-percentage-point reduction in risk exposure. This is not just a theoretical improvement; it directly translates to higher returns and lower risks for investors. A hedge fund that adopted a quantum optimization system saw its annualized return rate increase by 28% and its maximum drawdown rate drop from 8.5% to 5.2% in just six months of use. These results are not isolated cases—according to a 2026 report by McKinsey, 35% of large financial institutions have already piloted quantum computing projects, with 78% of them planning to scale up their investments in the next two years. In the medical and materials fields, quantum computing is accelerating “from 0 to 1” innovation breakthroughs. Traditional drug research and development is a time-consuming and costly process, often taking 10-15 years and billions of dollars to bring a new drug to market. A key bottleneck is the simulation of molecular interactions, which is computationally intensive for traditional computers. Quantum computing can accurately simulate these interactions at the atomic level, greatly shortening the new drug screening cycle. For example, a biotech startup in Boston used a quantum simulator to identify a potential treatment for Alzheimer’s disease in just 18 months, a process that would have taken 5-7 years with traditional methods. The quantum computing market in the medical field is expected to reach 4 billion US dollars in 2026, with a focus on drug discovery, genetic sequencing, and personalized medicine. In the materials science sector, quantum simulation is driving innovation in green energy and high-end manufacturing. Researchers at MIT used quantum computing to design a new type of battery material that increases energy density by 40% and reduces charging time by 50%, a breakthrough that could revolutionize the electric vehicle industry. Similarly, in the semiconductor industry, quantum computing is being used to simulate the behavior of electrons in new materials, helping to develop smaller, more efficient chips. The related market size for quantum computing in materials science is projected to reach 6 billion US dollars by the end of 2026. Despite these advancements, there are still many cognitive misunderstandings in the industry. One common misconception is that quantum computing will soon replace traditional computers. In reality, NISQ-era quantum computers are not designed to replace classical systems; instead, they work best in hybrid architectures, where quantum processors handle specialized tasks and classical computers manage the rest. For example, a quantum computer might optimize a supply chain, while a classical computer handles the day-to-day operations and data storage. This hybrid approach maximizes the strengths of both technologies. Another misconception is that quantum computing is only accessible to large tech companies and research institutions. While it is true that building a quantum computer is expensive, the rise of cloud-based quantum platforms has democratized access. Companies like IBM, Amazon, and Microsoft now offer cloud-based quantum computing services, allowing small and medium-sized enterprises (SMEs), universities, and even individual researchers to access quantum at a fraction of the cost. For example, a university research team can use IBM’s Quantum Experience to run experiments for as little as $100 per hour, making quantum research accessible to a wider audience. Looking ahead to the rest of 2026 and beyond, the commercialization of quantum computing will enter an acceleration period. We can expect to see large-scale applications in finance, medical care, materials, and cybersecurity. For enterprises, the key is to identify their specific pain points and determine whether quantum computing can provide a solution. For example, a logistics company might use quantum computing to optimize delivery routes, while a pharmaceutical company could use it to speed up drug discovery. For technology practitioners, learning the basics of quantum computing and understanding its application scenarios will become a core competency. Quantum computing is not an “all-powerful artifact,” but it is a powerful tool that can solve some of the most complex problems facing the world today. By understanding its true capabilities and limitations, we can harness its potential to drive innovation and create new value in the computer industry and beyond. As we move further into the NISQ era, the line between theory and practice will continue to blur, and quantum computing will become an integral part of the technological landscape.
