Quantum Computing: A Primer for Exponential Investors

Every week, another headline proclaims a quantum computing revolution. Google claims “quantum supremacy.” IBM unveils a 1,000-qubit processor. Chinese scientists teleport quantum information. But for investors, these headlines raise the key question—is this real? And if so, how do we capitalize on it?

The short answer is: Yes, quantum computing is very much real. But like the quantum world itself, it’s complex, unpredictable, and often misunderstood. Yes, the technology promises to redefine computational limits. But the investment opportunities surrounding it are also tangled in hype, speculation, and technical barriers.

This primer cuts through that noise. We’ll break down what quantum computers can and can’t do, highlight key players, and outline how to assess investment potential. No stock picks; just the foundational knowledge you need to make informed decisions. Let’s dive in.

The Exponential Mechanism

Before we dive into market opportunities, let’s see what makes quantum computers so special. For this, we’ll need a few basic concepts from quantum mechanics. (Don’t worry—we won’t ask you to solve Schrödinger equation.)

Limitations of Classical Computing

The term “classical computer” refers to every computer under the current paradigm, no matter how powerful. Your smartphone or laptop are “classical,” as are NASA’s supercomputers. What they share in common is that they all process information in “bits”—binary digits that are either 0 or 1.

To understand the limitations of bits, think of them as coins lying on a table. They can be either “heads” or “tails.” Let’s say you have two coins—there are four possible “states” for the table:

That’s the equivalent of a 2-bit computer. But what if you add a third coin? Well, now we have eight possibilities in total for the table:

Basically, each additional coin doubles the number of possible “states” of your table. But here’s the key limitation: no matter how many possible states it has… the table can still only be in one state at time. Adding more coins doesn’t give you more tables. You still only have one table.

Let’s look at how this limitation would play out in a real-world problem. Let’s say you’re trying to optimize a portfolio of 100 different assets. Each asset could be either in or out of your portfolio. We could represent this portfolio with 100 coins on our table. But again, our table can only be in one configuration at a time.

So if we were to calculate an answer on all 2¹⁰⁰ possible “states” of our portfolio, it would be like toggling our coin configuration until we try every combination. That would take an obscene amount of time. In fact, 2¹⁰⁰ is more combinations than there are atoms in the known universe.

That’s why, in classical computing, we use approximations for these types of “optimization” problems. That’s fundamental limitation of classical computing—being able to process only one state at a time. It creates a hard ceiling on what’s possible.

But what if we could somehow check all possibilities simultaneously? This is where quantum computing changes everything.

Superposition & Entanglement

Imagine if our portfolio coins could spin indefinitely on the table. While spinning, each coin represents both heads AND tails simultaneously. This is called superposition, and it’s the first key property of quantum bits (qubits). Unlike classical bits that must be either 0 or 1, qubits can exist in both states until they’re observed or “measured.”

But quantum mechanics offers something even more powerful. When we add a second spinning qubit, the two can become entangled. Think of entangled qubits like perfectly synchronized spinning coins—not just coordinated, but fundamentally connected in ways classical physics can’t explain.

This entanglement allows quantum computers to process information in ways impossible for classical computers, because changing one qubit instantly affects all its entangled partners.

Quantum Parallelism

This combination of superposition and entanglement creates quantum computing’s power. Remember our 100-asset portfolio problem? With classical coins, we could only check one possible portfolio configuration at a time. But with 100 entangled qubits, we could explore all 2¹⁰⁰ possible portfolios at once—not by iterating through each, but by suspending all possibilities in quantum superposition.

When we finally “measure” the system, it’s designed to collapse into the optimal solution we’re seeking. It’s as if all those spinning coins collapse onto the table at the same time, into the optimal pattern.

Of course, maintaining these quantum properties is incredibly difficult. Our spinning quantum coins are extremely fragile. The slightest disturbance—a tiny vibration, a stray magnetic field, even heat—can cause them to “decohere,” or lose their quantum properties. This is why quantum computers require such extreme conditions, like temperatures colder than outer space.

But when they work, quantum computers could revolutionize any field that involves exploring vast numbers of possibilities. Beyond portfolio optimization, think of drug discovery (testing millions of molecular combinations), climate modeling (simulating countless atmospheric interactions), or logistics (calculating optimal routes across global supply chains). These are the domains where quantum computing could create massive disruption.

The State of Quantum Technology

Headlines about quantum computing often focus on qubit counts—as if they’re measuring the power of a classical computer by counting transistors. The reality is far more complex. Consider two companies:

• Company A has a 50-qubit processor
• Company B announces a 100-qubit processor

Which is more advanced? The answer is… it depends.

