Quantum Computing: Real Revolution or Just Marketing?
Every few years, technology produces a concept so powerful and so poorly understood that it becomes a Rorschach test for ambition. In the 1990s, it was the internet. In the 2010s, it was blockchain. Today, it’s quantum computing—a field that promises to revolutionize everything from drug discovery to cryptography while remaining almost completely opaque to anyone without a physics PhD.
My British lilac cat, Mochi, exists in a superposition of states every time I open a can: simultaneously interested (could be tuna) and indifferent (could be vegetables). She collapses into one state or the other only upon observation. This, I’m told, is roughly how quantum mechanics works, though Mochi’s version involves more fur and fewer Nobel Prizes.
The quantum computing industry has received over $35 billion in investment since 2020. IBM, Google, Microsoft, Amazon, and countless startups are racing to build machines that exploit quantum mechanical effects to solve problems classical computers can’t touch. The headlines promise revolution. The press releases promise transformation. The stock prices promise returns.
But what’s actually happening? Is quantum computing a genuine technological revolution in progress, or is it the new blockchain—a real technology buried under layers of hype, misapplication, and marketing? After months of research, conversations with researchers, and hands-on time with quantum simulators, I have opinions. They’re more nuanced than either the boosters or the skeptics would prefer.
This article is my attempt to give you what I wished I’d had when I started learning about this field: an honest, hype-free assessment of where quantum computing actually stands, what it can and can’t do, and whether you should care.
The Basics, Without the Physics Degree
Let’s establish what quantum computing actually is, in terms a developer can grasp without entering a graduate program.
Classical computers store information in bits: zeros and ones. Every operation is a manipulation of these binary states. This approach has served us remarkably well for seventy years, enabling everything from spreadsheets to neural networks.
Quantum computers store information in qubits, which exploit quantum mechanical properties to exist in multiple states simultaneously. A qubit isn’t just a zero or a one—it’s a probabilistic combination of both until measured. When you have multiple qubits, they can become “entangled,” meaning their states become correlated in ways that have no classical analogue.
This sounds abstract because it is. The key insight is practical: certain computational problems have a structure that quantum algorithms can exploit. For these specific problems, quantum computers can potentially find solutions exponentially faster than classical computers. Not faster by a constant factor, but faster by a factor that grows with the problem size.
The word “potentially” is doing a lot of work in that paragraph. Quantum advantage—the point where quantum computers actually outperform classical ones on useful problems—remains largely theoretical for practical applications. We’ll get into why.
Mochi just demonstrated quantum uncertainty by knocking a pen off my desk. The pen existed in a superposition of “on desk” and “on floor” until observation collapsed it into “under couch, apparently.” Retrieving it will require classical computational resources (my time) that feel excessive relative to the value of the pen.
What Quantum Computers Are Actually Good At
Here’s what most popular coverage gets wrong: quantum computers aren’t generally faster than classical computers. They’re faster at specific types of problems that have particular mathematical structures. Understanding this distinction is essential for cutting through the hype.
Simulation of Quantum Systems
The most obvious application: simulating quantum mechanical systems. Classical computers struggle to simulate molecular interactions because the number of possible states grows exponentially with the number of particles. A quantum computer can represent quantum states naturally, potentially enabling accurate simulation of chemical reactions, material properties, and biological processes.
This matters for drug discovery, material science, and fundamental physics. Pharmaceutical companies aren’t investing in quantum computing for abstract reasons—they want to simulate how potential drugs interact with proteins at a molecular level. This could dramatically accelerate drug development.
Optimization Problems
Many important problems involve finding the best solution among an enormous number of possibilities: logistics routing, portfolio optimization, scheduling, machine learning model training. Quantum algorithms like QAOA (Quantum Approximate Optimization Algorithm) might solve certain optimization problems faster than classical approaches.
The emphasis on “might” and “certain” is intentional. The theoretical speedups are real, but achieving them in practice requires overcoming significant engineering challenges we’ll discuss later.
