Six Industries Poised to Be Transformed by Quantum Computing

Part II to the initial article published in 2021 "What's the Big Deal about Quantum"

Apr 21, 2025

I’ve been fascinated by quantum mechanics and its real-world applications in quantum computing since 2021. Back then, I wrote a primer titled “What’s the Big Deal about Quantum” to explore its significance, use cases, and the emerging venture landscape.

Since then, the field has advanced dramatically, with major breakthroughs making headlines in 2024 and 2025:

This piece is a follow-up to that original article—grounded in refreshed examples in detail —to illustrate just how transformative quantum computing will be across some of the world’s most critical sectors.

Please note, this article is heavily assisted by ChatGPT.

Digital Assets (Cryptocurrency & Blockchain)

Near-term: Quantum computers threaten current cryptographic safeguards underpinning digital assets. Shor’s algorithm could factor large encryption keys, undermining RSA and ECC security that protect crypto wallets – a task infeasible for classical computers. This is driving an industry push toward quantum-resistant cryptography long before large quantum machines arrive, ensuring cryptocurrencies remain secure as quantum hardware progresses.
Long-term: Fully scalable quantum computers could break today’s blockchain encryption in a practical timeframe, enabling theft or forgery of digital assets that would have been impossible with classical computing. For instance, deriving a private key from a public key (which would take a classical computer billions of years) may be done in minutes or hours on a future quantum machine. Such an exponential speedup would force a complete overhaul of digital asset security. On the upside, quantum technology may also introduce ultra-secure transaction methods (like quantum key distribution) and new cryptographic algorithms, unlocking unprecedented security paradigms for digital asset exchanges.

Financial Services & Banking

Near-term: Quantum computing offers powerful tools for risk management and trading optimization. Banks are testing quantum algorithms (e.g. quantum Monte Carlo methods) to price complex derivatives and simulate market scenarios faster than classical HPC allows. This yields more accurate risk assessments and portfolio optimization by considering a broader range of variables in real time.
Early prototypes show quadratic speedups in Monte Carlo simulations – problems taking days on classical systems could run in hours – giving firms a competitive edge in decision-making. Major institutions like JPMorgan and Goldman Sachs have even formed in-house quantum teams to explore these advantages.
Additionally, financial firms are “quantum-proofing” their infrastructure by upgrading encryption now, ensuring secure transactions before quantum attacks become feasible.
Long-term: In a decade or more, full-scale quantum computers could solve financial optimization problems of staggering complexity that classical computers can’t handle at all.
- Global portfolio optimization with thousands of assets, real-time arbitrage across markets, or instantaneous fraud detection on vast data sets could become reality
- Quantum algorithms promise to find optimal investment strategies or loan portfolios in polynomial time – an enormous leap over today’s NP-hard brute-force limits. The magnitude of improvement could be transformative: tasks like risk simulations that would take classical supercomputers 10,000 years might be done in seconds, enabling near–instantaneous stress tests and economic forecasts. As quantum hardware matures (with fault tolerance beyond 2030), using it will move from a novelty to a necessity in finance.

Biotech & Pharmaceuticals

Near-term: Quantum computing is already being explored to speed up drug discovery and biomedical research. Early quantum processors can simulate small molecules and protein fragments with higher accuracy than classical methods, which struggle with quantum chemistry at scale. For example, pharma companies are using quantum algorithms to model how a candidate drug binds to a target protein – computations that classical computers must approximate (or cannot do at all beyond a certain molecular size).
Even with today’s nascent hardware, there are prototypes: Moderna and IBM have used a quantum algorithm (CVaR-VQE) to predict mRNA molecule structures more accurately than classical solvers, aiming to design stable mRNA vaccines faster. These near-term experiments show how quantum computers can tackle biomedical problems (like protein folding or genomic pattern recognition) that were previously impractical, potentially cutting initial R&D iterations from months to days.
Long-term: Quantum computing could revolutionize biotech by making the in silico simulation of complex biological systems routine. Tasks like accurately modeling the folding of large proteins or the interaction of a multi-step biochemical pathway – currently impossible for classical supercomputers – would become feasible.
This means researchers could virtually test thousands of drug molecules against a disease target with atomic precision, pinpointing the best candidate instantly. The improvement unlocked is massive: imagine reducing the drug development cycle from ~10 years down to a couple of years or less through computational breakthroughs. In medicine, quantum-powered analysis might enable personalized treatment optimizations (by crunching through a patient’s entire genomic and proteomic data to find tailored therapies, something classical AI can’t fully manage).
In short, quantum computing could yield cures and treatments for complex diseases by exploring biochemical possibilities that are astronomically large in number – a quantum leap in both speed and scope, likely materializing in the 10+ year horizon as more powerful quantum machines come online.