The path to commercial quantum computing isn’t just about the numbers. It’s about how long the qubits maintain their quantum properties, how accurately they can be controlled, and how well they work together.

The NISQ Era

We’re currently in what researchers call the “Noisy Intermediate-Scale Quantum” (NISQ) era. This means our quantum computers are:

• Noisy: Qubits are extremely sensitive to interference
• Intermediate-Scale: We can build systems with dozens or hundreds of qubits

But maintaining quality as we add qubits remains the key challenge. Think of our spinning coin analogy. Current quantum computers are like trying to keep 50-100 coins spinning on a table. The slightest vibration, temperature change, or magnetic field can make them fall over (or “decohere”). The more coins we add, the harder it becomes to keep them all spinning perfectly.

The Error Correction Problem

This sensitivity creates a crucial scaling challenge. To build reliable quantum computers, we need “error correction.” This means using multiple physical qubits to create a single, more stable “logical” qubit. Current estimates suggest we might need thousands of physical qubits to create just one reliable logical qubit.

For investors, this means that raw qubit counts can be misleading. Instead, it’s also crucial to look at quality metrics like error rates and coherence times. In addition, different approaches to building these qubits face different scaling challenges:

Types of Quantum Hardware

Different companies are betting on distinct approaches to building quantum computers. Each has unique advantages and challenges:

Right now, we’re likely in a “Betamax vs VHS” moment—too early to know which approach will dominate. More likely, different approaches will serve different market needs.

Superconducting qubits, with their faster gate speeds and integration with existing electronics, could dominate high-performance data centers. Trapped ions, with their coherence times and precise control, might excel in molecular simulation or cryptography. Photonic systems, which naturally interface with fiber optic networks, could thrive in networked quantum applications.

This specialization mirrors today’s classical computing landscape, where CPUs, GPUs, and TPUs each serve distinct purposes. For investors, this means it’s wise to watch for technical milestones within each approach. Don’t assume a winner-take-all outcome and consider the full ecosystem.

The Quest for Quantum Advantage

You’ve likely heard terms like “quantum supremacy” or “quantum advantage” in the media. But what do these terms actually mean for investors?

The industry recognizes three key milestones:

• Quantum Supremacy: Solving any problem faster than classical computers (like Google’s 2019 claim)
• Quantum Advantage: Solving commercially useful problems better than classical alternatives
• Quantum Utility: Delivering enough practical value to justify the investment

For investors, this progression represents different levels of commercial viability. While quantum supremacy makes headlines, quantum utility is the holy grail. Companies might claim various benchmarks, but watch for practical advantages in real-world applications.

Applications of Quantum Computing

We’ve explored how quantum computers work and their current state of development. But where will they create actual business value? Several industries are already preparing for quantum advantage, each targeting specific computational bottlenecks that limit their progress.

These applications aren’t just theoretical—they’re driving real investment and development decisions now. Let’s examine the most promising use cases and their near-term commercial implications:

Financial Services

The financial sector is likely to be an early adopter of quantum computing. Many financial problems are optimization challenges that perfectly match quantum computing’s strengths. We looked at portfolio optimization earlier, but risk analysis and derivative pricing face similar computational barriers. Banks run millions of Monte Carlo simulations for risk assessment and rely on mathematical approximations for pricing complex derivatives.

Quantum computers could potentially perform these calculations exactly and exponentially faster. In high-frequency trading, quantum algorithms could optimize strategies across more assets and scenarios than currently possible—where even slight improvements in accuracy translate to significant profits.

Major financial institutions are already preparing. Goldman Sachs, JPMorgan, and others are developing quantum-ready algorithms and running pilots on early quantum systems. Many are also implementing quantum-inspired classical algorithms, using insights from quantum computing to improve current systems. This early investment by major banks is a clear signal that even incremental improvements in financial optimization could represent billions in returns.

Cryptography and Cybersecurity

The cybersecurity implications of quantum computing create another immediate call to action. This urgency stems from a specific threat: “store now, decrypt later” attacks. It works exactly as it sounds. Adversaries are collect encrypted data now and store it, waiting until quantum computers become mature enough to break it.

This threat is especially serious for data that must remain secure for many years. Think: military communications, trade secrets, or healthcare records. An encrypted message sent today might be safe from current computers, but stored copies could be decrypted by quantum devices in the future. That means organizations can’t afford to wait until quantum computers arrive to upgrade their security. By then, years of sensitive data could be exposed.

This dynamic has triggered a wave of preparatory activity. The National Institute of Standards and Technology (NIST) has already selected post-quantum cryptography algorithms for the new standard. Banks, government agencies, and large companies are beginning massive infrastructure upgrades.

