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The promise of quantum computing in manufacturing

July 8, 2024
The technology, which could have $2 trillion in end-user economic impact by 2035,  is primed to revolutionize the advanced technologies of Industry 4.0.

For many, quantum computing is a term heard in movies to portray a futuristic scenario. And while yes, it’s fun to see Hollywood’s interpretation of it, some early adopters are already seeing great promise and return from the technology. Don’t worry, if you’re feeling like you’re missing the boat, you’ve got time, but it’s important to understand the benefits this technology will bring to decide if it’s something your organization should tap into.

A recent report from McKinsey reveals that four sectors—chemicals, life sciences, finance and mobility—are likely to see the earliest impact from quantum computing and could gain up to $2 trillion in end-user economic impact by 2035.

Manufacturing plays into every one of these industries and makes up a significant portion of the monetary estimate. Quantum computing is primed to revolutionize the advanced technologies of Industry 4.0.

What is quantum computing?

At its core, quantum computing departs fundamentally from traditional computing. While conventional computers use bits as the basic unit of information, represented with a 0 or 1, quantum computers utilize qubits that can be in both 0 and 1 states simultaneously. This property, known as superposition, coupled with quantum entanglement, enables quantum computers to process a vast amount of data at unprecedented speeds.

Quantum use cases in manufacturing

Data analytics and AI

It’s worth noting that quantum computers can process large amounts of data much faster than classical computers, enabling quicker and more complex data analysis. This increased processing capability can lead to more accurate predictions and insights.

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Specifically, quantum algorithms such as quantum neural networks and quantum support vector machines can significantly enhance machine learning models, making them more efficient and accurate in recognizing patterns and making predictions.

One of the biggest issues plaguing the future of AI and large language models, or LLMs, is the availability of data to train models. As AI continues to run out of data, many are turning to “synthetic data,” or data made by the AI itself to continue to train the models.

With quantum computing, developers can train machine learning and AI models with fewer data points. The benefits of this are huge, particularly for applications with limited data, such as rare disease and cancer imaging or new and tightly toleranced manufactured products. These products won’t have much data or examples of defects, but the models can be quickly trained on a smaller dataset for more accurate defect detection.


Quantum computers will revolutionize many pieces of the supply chain, from resource allocation to logistics and delivery. One of the first problems students are learning to tackle today with quantum computing is the age-old traveling salesman dilemma: How do I get to these five stops and come back to my base of operations in the shortest time?

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This is surprisingly not an easy task for a classical computer to solvecespecially as more and more considerations come into play—like the number of stops, traffic patterns, weather, etc. With quantum computing, organizations can optimize these logistical patterns quickly and efficiently, reducing costs and improving delivery times.

Quantum algorithms can also optimize resource allocation in manufacturing processes, leading to better utilization of machinery, materials and labor.


Quantum computers could revolutionize materials development and lead to the creation of more efficient and durable manufacturing materials. This technology allows organizations to simulate molecular and material behavior at an atomic level, enabling the development of new materials with desired properties.

Quantum computers are also good at doing certain types of physics simulations—enabling organizations to better examine how things like airflow, heat flow, water flow, turbulence and heat dissipation will affect every component of a product.

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If we take this a level deeper, we can imagine the possibilities quantum computing will bring to the accuracy and complexity of digital twins. Digital twins require extensive GPU power; when switching to quantum processing units (QPUs), you’ll be lowering the computational intensity, making it more feasible to simulate something like an entire airplane.

Last year, Rolls Royce announced its work on quantum algorithms for computational fluid dynamics, which is a part of digital twinning, to increase the efficiency of its jet engines.


Quantum algorithms can optimize the movement and operations of robots and automated guided vehicles (AGVs) in a manufacturing plant, improving efficiency and reducing energy consumption.

If you consider the discussion earlier around route optimization, you can begin to understand how this would benefit a company like Amazon in its warehouses as robots or employees pick products to be packaged.

In addition to picking, quantum computers could be used to look at frequently purchased items to ensure they are on the most optimal routes for pickers.

Additionally, quantum computing can improve the control algorithms for automated systems, leading to more precise and adaptive automation solutions. Quantum computers will process data faster, allowing automated systems to react faster to new data or changes to data, which will enable more dynamic and adaptive feedback responses for mission-critical applications.

Efficiency and sustainability

I’m sure as you’re considering these use cases, you’ve thought about the efficiencies and sustainability benefits throughout. It’s hard not to. At the most basic level, by optimizing granular manufacturing processes, quantum computing can help reduce waste and improve the overall efficiency of resource use.

However, there are ways we could see a much bigger impact when we think of things like energy consumption. Currently, the energy grid produces energy 24/7 regardless of the amount of power that’s required. Energy production plants are continuously burning coal or generating in other ways to supply us with energy.

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While the supply is increased or decreased based on projected demand, real-time demand is harder to act on with classical computers. Quantum computers will allow for more accurate power creation based on real-time demand, leading to less waste.

While many quantum applications are further out, some advancements, such as quantum machine learning and quantum optimization, are within reach. As these technologies evolve, the groundwork laid today will enable organizations to capitalize on quantum computing’s potential.

To repeat, quantum computing is projected to bring $2 trillion in end-user economic impact in the next 10 years. However, the digital transformation roadmap for implementing quantum for your business, from proof-of-concept to global deployment, is not a short journey.

Companies that want to be at the forefront should be investigating this technology today, so they are well-positioned to leverage these breakthroughs as increasing quantum capabilities meet the computational threshold required to solve industry-relevant problems.

About the Author

Erik Garcell

Erik Garcell is head of technical marketing at Classiq Technologies, a developer of quantum software. He was innovation product manager for IP.com and an innovation research scientist at Kodak Alaris. He has a doctorate in physics from the University of Rochester and a master's in technical entrepreneurship and management from Rochester’s Simon School of Business.