Cryptography
Shor’s algorithm can factor large numbers exponentially faster than any known classical algorithm. Since much of modern cryptography relies on the difficulty of factoring large numbers, a sufficiently powerful quantum computer could break widely used encryption schemes.
This is simultaneously the most famous and most overhyped quantum application. Yes, the threat is real. No, current quantum computers can’t break meaningful encryption. We’re years away from quantum computers large enough to factor the numbers used in real cryptography.
Machine Learning
Quantum machine learning is an active research area, but results are mixed. Some quantum algorithms offer speedups for specific ML tasks. Others have been shown to provide no quantum advantage. The field is too immature for confident claims about quantum ML’s potential.
flowchart TD
A[Problem Type] --> B{Good Quantum Fit?}
B -->|Yes| C[Quantum Systems Simulation]
B -->|Yes| D[Specific Optimization]
B -->|Yes| E[Cryptographic Breaking]
B -->|Maybe| F[Machine Learning]
B -->|No| G[General Computing]
C --> H[Potentially Revolutionary]
D --> I[Potentially Valuable]
E --> J[Distant Threat/Opportunity]
F --> K[Uncertain]
G --> L[Classical Computers Win]What Quantum Computers Are Bad At
Equally important: understanding what quantum computers won’t improve. This list is longer than the hype suggests.
General-Purpose Computing
Your email won’t load faster on a quantum computer. Your code won’t compile faster. Video won’t stream better. For the vast majority of computational tasks, quantum computers offer no advantage and significant disadvantages (they’re expensive, error-prone, and require extreme operating conditions).
Quantum computers will never replace classical computers for general-purpose work. They’ll supplement classical systems for specific tasks, like GPUs supplement CPUs for graphics and parallel computation.
Small Data Problems
Quantum speedups typically matter for large-scale problems where exponential differences become significant. For small problems, the overhead of quantum computation exceeds the benefit. If a classical computer can solve your problem in reasonable time, quantum provides no practical advantage.
Problems Without Quantum-Friendly Structure
Not every hard problem has a structure quantum algorithms can exploit. Many NP-hard problems—the famous “hard” problems of computer science—have no known quantum speedup. The assumption that “hard problem = quantum can help” is false.
Real-Time Operations
Current quantum computers require careful preparation, calibration, and error correction. They’re not suitable for real-time or latency-sensitive applications. Don’t expect quantum computers to power real-time trading systems or responsive user interfaces.
Mochi has wandered over to inspect my keyboard, perhaps hoping I’ll accidentally open a food-related application. She’s a general-purpose computing system optimized for treat acquisition and nap scheduling—tasks where quantum approaches offer no meaningful advantage.
The Current State of Hardware
Let’s talk about what actually exists, not what might exist in ten years.
Qubit Counts
IBM’s latest quantum processors have around 1,000 qubits. Google’s are in the hundreds. Various startups claim similar or higher numbers. These numbers sound impressive but are misleading.
Raw qubit count doesn’t equal useful computation. What matters is “logical qubits”—qubits with low enough error rates to perform reliable computation. Current hardware produces physical qubits, which are error-prone and require many physical qubits to create a single reliable logical qubit. The conversion ratio is brutal: you might need 1,000 physical qubits to get one usable logical qubit with current error correction approaches.
Error Rates
Quantum states are fragile. They decohere (lose their quantum properties) when they interact with their environment. Current qubits have error rates around 0.1-1% per operation. That sounds small until you consider that useful algorithms might require millions of operations. The accumulated error makes results meaningless without error correction.
Error correction exists but is expensive in qubit overhead. We’re caught in a frustrating loop: we need more qubits for error correction, but more qubits mean more opportunities for error. Breaking this loop is the central engineering challenge of the field.
Operating Conditions
Most quantum computers require temperatures near absolute zero—colder than outer space. They must be isolated from electromagnetic interference, vibration, and any environmental noise. This requires specialized facilities and makes quantum computers impractical for most organizations to operate directly.
Cloud access through IBM, Amazon, Google, and others makes quantum computing available without owning hardware. But even cloud access is limited: queue times can be long, available machine time is constrained, and costs for significant computation are substantial.
