11 Quantum Computing Applications and Examples

Quantum computing (QC) has often felt like a theoretical concept due to the many hurdles researchers must clear. Chief among them is upping the number of qubits, or the units of information that these impressive pieces of hardware use to perform tasks. Whereas classical computer “bits” exist as 1s or 0s, qubits can be either — or both simultaneously. That’s key to massively greater processing speeds, which are necessary to simulate molecular-level quantum mechanics.

But quantum computers are inching closer to reality, thanks to Microsoft focusing on another key issue. Microsoft and Quantinuum have figured out a way to check qubit errors without altering a quantum computer’s environment, signaling a new era in quantum computing. 

Quantum computers have a reputation for being unreliable since even the most minute changes can create ‘noise’ that makes it difficult to get accurate results, if any. The discovery by Microsoft and Quantinuum addresses this problem and reignites the heated race between top tech companies like Microsoft, Google and IBM to conquer quantum computing.    

But even once quantum computing reigns supreme, its potential impact remains largely theoretical. Still, quantum’s hypothetical nature hasn’t stopped a steady stream of investment in the hope quantum computing could be a game-changer for various industries. From cybersecurity to pharmaceutical research to finance, here are some ways quantum computing facilitates major advancements.

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11 Companies Utilizing Quantum Computing

Location: Armonk, New York

Quantum Computing Applications: Supercomputing; quantum AI 

Quantum computing and artificial intelligence may prove to be mutual back-scratchers. Advances in deep learning will likely increase our understanding of quantum mechanics while at the same time fully realized quantum computers could far surpass conventional ones in data pattern recognition. Regarding the latter, IBM’s quantum research team has found that entangling qubits on the quantum computer that ran a data-classification experiment cut the error rate in half compared to unentangled qubits.

“What this suggests,” an essay in the MIT Technology Review noted, “is that as quantum computers get better at harnessing qubits and at entangling them, they’ll also get better at tackling machine-learning problems.”

IBM’s research came in the wake of another promising machine-learning classification algorithm: a quantum-classical hybrid run on a 19-qubit machine built by Rigetti Computing. 

“Harnessing [quantum computers’ statistical distribution] has the potential to accelerate or otherwise improve machine learning relative to purely classical performance,” Rigetti researchers wrote. The hybridization of classical computers and quantum processors overcame “a key challenge” in realizing that aim, they explained. 

Both are important steps toward the ultimate goal of significantly accelerating AI through quantum computing. Which might mean virtual assistants that understand you the first time. Or non-player-controlled video game characters that behave hyper-realistically.

 

Location: New York, New York 

Quantum Computing Applications: Financial modeling

At IBM’s Q Network, JPMorgan Chase stands out amid a sea of tech-focused members as well as government and higher-ed research institutions.

That hugely profitable financial services companies would want to leverage paradigm-shifting technology is hardly a shocker, but quantum and financial modeling are a truly natural match thanks to structural similarities. As a group of European researchers wrote, “[T]he entire financial market can be modeled as a quantum process, where quantities that are important to finance, such as the covariance matrix, emerge naturally.”

A lot of research has focused specifically on quantum’s potential to dramatically speed up the so-called Monte Carlo model, which essentially gauges the probability of various outcomes and their corresponding risks. A 2019 paper co-written by IBM researchers and members of JPMorgan’s Quantitative Research team included a methodology to price option contracts using a quantum computer.

 

Location: Redmond, Washington

Quantum Computing Applications: Quantum processors

Since the early 2000s, Microsoft has taken a keen interest in quantum computing. The company has made many contributions, including using quantum computing in energy management, but most of its work involves hardware.

In 2025, the company announced the development of the Majorana 1 Chip, which is the first quantum processor to run on topological qubits. With topological qubits, Microsoft solves one of the quantum computing toughest challenges: information storage. On regular quantum processors, information is stored on regular qubits, which are susceptible to temperature fluctuations and environmental disturbances. But with topological qubits, information can be stored across multiple quantum states, making systems more scalable. 

The new development has the potential to reduce errors and lead to large quantum computing applications. It also puts Microsoft on the forefront of quantum computing. 

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Location: Berkeley, California

Quantum Computing Applications: Weather forecasting

Recent research into whether quantum computing might vastly improve weather prediction has determined it’s a topic worth researching. And while we still have little understanding of that relationship, many in the field view it as a notable use case. 

Ray Johnson, the former CTO at Lockheed Martin and now an independent director at quantum startup Rigetti Computing, is among those who’ve indicated that quantum computing’s method of simultaneous (rather than sequential) calculation will likely be successful in “analyzing the very, very complex system of variables that is weather.”

While we currently use some of the world’s most powerful supercomputers to model high-resolution weather forecasts, accurate numerical weather prediction is notoriously difficult. In fact, it probably hasn’t been that long since you cursed an off-the-mark meteorologist.

 

Location: London, England

Quantum Computing Applications: Cybersecurity

To former presidential candidate Andrew Yang, Google’s 2019 quantum milestone meant that “no code is uncrackable.” He was referring to a much-discussed notion that the unprecedented factorization power of quantum computers would severely undermine common internet encryption systems.

But Google’s device (like all current QC devices) is far too error-prone to pose the immediate cybersecurity threat that Yang implied. In fact, according to theoretical computer scientist Scott Aaronson, such a machine won’t exist for quite a while. But the looming danger is serious. And the years-long push toward quantum-resistant algorithms — like the National Institute of Standards and Technology’s ongoing competition to build such models — illustrates how seriously the security community takes the threat.

