What is a Quantum Computer Used For? 13 Real-World Use Cases

#1 Quantum Simulation

Quantum computers are especially powerful when it comes to simulating quantum systems. Unlike classical computers, which struggle with the uncertainty and complexity of quantum behavior, quantum computers operate using the same fundamental principles—like superposition and entanglement—that govern the systems they're modeling.

This natural alignment allows quantum computers to tackle problems that classical machines can't efficiently solve. They're able to simulate the inner workings of complex molecules, atomic structures, and physical phenomena that are too resource-intensive for even the best supercomputers.

What Can Be Simulated?

Some well-known examples of quantum systems that benefit from quantum simulation include:

• Photosynthesis mechanisms in plants and bacteria, helping researchers explore energy efficiency at the molecular level.
• Superconductivity, which may lead to revolutionary developments in energy transmission.
• Molecular interactions, such as how drugs bind to proteins or how new materials behave at the atomic scale.

#2 Drug Discovery and Healthcare

Quantum computers have the potential to transform drug discovery and healthcare by simulating molecular interactions at an unprecedented level of detail. Unlike classical computers, which struggle with complex molecular models, quantum computers can simulate molecules and chemical reactions in quantum states, which is essential for understanding diseases and creating new drugs.

Case in point: In collaboration with pharmaceutical company Boehringer Ingelheim, Google has demonstrated that quantum computers have the potential to simulate key components of Cytochrome P450—a vital enzyme in the human body—with greater efficiency and precision than classical methods. This enzyme plays a crucial role in drug metabolism, as it helps break down medications in the bloodstream. Accurately modeling its structure could significantly enhance how we predict drug interactions and treatment efficacy, accelerating progress in pharmaceutical research.

By accurately modeling molecular interactions, quantum computers could speed up the process of developing new medications, from cancer treatments to vaccines. The ability to predict how molecules interact could also help in the design of new materials and medical devices.

1) Protein Folding

Protein folding—the process by which a protein assumes its functional three-dimensional shape—is one of the most complex puzzles in biology. This process plays a crucial role in understanding diseases and developing effective drugs, yet classical computing methods often fall short due to the vast number of possible folding configurations.

Quantum computing offers a promising new angle. By leveraging quantum algorithms, researchers aim to explore the energy landscape of protein folding more efficiently than traditional methods. Although today's quantum hardware is not yet powerful enough to fully model real-world protein structures, ongoing research is laying the groundwork for hybrid quantum-classical approaches.

These quantum-enhanced models could one day complement breakthroughs like AlphaFold, helping scientists gain deeper insight into protein dynamics, misfolding disorders, and drug-target interactions—potentially transforming how we design treatments for complex diseases.

2) Disease Risk Prediction

Quantum machine learning is emerging as a powerful tool in the future of disease prediction and diagnostics. By harnessing the ability of quantum algorithms to analyze complex, high-dimensional biomedical data, these models may uncover patterns that traditional methods often miss.

As the technology matures, researchers envision quantum models that could enhance early detection of diseases through deeper analysis of genetic sequences, medical imaging, and patient records. Such insights could support clinicians in identifying risks earlier and more accurately, paving the way for more personalized and preventive healthcare strategies.

#3 Quantum Education

As quantum computing moves from theory to application, education is playing a vital role in preparing the next generation of scientists and engineers. To bridge the gap between abstract quantum principles and hands-on experience, educational quantum computers are being developed to make quantum technology more accessible to students and researchers.

Case in point: SpinQ has developed a series of portable NMR-based quantum computers specifically designed for educational and research purposes. Their devices—such as the SpinQ Gemini Mini and SpinQ Triangulum Ⅱ—operate at room temperature and are compact enough for classroom use.

These systems are already being used in universities and high schools around the world to teach quantum algorithms, quantum gates, and entanglement, providing practical learning opportunities that were once limited to large-scale research labs.

By making quantum computing tangible and approachable, SpinQ is accelerating the development of a skilled quantum workforce.

#4 Financial Modeling and Risk Analysis

The finance industry is actively exploring the use of quantum computing for complex financial modeling, risk assessment, and portfolio optimization. Thanks to their ability to process vast amounts of interdependent variables, quantum computers can simulate intricate market systems more efficiently than traditional models.