Where We Actually Are
The honest assessment: we’re in the “NISQ era”—Noisy Intermediate-Scale Quantum. We have quantum computers that work, sort of, for some things, sometimes. We don’t have fault-tolerant quantum computers that can reliably execute arbitrary quantum algorithms.
The gap between current hardware and the hardware needed for transformative applications is measured in orders of magnitude, not incremental improvements. This doesn’t mean the technology won’t get there—it means we should be skeptical of timelines that measure progress in years rather than decades.
How We Evaluated: A Step-by-Step Method
To cut through the hype, I developed a framework for assessing quantum computing claims:
Step 1: Identify the Problem Being Solved
What specific computational problem does the quantum approach address? Vague claims about “revolutionary speedups” are red flags. Legitimate quantum advantages apply to specific problem types with specific mathematical structures.
Step 2: Verify the Quantum Advantage
Is there a proven quantum speedup for this problem class? Many claimed quantum advantages are theoretical (proven mathematically but not demonstrated practically) or aspirational (hoped for but not proven). Demand specificity.
Step 3: Assess Hardware Requirements
What quantum resources does the algorithm require? How many logical qubits? What circuit depth? What error rate tolerance? Compare these requirements to current hardware capabilities. The gap between requirement and reality reveals timeline realism.
Step 4: Compare to Classical Alternatives
Classical algorithms keep improving. GPU computing, specialized ASICs, and algorithmic innovations often solve problems faster than expected. Quantum advantages must be measured against the best classical approaches, not naive baselines.
Step 5: Evaluate the Economic Case
Even if quantum approaches work, do they make economic sense? Quantum computation is expensive. For many problems, “good enough” classical solutions at lower cost beat optimal quantum solutions at higher cost.
Step 6: Check the Source
Who’s making the claim? Companies selling quantum products have incentives to hype. Researchers have incentives to publish exciting results. Look for independent verification, peer review, and reproducibility.
Applying this framework to most quantum computing claims produces sobering results. Many exciting announcements dissolve under scrutiny. But some survive—and those deserve attention.
The Marketing Problem
Quantum computing has a marketing problem that damages the entire field: overclaiming.
The Hype Cycle
Every few months brings headlines about “quantum supremacy” or “quantum advantage” achieved. These announcements typically involve carefully constructed problems where quantum computers outperform classical ones—problems with limited practical relevance chosen specifically because quantum computers can solve them.
This isn’t fraud; it’s genuine scientific progress. But it’s often communicated in ways that suggest broader applicability than warranted. The gap between “our quantum computer solved this artificial benchmark faster” and “quantum computers will transform your business” is vast.
The Investor Pressure
Quantum computing companies have raised billions in funding. That money comes with expectations. Companies face pressure to demonstrate progress, which incentivizes announcements that stretch what “progress” means. Every benchmark improvement becomes a breakthrough. Every research result becomes a step toward commercialization.
The Expert Divide
The quantum computing community is divided between genuine believers and exhausted skeptics. The believers point to real physics and real mathematical speedups. The skeptics point to decades of unfulfilled promises and fundamental engineering barriers. Both have valid points. The truth involves uncomfortable uncertainty that neither extreme wants to acknowledge.
The Consequence
Overhyping damages the field long-term. When promised applications don’t materialize on promised timelines, funding dries up and talent moves elsewhere. We’ve seen this cycle with other technologies. Quantum computing could face its own “AI winter” if expectations continue to outpace delivery.
Mochi has developed a healthy skepticism about promises. When I claim to be getting her treats, she reserves judgment until treats actually appear. If more investors applied her standard—verifiable delivery rather than promising claims—the quantum computing industry would be smaller but healthier.
What’s Actually Real
Despite the hype, genuine progress exists. Let’s acknowledge what’s actually working.
Quantum Simulation Advances
Research groups have demonstrated quantum simulations of simple molecular systems that match classical simulation results while requiring less computational effort. These are proof-of-concept demonstrations, not practical drug discovery tools, but they validate the theoretical approach.