One of just 26 so-called post-quantum algorithms to make the NIST’s “semifinals” comes from, appropriately enough, British-based cybersecurity leader Post-Quantum. Experts say the careful and deliberate process exemplified by the NIST’s project is precisely what quantum-focused security needs. As Dr. Deborah Franke of the National Security Agency told Nextgov, “There are two ways you could make a mistake with quantum-resistant encryption: One is you could jump to the algorithm too soon, and the other is you jump to the algorithm too late.”

As a result of this competition, NIST announced four cryptographic models in 2022 and is in the process of standardizing the algorithms before releasing them for widespread use in 2024.

 

Location: Toronto, Ontario

Quantum Computing Applications: Drug development

One company focusing computational heft on molecular simulation, specifically protein behavior, is Toronto-based biotech startup ProteinQure. It partners with quantum-computing leaders (IBM, Microsoft and Rigetti Computing) and pharma research outfits (SRI International, AstraZeneca) to explore QC’s potential in modeling protein.

That’s the deeply complex but high-yield route of drug development in which proteins are engineered for targeted medical purposes. Although it’s vastly more precise than the old-school trial-and-error method of running chemical experiments, it’s infinitely more challenging from a computational standpoint. As Boston Consulting Group noted, merely modeling a penicillin molecule would require an impossibly large classical computer with 10-to-the-86th-power bits. For advanced quantum computers, though, that same process could be a snap — and could lead to the discovery of new drugs for serious maladies like cancer, Alzheimer’s and heart disease.

 

Location: Stuttgart, Germany

Quantum Computing Applications: Electric vehicle manufacturing; supply chain optimization

QC’s potential to simulate quantum mechanics could be equally transformative in other chemistry-related realms beyond drug development. The auto industry, for example, wants to harness the technology to build better car batteries. 

In 2018, German car manufacturer Daimler AG (the parent company of Mercedes-Benz) announced two distinct partnerships with quantum-computing powerhouses Google and IBM. Electric vehicles are “mainly based on a well-functioning cell chemistry of the batteries,” the company wrote in its magazine at the time. Quantum computing, it added, inspires “justified hope” for “initial results” in areas like cellular simulation and the aging of battery cells. Improved batteries for electric vehicles could help increase adoption of those vehicles.

Daimler is also looking into how QC could potentially supercharge AI, improve the process for developing more sustainable batteries, plus manage an autonomous-vehicle-choked traffic future and accelerate its logistics.

 

Location: Wolfsburg, Germany

Quantum Computing Applications: Traffic simulation; autonomous driving

Volkswagen’s exploration of optimization brings up a point worth emphasizing: Despite some common framing, the main breakthrough of quantum computing isn’t just the speed at which it will solve challenges, but the kinds of challenges it will solve.

The “traveling salesman” problem, for instance, is one of the most famous in computation. It aims to determine the shortest possible route between multiple cities, hitting each city once and returning to the starting point. Known as an optimization problem, it’s incredibly difficult for a classical computer to tackle. For fully realized QCs, though, it could be much easier.

D-Wave and VW have already run pilot programs on a number of traffic- and travel-related optimization challenges, including streamlining traffic flows in Beijing, Barcelona and Lisbon. For the latter, a fleet of buses traveled along distinct routes tailored to real-time traffic conditions through a quantum algorithm, which VW continues to tweak after each trial run. According to D-Wave CEO Vern Brownell, the company’s pilot “brings us closer than ever to realizing true, practical quantum computing.”

 

Location: College Park, Maryland

Quantum Computing Applications: Sustainable fuel development

In the search for sustainable energy alternatives, hydrogen fuel, when produced without the use of fossil fuels, is serving to be a viable solution for reducing harmful greenhouse gas emissions. Most hydrogen fuel production is currently rooted in fossil fuel use, though quantum computing could create an efficient avenue to turn this around.

Electrolysis, the process of deconstructing water into basal hydrogen and oxygen molecules, can work to extract hydrogen for fuel in an environmentally-friendly manner. Quantum computing has already been helping research how to utilize electrolysis for the most efficient and sustainable hydrogen production possible. 

In 2019, IonQ performed the first simulation of a water molecule on a quantum device, marking as evidence that computing is able to approach accurate chemical predictions. In 2022, IonQ released Forte, its newest generation of quantum systems allowing software configurability and greater flexibility for researchers and other users. More recently, the company has released two new quantum computing systems and has found a way to facilitate communication between quantum systems.

 

Location: Boulder, Colorado 

Quantum Computing Applications: Quantum computing technology

Infleqtion (formerly known as ColdQuanta) is known for its use of cold atom quantum computing, in which laser-cooled atoms can act the role of qubits. With this method, fragile atoms can be kept cold while the operating system remains at room temperature, allowing quantum devices to be used in various environments.

To aid in research conducted by NASA’s Cold Atom Laboratory, Infleqtion’s Quantum Core technology was successfully shipped to the International Space Station in 2019. The technology has since been expected to support communications, global positioning, and signal processing applications. Infleqtion has also signed in multi-million dollar contracts by U.S. government agencies to develop quantum atomic clock and ion trap system technologies in.

The company plans to commercialize its technology in the coming years, with the initial goal of creating error-corrected logical qubits and a quantum computer.

 

Location: Broomfield, Colorado

Quantum Computing Applications: Machine learning

Quantinuum is a quantum computing company that spun out of Honeywell. As one of the largest players in the space, Quantinuum does it all — from research to hardware development, including high-performance quantum computers that use trapped-ion architecture for large qubit counts. 

Most recently, the company made new advances in the field by developing a generative quantum AI framework, which uses a quantum computer to generate more complex data and content than regular generative AI systems. According to the company, this new development can solve-real world problems such as drug discovery and financial modeling.

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