Hybrid Quantum Opens Up Real-World Applications

Discover how hybrid quantum computing is revolutionizing industries from banking to aviation, unlocking solutions once thought impossible.

• Fraud Detection Gets a Quantum Boost: HSBC’s hybrid quantum approach uses quantum simulators to run machine learning algorithms, showing immediate performance gains in detecting fraudulent transactions. This paves the way for future hardware advancements to handle larger datasets and deeper algorithms.

• Aviation Efficiency Takes Flight: Hybrid quantum computing optimizes cargo loading in aircraft, balancing weight to reduce drag and fuel consumption. Even a 75 cm center-of-gravity shift on a Boeing 747 can save 4 tonnes of CO₂ emissions on a 10,000-mile flight.

• Hybrid Quantum is Here to Stay: Industries like automotive (e.g., Volkswagen) already use hybrid quantum for real-time logistics and design optimization. This model isn’t a temporary fix—it’s a scalable, evolving solution that will remain essential even as quantum hardware matures.

Hybrid Quantum, a combination of classical computing with quantum processors have opened up exciting possibilities of solving real-world problems – a major step forwards in realizing the potential of quantum computing. HSBC, a high-street bank, working the UK’s National Quantum Computing Center, hardware provider Rigetti, and the Quantum Software Lab, at the University of Edinburgh, has pioneered its usage in fraud detection. Another study focused on a key problem in the aviation industry to find the optimal way to load cargo containers onto a commercial aircraft to maximize the amount of cargo that can be carried, and to balance the weight of the cargo to reduce drag and improve fuel efficiency.

HSBC normally uses classical machine learning to detect fraudulent transactions, but these techniques require a large computational overhead to train the models and deliver accurate results. The bank’s project team also found that a hybrid quantum could provide an immediate performance boost for detecting anomalous transactions. In this case, a quantum simulator running on a classical computer was used to run quantum algorithms for machine learning. These simulators allow the bank to execute simple Quantum Machine Learning (QML) programs, though they couldn’t be run to the same level of complexity as one could achieve with a physical quantum processor. Nevertheless these simulations showed the potential of these low-depth QML programs for fraud detection in the near term.

This results were quite encouraging and indicated that running deeper QML algorithms on a physical quantum processor could deliver an advantage for detecting anomalies in larger datasets, even though the hardware does not yet provide the performance needed to achieve reliable results. The outcomes have empowered the project partners to formulate a strategic road-map that will steer their ongoing developmental endeavors as the hardware evolves. One pivotal insight, for instance, is that even a fault-tolerant quantum computer would encounter significant challenges in processing the vast financial datasets generated by an institution such as HSBC, given that a finite duration is requisite to execute the quantum calculations for each individual data point.

This project further underscored the need for agreed protocols to maneuver the strict rules on data security within the banking sector. The HSBC team was able to run the QML simulations on its current computing infrastructure, thereby preventing any need to share sensitive financial data outside the system with other partners. Banks, however,  will need reassurance that their customer information can be protected when processed using a quantum computer.

The financial sector would be one of the earliest to adopt quantum computing going by the interest generated so far. 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.

Another study focused on a key problem in the aviation industry that has a direct impact on fuel consumption and the amount of carbon emissions produced during a flight. In this logistical challenge, the aim was to find the optimal way to load cargo containers onto a commercial aircraft. The business need was to maximize the amount of cargo that could be carried, and the other was to balance the weight of the cargo to reduce drag and improve fuel efficiency. Even a small shift in the centre of gravity could have a big effect. On a Boeing 747 a displacement of just 75 cm can increase the carbon emissions on a flight of 10,000 miles by four tonnes, and also increases the fuel costs for the airline company.

In the automotive sector, hybrid quantum computing has advanced from theoretical research to real-world implementation, delivering tangible business value. Industry leaders like Volkswagen using these systems to optimize complex routing algorithms, significantly enhancing supply chain efficiency and production workflows. By processing massive datasets through hybrid quantum neural networks, manufacturers enable real-time decision-making across logistics operations—from component deliveries to distribution networks. Additionally, these advanced capabilities facilitate the simulation and testing of next-generation vehicle systems under diverse conditions, accelerating design cycles and reducing time-to-market for new models. These case-studies are a clear indication that hybrid quantum computing isn’t just a workaround until quantum systems mature; it’s a durable solution designed to evolve. Even as quantum processors grow more powerful, the hybrid model will remain indispensable, and its flexibility and efficiency will continue to make it the choice for businesses looking to leverage the cutting edge in computation to solve today’s most demanding problems.