Tom Hundley🤖 Written by Claude · Curated by Tom Hundley
I'm a tech executive and software architect—not a subject matter expert in every field I write about. I'm a generalist trying to keep up with emerging technologies like everyone else. This article was researched and written by Claude (Anthropic's AI assistant), and I've curated and reviewed it for our readers.
Forget the hype about quantum computing being "10 years away." These companies are using it right now—and seeing measurable results.
For years, quantum computing was discussed in terms of potential. "Someday it will revolutionize drug discovery." "Eventually it will transform financial modeling." "In the future it will optimize supply chains."
2025 is the year "someday" became "today."
A recent survey found that 22% of business leaders already see quantum computing making a "huge impact" for early adopters. Another 53% are planning to build quantum computing into their workflows. And 81% say they've reached the limit of what they can do with classical computers alone.
The question is no longer whether quantum computing works for business. It's which businesses are using it—and what they're achieving.
Financial services has emerged as quantum computing's first major commercial proving ground. The combination of massive optimization problems, time-sensitive decisions, and enormous stakes makes finance ideal for quantum applications.
In September 2025, HSBC announced they were using IBM's Heron quantum computer to improve their bond trading predictions by 34% compared to classical computing alone.
This isn't a laboratory experiment. This is one of the world's largest banks using quantum computing for actual trading decisions.
Why it matters: Bond trading involves predicting price movements across thousands of securities, each influenced by dozens of factors—interest rates, credit risk, market sentiment, economic indicators. Classical computers model these relationships, but they're limited in how many interactions they can consider simultaneously. Quantum computers can evaluate vastly more combinations, finding patterns classical systems miss.
JPMorgan and Amazon are collaborating on a "decomposition pipeline" that translates portfolio optimization problems into forms that can run on today's quantum computers.
Portfolio optimization is the classic example of a quantum-suitable problem. You're trying to find the ideal mix of investments that maximizes returns while minimizing risk. With thousands of potential investments, each correlated with others in complex ways, the number of possible portfolios is astronomical.
Classical computers use simplifying assumptions to make the problem tractable. Quantum computers can explore more of the actual solution space.
According to Hyperion Research, 17% of enterprises view finance-oriented optimization as their top quantum computing interest. Use cases include:
| Application | Current Challenge | Quantum Advantage |
|---|---|---|
| Portfolio Optimization | Too many combinations to evaluate fully | Explore vastly more portfolio combinations |
| Risk Analysis | Models oversimplify correlations | More accurate risk correlation modeling |
| Fraud Detection | Pattern matching at scale | Faster identification of anomalous patterns |
| Derivative Pricing | Computationally expensive simulations | Faster Monte Carlo simulations |
| Credit Scoring | Limited variable interactions | More factors, better predictions |
Healthcare presents some of the most impactful quantum computing applications—problems where better solutions could save millions of lives.
In January 2025, Pfizer joined IBM's Quantum Network to use quantum molecular modeling in the search for new antibiotics and antivirals.
The problem: Antibiotic resistance is a growing global health crisis. We need new drugs, but discovering them is brutally slow and expensive. Traditional drug discovery involves screening millions of compounds, most of which fail. Understanding which molecules will work requires modeling how they interact with target proteins at the molecular level.
The quantum solution: Molecules are quantum systems. Classical computers simulate them using approximations that sacrifice accuracy for tractability. Quantum computers can simulate molecular behavior more accurately, predicting which drug candidates are most likely to work before expensive laboratory testing.
The Cleveland Clinic is using a dedicated healthcare quantum computer, developed with IBM, to model protein-protein interactions relevant to cancer research.
Cancer often involves proteins that fold incorrectly or interact in ways they shouldn't. Understanding these interactions at the molecular level is key to developing targeted treatments. Quantum computers can model these protein dynamics more accurately than classical systems.
In March 2025, engineering company Ansys used IonQ's quantum computer to speed up analysis of fluid interactions in medical devices by 12% compared to classical computing alone.
This might sound modest, but medical device design involves countless simulation iterations. A 12% improvement per iteration compounds across thousands of design cycles, potentially shaving months off development timelines.
Healthcare applications span the drug development pipeline:
| Stage | Application | Impact |
|---|---|---|
| Discovery | Molecular simulation | Identify promising drug candidates faster |
| Pre-clinical | Protein interaction modeling | Better predict drug efficacy and safety |
| Clinical | Patient trial matching | Find eligible patients more efficiently |
| Manufacturing | Process optimization | Reduce production costs and time |
| Delivery | Treatment personalization | Match patients to optimal treatments |
Logistics might seem less glamorous than drug discovery, but it's where quantum computing is producing the most immediately practical results.
In March 2025, Ford Otosan announced they used D-Wave's quantum annealing technology to reduce scheduling times from 30 minutes to less than five.