AI and Machine Learning

The intersection of quantum computing and AI addresses fundamental computational bottlenecks. Training large AI models like GPT-4 requires massive computing resources—often tens of millions of dollars worth. The challenge lies in optimizing millions of parameters through vast mathematical spaces. Quantum algorithms could potentially navigate these spaces more efficiently.

What makes this convergence particularly valuable is its near-term potential. Companies don’t need perfect quantum computers to see benefits. Google has demonstrated quantum algorithms that could accelerate machine learning tasks, while IBM is developing quantum neural networks with novel architectures. Even quantum-inspired classical algorithms are already improving AI performance in specific applications.

Hybrid Quantum-Classical Computing

The future of computing will be hybrid. Quantum computers excel at specific calculations but require classical computers for everything else: data preparation, control operations, and results processing. This creates immediate opportunities in building quantum-classical interfaces.

Several companies are already commercializing hybrid approaches. D-Wave’s quantum annealers solve optimization problems by combining classical and quantum processing. Volkswagen uses this hybrid approach for traffic flow optimization, achieving better results than purely classical methods. As quantum capabilities grow, the ability to effectively split problems between classical and quantum processors becomes increasingly valuable.

Synthetic Biology

Synthetic biology presents some of the clearest use cases for quantum computing. Consider protein folding: a protein with just 100 amino acids can fold in more ways than atoms in the observable universe. Classical computers can only approximate these processes, leading to expensive trial and error in drug development.

The economic implications are significant. Traditional drug development often costs billions and takes over a decade, with most candidates failing in clinical trials. Even modest improvements in predicting protein behavior could save years of laboratory work. This explains why companies like Merck and Biogen are already partnering with quantum computing firms, while Amgen explores quantum approaches to designing more stable therapeutic proteins.

Next-Generation Energy

Quantum computing could accelerate energy innovation by solving key computational bottlenecks. Battery development for EVs and grid storage currently relies on approximating electron behavior in different materials. This leads to expensive trial-and-error testing. Quantum computers could simulate these quantum interactions directly, potentially revolutionizing battery design.

The implications for solar energy are equally significant. Even a 1-2% improvement in solar cell efficiency could represent billions in value. Quantum computers could model photon-material interactions precisely, enabling better photovoltaic designs. Companies like Quantum Brilliance and research institutions like the National Renewable Energy Laboratory (NREL) are already exploring these possibilities.

Fusion energy represents perhaps the most ambitious application. Companies like Commonwealth Fusion Systems are investigating how quantum computing could optimize plasma containment—a crucial challenge in fusion reactor design. While both technologies remain years from commercialization, their convergence could accelerate progress toward practical fusion power.

Market Landscape

These potential applications help explain why quantum computing has become a strategic priority for both companies and nations. Beyond commercial opportunities, quantum computing increasingly represents a matter of national security and economic competitiveness.

The Global Quantum Race

China has invested an estimated $10 billion in quantum technology, building the world’s largest quantum research facility and claiming several breakthrough patents. The U.S. has responded with the National Quantum Initiative, committing $1.2 billion in federal funding. Meanwhile, the EU’s Quantum Flagship program represents a €1 billion investment. Israel, Japan, and Australia are also making strategic investments in specific quantum technologies.

This national competition creates a complex landscape for investors. On one hand, government funding provides significant non-dilutive capital. This allows companies to pursue ambitious research without diluting shareholder value. However, growing concerns about technological sovereignty have led to stricter export controls. Thus, regulatory risk is a real consideration that investors must keep top of mind.

Market Projections

Against this backdrop, you’ll find market projections ranging from conservative to astronomical. Some analysts forecast 30-40% compound annual growth over the next decade, potentially creating another trillion-dollar market. Others are far more cautious. Why such disparity?

The answer lies in quantum computing development’s binary outcomes. Quantum computing isn’t a technology that will predictably improve year by year. Instead, it requires fundamental breakthroughs in error correction and qubit stability before many applications become possible.

Early revenue will likely come from consulting services, specialized hardware sales, and pilot programs—not from fully operational quantum solutions. Major corporations are already investing in quantum “readiness.” This creates immediate opportunities in quantum consulting, software development, and training services.

However, the real inflection point remains uncertain. This timing uncertainty explains why market forecasts vary so dramatically. Conservative estimates assume quantum advantage remains distant. Optimistic projections factor in faster-than-anticipated progress in overcoming technical hurdles.

The Quantum Stack

The field of quantum computing can be loosely broken down into three distinct layers. Each has its own dynamics and opportunities.