With quantum algorithms, institutions may gain sharper insights into market trends, option pricing, and risk calculations—potentially enabling faster and more informed decision-making.

Case in point: JPMorgan Chase has partnered with quantum computing companies like IBM to explore quantum algorithms for option pricing and risk analysis. These early studies show how quantum models could outperform classical Monte Carlo simulations in both speed and scalability.

Also Read: How Quantum Computing Benefits Financial Services [2025]

#5 Cryptography and Cybersecurity

One of the most well-known uses of quantum computers is in the field of cryptography. Classical encryption methods, such as RSA encryption, rely on the difficulty of factoring large numbers to keep data secure. However, quantum computers can leverage Shor's algorithm to factor these numbers exponentially faster, posing a threat to traditional encryption methods.

While this sounds alarming, quantum computers can also be used to develop quantum cryptography, which is inherently more secure than traditional methods. Quantum key distribution (QKD) allows two parties to share a secret key securely, even if a third party is listening in, making communications virtually unhackable.

Case in point: China's Micius satellite successfully demonstrated QKD over thousands of kilometers, showing that secure quantum communication is not only possible but already being tested at global scales.

#6 Optimization Problems

Optimization problems involve finding the best solution among a vast number of possibilities and are critical in many industries, including transportation, supply chain management, engineering design, energy systems, and manufacturing.

Quantum computers, with their ability to process complex datasets and explore large solution spaces simultaneously, offer promising advances in solving these complex optimization challenges more efficiently and accurately than classical methods.

1) Route and Traffic Optimization

Optimizing vehicle routing and traffic flow is essential for reducing congestion, saving travel time, and minimizing environmental impact. Traditional computational approaches struggle with the complexity and scale of urban traffic networks, where the number of possible routes can be enormous. Quantum computing can analyze these vast combinatorial problems more effectively, providing near-optimal routing solutions that improve traffic management and urban mobility.

Case in point: Volkswagen has employed quantum algorithms to optimize taxi dispatch routes in Beijing, successfully reducing travel times and easing congestion, thereby enhancing overall traffic efficiency and lowering emissions.

2) Supply Chain and Inventory Optimization

Supply chain management requires coordinating multiple variables such as inventory levels, transportation routes, and delivery schedules. Classical optimization techniques often fall short when handling the dynamic and large-scale nature of global supply chains, leading to inefficiencies like overstock or shortages. Quantum computing can handle these complex variables simultaneously, enabling real-time adjustments that improve inventory management and logistics efficiency.

Case in point: DHL has experimented with quantum computing to optimize warehouse picking routes and logistics scheduling, leading to increased delivery speed and reduced operational costs.

3) Engineering Design Optimization

In engineering, optimizing designs under multiple constraints is key to enhancing product performance and efficiency. Whether in aerodynamics, structural materials, or other complex systems, exploring a vast design space to find the best solution is computationally intensive. Quantum algorithms can accelerate this exploration, helping engineers identify innovative and optimized design configurations faster than classical methods.

Case in point: Airbus is researching quantum optimization algorithms to refine aircraft wing designs, achieving better fuel efficiency and improved aerodynamic performance.

4) Energy Systems Optimization

Smart grids require continuous balancing of electricity supply and demand, managing distributed energy resources, and optimizing load distribution. These are highly complex, nonlinear problems involving large datasets and real-time decision-making. Quantum computing has the potential to model and optimize these systems more accurately, enabling more resilient and sustainable energy distribution.

Case in point: Canadian company 1QBit partnered with British Columbia's utility provider to explore quantum-enhanced approaches for load balancing and energy distribution in smart grids.

5) Smart Manufacturing Optimization

Modern manufacturing processes involve complex scheduling, resource allocation, and quality control challenges. Quantum computing can optimize production workflows, minimize cycle times, and improve resource utilization by efficiently evaluating many variables and constraints simultaneously, leading to increased operational efficiency and product quality.

Case in point: Ford has investigated quantum algorithms to optimize assembly line scheduling, reducing production time and improving resource allocation in automotive manufacturing.

#7 Artificial Intelligence and Machine Learning

Quantum computers are also being explored for their potential to revolutionize artificial intelligence (AI) and machine learning (ML). Traditional AI algorithms require massive amounts of data to train, and while classical computers can handle this, quantum computing promises to speed up certain tasks dramatically.