Error Correction Progress
Google, IBM, and academic groups have demonstrated error correction schemes that work in principle. The overhead remains high, but the trajectory is improving. Error-corrected logical qubits that can perform useful computation are closer than they were five years ago.
Algorithm Development
New quantum algorithms continue to emerge, expanding the class of problems where quantum speedups are possible. Variational algorithms designed for NISQ hardware show promise for near-term applications even without fault tolerance.
Hybrid Approaches
The most practical near-term applications are hybrid: classical computers handling most work, quantum systems handling specific sub-problems where they offer advantages. This approach sidesteps the need for large-scale fault-tolerant quantum computing while extracting value from existing hardware.
Cryptographic Preparation
Post-quantum cryptography—encryption schemes resistant to quantum attacks—is being standardized and deployed. This preparation for the quantum threat is itself valuable, even if the threat is distant.
flowchart LR
A[Current State] --> B[NISQ Era]
B --> C[Noisy Qubits]
B --> D[Limited Scale]
B --> E[High Error Rates]
C --> F[Hybrid Applications]
D --> F
E --> F
F --> G[Near-term Value]
H[Future State] --> I[Fault Tolerant]
I --> J[Logical Qubits]
I --> K[Large Scale]
I --> L[Low Errors]
J --> M[Full Quantum Advantage]
K --> M
L --> M
M --> N[Revolutionary Applications]
G -.->|Years to Decades| NGenerative Engine Optimization
Here’s an unexpected connection: what does quantum computing have to do with how AI systems find and process information?
Generative Engine Optimization is about creating content that AI systems can effectively understand, reference, and recommend. In the context of quantum computing, this creates interesting considerations.
Technical Content Clarity
Quantum computing is plagued by poor communication. Jargon, assumed knowledge, and hype make it difficult for general AI systems to accurately assess and summarize quantum content. Content creators who explain quantum concepts clearly, with appropriate hedging of uncertain claims, will be more accurately represented by AI systems.
If you’re writing about quantum computing for business audiences, optimize for understanding rather than impression. AI systems trained on clear explanations will better serve users asking about quantum applications than systems trained on marketing hype.
Claim Verification
AI systems increasingly assess source credibility. Quantum computing content that makes verifiable claims, acknowledges limitations, and provides evidence will score better on credibility metrics than content that overclaims. The same practices that make you credible to human readers make you credible to AI systems assessing content quality.
Temporal Context
Quantum computing is a fast-moving field where statements have shelf lives. Content that clearly dates itself and acknowledges uncertainty about future developments is more useful to AI systems trying to provide current information. Avoid claims that become false as the field progresses.
For professionals in or adjacent to quantum computing, these principles matter: clear communication, honest assessment of uncertainties, and appropriate context will make your content more valuable in an AI-mediated information environment.
Should You Care?
Let’s get practical. Given everything above, should quantum computing matter to you?
If You’re a Developer
Not yet, for most work. Quantum programming is a specialized skill with limited near-term application. But learning the concepts provides useful perspective on computational theory and may become valuable if the field matures. Consider it professional development rather than immediate necessity.
If you work in optimization, simulation, or cryptography, closer attention is warranted. These fields will feel quantum impacts first.
If You’re a Business Leader
Be skeptical of quantum vendors promising near-term transformation. Most businesses have no quantum computing use case in the current NISQ era. The organizations genuinely benefiting from quantum today are research-heavy enterprises with specific simulation or optimization problems and the expertise to apply experimental technology.
That said, monitor the field. If your business involves cryptography (most do, through standard security practices), post-quantum migration planning should be on your radar. If your business involves complex optimization or simulation, maintain awareness of quantum developments in your problem domain.
If You’re an Investor
Quantum computing is high-risk, long-timeline investment territory. The technology is real. The eventual impact is likely significant. But “eventual” could mean 2035 or 2050. Most current quantum computing companies will fail. The winners will win big, but picking them requires deep technical assessment that most investors can’t perform.