Hardware Layer

The hardware layer forms the foundation—and presents the biggest challenge. Companies are pouring billions into competing approaches, from superconducting circuits to trapped ions. High capital requirements mean only well-funded players can compete. In this environment, patents and intellectual property become key competitive moats.

Examples include:

When investing in quantum hardware, investors face a choice: Large tech companies like IBM and Google offer stable but diluted exposure, while pure-plays like IonQ and Rigetti provide direct exposure but higher risk. The former have resources and diverse revenue streams but limited quantum upside; the latter offer more potential upside but face binary outcomes tied to their technical approach.

Software & Services Layer

The software layer is evolving faster than hardware, with clearer paths to revenue. Cloud providers are already monetizing early quantum access. Development tools create valuable developer lock-in. Software companies benefit from lower capital requirements and faster iteration cycles. This lets them adapt to new hardware while building businesses around current capabilities.

Examples include:

Applications Layer

This is where near-term value is emerging. Rather than waiting for perfect quantum computers, companies are taking practical steps today. They’re building quantum-ready workflows and creating institutional knowledge. Most use hybrid setups where classical computers handle most tasks and quantum processors tackle the most complex calculations.

Examples include:

Key Indicators for Quantum Companies

In quantum computing, the gap between “somewhat useful” and “revolutionary” is so vast that many applications will remain theoretical until we achieve full-scale “fault-tolerant” quantum computers. Thus, when analyzing quantum computing companies, particularly public ones, traditional financial metrics tell only part of the story.

Instead, supplement your research with these key indicators:

Technical Progress

• Error rates and coherence times — Leading systems maintain coherence for >100 microseconds
• Quantum volume measurements — Look for accelerating growth year-over-year (quantum volume is a metric developed by IBM to measure both quantity and quality of qubits)
• Demonstrations of actual algorithms — Favor companies showing results on industry-relevant problems, not just abstract benchmarks
• Peer-reviewed publications — Strong teams publish regularly in journals like Nature and Physical Review
• Patent portfolio strength — Focus on fundamental architecture patents, not just peripheral improvements

Commercial Validation

• Customer evolution — Watch for transition from research labs to enterprise customers
• Revenue mix — Growing commercial revenue (vs. grants) signals market validation
• Partnership depth — Joint development agreements and shared development costs
• Pilot program progression — Transition from free pilots to paid engagements
• Commercial pipeline — Multi-year commitments and expanding deployments with existing customers

Financial Health & Funding

• Cash runway — Look for at least 24 months of runway in current market conditions
• R&D investment — Expect 40-60% for hardware companies, 30-40% for software
• Non-dilutive funding — Success with government grants and contracts reduces dilution
• Strategic backing — Partnerships with major tech or industry players that include investment
• Capital efficiency — Compare burn rate and technical progress against peers

Management & Expertise

• Technical credentials — Look for PhDs and publications from top quantum research groups
• Commercial experience — Prior success commercializing complex technologies
• Advisory board quality — Active involvement from quantum pioneers
• Talent retention — Watch for stability in key technical positions
• Milestone clarity — Clear technical roadmap with specific, measurable goals

Remember: in this early market, the absence of traditional metrics (like cash flow) doesn’t indicate weakness. Instead, look for steady progress toward clear commercial goals, with enough financial runway to get there.

Putting It All Together

Quantum computing represents one of the most significant technological opportunities of our time. But unlike other emerging technologies, it won’t follow a smooth adoption curve. The landscape will likely develop in three distinct phases:

Phase 1: Quantum Readiness (Now-3 Years)

Current opportunities lie in enabling technologies and quantum-inspired solutions. Look for:

• Companies generating revenue from research labs and early adopters
• Picks-and-shovels plays with clear paths to revenue
• Software and services firms building quantum expertise

Phase 2: Early Advantage (3-7 Years)

The first quantum advantages will emerge in specific high-value domains:

• Chemistry simulation for drug discovery
• Financial optimization and risk analysis
• Post-quantum cryptography
  Focus on companies positioned at these intersection points.

Phase 3: Quantum Revolution (7+ Years)

Fault-tolerant quantum computers could transform entire industries. But capturing this value requires careful portfolio construction:

• Balance pure-play quantum exposure with established tech leaders
• Consider the full stack: hardware, software, and applications
• Watch for companies building defendable positions in key verticals

Investment Strategy

Match your approach to your risk tolerance and time horizon. Consider a barbell strategy:

• Core Position: Established tech companies with quantum programs
• Satellite Positions: Pure-play quantum companies and enabling technologies
• Watch List: Companies developing quantum end-use applications

Remember: quantum computing isn’t just another technology wave—it’s a paradigm shift in information processing. Like the early days of classical computing, identifying winners requires patience and discipline. But the potential rewards could be exponential.