Quantum computers can help improve machine learning models by accelerating pattern recognition and training processes. The potential of quantum-enhanced machine learning could lead to more efficient and accurate AI models, benefiting industries ranging from finance to healthcare.

#8 Weather Forecasting and Climate Modeling

Accurate weather prediction and climate modeling are among science's most computationally intensive tasks. They require simulating complex, nonlinear interactions between countless atmospheric variables across multiple time and space scales.

Quantum computers have the potential to revolutionize this field by enabling faster simulations of atmospheric dynamics and more accurate long-term climate projections.

Case in point: In collaboration with the U.S. Department of Energy, IBM is exploring how quantum computing could improve climate models by simulating cloud microphysics—an area that significantly impacts weather prediction but is notoriously difficult to model with classical methods.

#9 Material Science and Design

Quantum computers are expected to transform material science by enabling precise simulations of molecular and atomic interactions—something classical computers struggle with, especially in complex materials. This advancement could lead to the discovery of new materials with enhanced properties for use in electronics, manufacturing, aerospace, and energy storage.

Better Batteries

As global demand for energy and efficient energy storage continues to rise, Google is exploring how quantum computers could accelerate the discovery of advanced materials.

Case in point: In collaboration with chemical company BASF, Google has studied how quantum algorithms may enable more precise simulations of Lithium Nickel Oxide (LNO)—a promising battery material with a lower environmental impact than the widely used lithium cobalt oxide.

Despite its potential, LNO remains difficult to synthesize on an industrial scale, partly due to the complexity of its chemical behavior. By simulating its quantum properties, Google aims to support more sustainable battery innovation, including the development of cobalt-free alternatives and improvements in manufacturing efficiency.

#10 Agricultural Fertilization

Optimizing fertilizer production and application is crucial for sustainable agriculture. However, producing key components like ammonia through the traditional Haber-Bosch process consumes significant energy and contributes to global carbon emissions. Quantum computers offer a new approach by simulating the molecular interactions involved in nitrogen fixation with much higher accuracy than classical systems, potentially leading to the design of more efficient and eco-friendly catalysts.

Case in point: In 2017, researchers from Microsoft and ETH Zurich explored a more sustainable approach to ammonia production by studying nitrogen fixation through the enzyme nitrogenase—a natural process that occurs under normal temperature and pressure conditions.

Their findings revealed that classical computing alone is insufficient to model the reaction's complexity, but a hybrid approach combining classical and quantum computing could make accurate simulations possible. As researcher Matthias Troyer noted, developing a localized, low-energy nitrogen fixation method—such as one that could operate directly on farms—could dramatically enhance global food security.

#11 Fundamental Physics Research

Quantum computers offer new tools for probing the foundations of physics, enabling simulations of systems that are too complex for classical computation. They are particularly valuable in exploring high-energy physics, quantum field theories, and the behavior of particles under extreme conditions—areas that are crucial for advancing our understanding of the universe.

Case in point: Researchers at Google Quantum AI have collaborated with physicists to simulate simplified versions of lattice gauge theories, which are essential in describing fundamental forces like quantum chromodynamics. These early experiments mark a step toward using quantum processors to model the fabric of space-time and the interactions that govern subatomic particles.

#12 Quantum Chemistry

Quantum computing holds transformative potential for the field of chemistry. By simulating molecular interactions and chemical reactions at the quantum level, these systems can offer insights far beyond the reach of classical computers. Scientists can leverage this capability to design more efficient catalysts, unravel complex reaction pathways, and accelerate the discovery of novel materials for use in industries ranging from pharmaceuticals to energy.

#13 Quantum Computing in Space

Quantum computing is poised to revolutionize aerospace computation by solving complex optimization and simulation problems that traditional supercomputers struggle with. From trajectory planning to spacecraft material design, quantum algorithms can enable faster, more efficient solutions critical for space missions.

Case in point: NASA has been exploring the use of quantum computers for mission scheduling and spacecraft trajectory optimization. In collaboration with Google and other partners, NASA researchers have tested quantum algorithms to improve the efficiency of satellite constellations and deep-space navigation, laying the groundwork for applying quantum-enhanced solutions to future interplanetary missions.