This isn't a pilot. This is deployed in production.
The scheduling problem: Ford's manufacturing involves coordinating thousands of operations across multiple production lines, with complex dependencies and constraints. Finding optimal schedules using classical computers takes time—time that limits how quickly production can adapt to changes.
Quantum annealing is particularly good at these constraint-satisfaction problems. It finds near-optimal solutions much faster than classical approaches.
The classic logistics problem is the "traveling salesman": What's the shortest route that visits every city exactly once? With a handful of cities, it's easy. With hundreds or thousands of stops, the number of possible routes explodes beyond any computer's ability to evaluate fully.
Real-world logistics is the traveling salesman problem multiplied by:
Classical computers use heuristics—educated shortcuts that find good-enough solutions. Quantum computers can evaluate more possibilities, finding better solutions faster.
A Hyperion Research survey found supply chain management is the second-highest quantum computing interest (16%), just behind finance (17%).
The quantum opportunity in supply chains:
| Challenge | Classical Limitation | Quantum Advantage |
|---|---|---|
| Inventory placement | Too many warehouse/product combinations | Optimize across full network |
| Route planning | Heuristics miss optimal solutions | Find better routes faster |
| Demand forecasting | Limited factor interactions | More sophisticated prediction models |
| Supplier selection | Can't evaluate all options | Global optimization across suppliers |
| Risk management | Simplified disruption models | Better model cascading effects |
Understanding why quantum computers help with these problems requires understanding what makes them different.
Many business problems are optimization problems: find the best solution among millions or billions of possibilities. Classical computers evaluate possibilities sequentially. Even very fast sequential evaluation can't check every option when the possibilities number in the trillions.
Quantum computers, through superposition, can explore many possibilities simultaneously. They don't check every option—they use quantum interference to amplify good solutions and cancel out bad ones.
Molecules, proteins, and materials are inherently quantum systems. Classical computers simulate them using approximations that sacrifice accuracy for speed.
Quantum computers can simulate quantum systems directly, maintaining accuracy that classical simulations can't match. This is why drug discovery and materials science are such promising applications—you're using quantum computers to model quantum reality.
Some quantum algorithms can potentially train machine learning models faster or find patterns in data that classical algorithms miss. This is still more experimental than optimization or simulation, but early results in financial fraud detection and medical imaging are promising.
You don't need to build a quantum computer to start benefiting from quantum computing. Here's how organizations are getting started:
All major quantum computers are available through cloud services:
| Provider | Platform | Quantum Hardware |
|---|---|---|
| IBM | IBM Quantum | Superconducting (Heron, Eagle) |
| Amazon | AWS Braket | Multiple (IonQ, Rigetti, OQC) |
| Microsoft | Azure Quantum | Multiple (IonQ, Quantinuum) |
| Google Quantum AI | Superconducting (Willow) |
These platforms let you experiment without massive capital investment. Most offer free tiers for learning and small-scale testing.
Not every problem benefits from quantum. Good candidates have:
Start by cataloging your most computationally intensive problems. Which ones are you solving with heuristics because exact solutions are intractable?
Your technical team needs basic quantum computing understanding. Resources include:
Start small with problems that:
Successful pilots become production applications. Build the infrastructure, expertise, and processes to deploy quantum solutions at scale.
The money following quantum computing tells a story:
Quantum computing companies raised $3.77 billion in equity funding during the first nine months of 2025—nearly triple the $1.3 billion raised in all of 2024.
The market is projected to grow from $1.8-3.5 billion in 2025 to $5.3 billion by 2029—a 32.7% compound annual growth rate.
McKinsey estimates quantum computing could unlock up to $250 billion of market value across industries like pharmaceuticals, finance, logistics, and materials science.
Let's be clear about what quantum computing can and can't do today:
What it can do:
What it can't do (yet):
The hybrid reality:
Quantum computing won't replace classical computing—it will complement it. As Bain notes, quantum computing will become "an important part of a broad mosaic of solutions," tackling specific problems where classical systems fall short.
The organizations benefiting today aren't replacing their IT infrastructure with quantum systems. They're identifying specific high-value problems where quantum provides an edge, and building hybrid classical-quantum workflows.
Quantum computing has crossed from theoretical promise to practical business impact. Real companies are seeing real results:
These aren't future possibilities. They're 2025 achievements.
The organizations that start exploring quantum computing now—identifying use cases, building skills, running pilots—will be positioned to capture the larger benefits as the technology continues to advance.
Those that wait will find themselves competing against quantum-enabled competitors with capabilities they can't match.
The quantum era isn't coming. For early adopters, it's already here.
Emerging technologies create competitive advantages for prepared organizations. At Elegant Software Solutions, we help businesses understand and leverage AI and emerging tech capabilities. Contact us to explore how quantum computing might benefit your operations.
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