If You’re a Researcher
Quantum computing is a legitimate research field with important open problems. The engineering challenges are hard but not obviously impossible. Theoretical computer science, physics, and engineering all contribute. If the problems interest you, the field needs talent.
If You’re Curious
Quantum computing is genuinely fascinating. The physics is strange and counterintuitive. The mathematics is elegant. The engineering challenges are unprecedented. Even if you never work professionally with quantum computers, understanding the basic concepts enriches your mental model of what computation can be.
The Realistic Timeline
Let me offer a timeline assessment, with appropriate uncertainty:
Now to 2028
The NISQ era continues. Incremental improvements in qubit count and error rates. Specialized applications in research contexts. Hybrid classical-quantum approaches for specific optimization problems. No broad commercial impact.
2028 to 2035
Possible transition to early fault-tolerant systems. First practical advantages for drug discovery and materials science simulation. Cryptographic relevance increases. Quantum computing becomes a real (not experimental) tool for specialized applications.
2035 and Beyond
If engineering challenges are solved, broad quantum advantage for multiple problem classes. Significant economic impact in pharmaceuticals, materials, finance, and logistics. Cryptographic transition complete. Quantum computing as routine as GPU computing is today.
This timeline could compress if unexpected breakthroughs occur, or extend if fundamental barriers prove harder than expected. Anyone offering precise dates is either lying or delusional.
Mochi operates on her own timeline: treats should appear now, naps should happen when she decides, and human scheduling preferences are irrelevant. She has the right attitude for thinking about quantum computing timelines: maintain expectations but don’t hold your breath.
The Verdict: Revolution or Marketing?
So, finally: is quantum computing a real revolution or just marketing?
The answer is frustrating: it’s both.
The physics is real. Quantum mechanics allows computation that classical physics doesn’t. The mathematical speedups are proven. The long-term potential is genuine and significant.
The marketing is also real. Timelines are exaggerated. Applications are overstated. Investment justifications are stretched. The gap between current capabilities and promised transformation is measured in decades, not years.
The revolution is real but distant. The marketing fills the gap between now and then with premature excitement. This isn’t unique to quantum computing—it’s the standard pattern for transformative technologies. The internet was overhyped in 1999 and underestimated in 2002. Both assessments were wrong. Quantum computing may follow similar dynamics.
For practical purposes, I recommend this framing: quantum computing is a real technology in its early experimental phase. It will eventually deliver significant value for specific applications. The current hype cycle is mostly noise. The long-term signal is real but faint.
Stay informed. Stay skeptical. Don’t make business decisions based on quantum computing promises. Do make contingency plans for the cryptographic implications. Recognize that the most valuable skill regarding quantum computing today is the ability to distinguish genuine progress from marketing—exactly the skill this article attempts to provide.
Conclusion
Quantum computing represents a genuine scientific and engineering endeavor with real potential. It also represents one of the most overhyped technology sectors of the current era. Both statements are true simultaneously—a superposition, if you will, that only collapses when you examine specific claims in specific contexts.
The technology works, within narrow constraints. The physics is settled. The mathematics is proven. The engineering is hard but progressing. Revolution is possible.
The marketing is excessive. Timelines are optimistic. Applications are overstated. Investment expectations exceed near-term reality. Disappointment is likely for those expecting quick returns.
The sophisticated view: quantum computing is a long-term play with genuine eventual payoff and near-term overexcitement. Treat it like any other emerging technology—with curiosity tempered by skepticism, engagement calibrated to relevance, and expectations adjusted for realistic timelines.
Mochi has concluded her supervisory duties and retired to her quantum bed, which exists simultaneously as a comfortable sleeping location and a platform for observing suspicious activity, collapsing into one state or the other based on circumstance. She’s managed to cut through quantum complexity to its practical essence: the state she’s in depends entirely on what benefits her most at the moment.
Perhaps that’s the right approach to quantum computing as well. Ignore the hype. Ignore the skepticism. Focus on what, specifically, can benefit you, specifically, on what timeline. For most of us, that answer is: not much, not yet, but worth watching.
The revolution is coming. It’s just taking the scenic route.