National Economic Planning (Government Modeling & Budgeting)

Near-term: Governments can begin harnessing quantum computers for enhanced economic simulations and scenario planning on a limited scale. While current quantum hardware is small, it can be used in hybrid quantum-classical algorithms to analyze specific complex problems – for example, optimizing a subset of a national budget or running simplified models with many variables.
Quantum-inspired simulators might help model supply chain shocks or epidemic impacts on the economy more accurately by sampling many scenario variants in parallel. In the next 5–10 years, we may see pilot projects where quantum algorithms assist in forecasting tax revenues or outcomes of infrastructure investments, offering insights beyond the reach of standard models. These early applications will likely be specialized and run alongside classical supercomputers, but they lay the groundwork for governments to develop expertise in quantum-based decision support tools.
Long-term: Quantum computing holds the promise to model an entire economy with unprecedented detail, transforming national economic planning and policy-making. Classical economic models are limited by computational complexity – for instance, simulating the full global economy or a national budget with millions of interdependent agents and policies is intractable today. Quantum computers could handle far larger, more complex models: one analysis noted that quantum machines will simulate financial networks at a scope that would require an impractical amount of classical resources (more than atoms in the universe) to replicate.
In practice, this means governments could run vast “what-if” simulations of fiscal and monetary policy choices (budgets, taxation, stimulus, etc.) and get results with high fidelity, helping them choose optimal strategies for growth and stability. They could also optimize multi-decade strategic plans – for example, finding the ideal allocation of trillions of dollars across education, defense, healthcare, etc., to maximize GDP growth or social welfare, a combinatorial optimization classical methods can’t fully solve.
The impact of such capability is enormous: one study predicts quantum computing could boost a nation’s productivity by ~7% by 2045 through better decision-making and innovation. Long-term (10+ years), quantum computing may become an indispensable tool in government planning, allowing leaders to navigate economic complexities with a clarity and foresight never before possible.

Materials Science & Chemistry

Near-term: Quantum computers are uniquely suited to simulating chemical and material systems, and early demonstrations are targeting high-value problems like battery chemistry and industrial catalysts. In the next few years, quantum algorithms (like the Variational Quantum Eigensolver) will be used to calculate properties of modest-sized molecules more exactly than classical chemistry codes can.
For example, automakers such as Daimler and Hyundai are already partnering with quantum computing firms to study new battery materials at the quantum level. These initial applications handle molecules that are on the edge of classical computing ability (e.g. certain lithium compounds) – classical supercomputers often have to resort to rough approximations for these, whereas quantum processors can in principle model them without loss of fidelity.
The benefit in the near term is targeted: even slightly improved accuracy in simulating a battery electrolyte or a catalyst’s reaction pathway can guide researchers to better designs, potentially shaving years off material R&D. We’re seeing the first quantum-guided discoveries (e.g. identifying a promising chemistry for a longer-lasting battery or a more efficient solar cell) on small scales, as a preview of what’s to come.
Long-term: In the 10+ year horizon, quantum computing could unlock materials and chemical discoveries that elude classical computing entirely. Many critical industrial challenges – like designing a room-temperature superconductor, a lightweight super-alloy, or a new carbon capture material – involve quantum interactions among electrons that are too complex for any classical simulation. A fully capable quantum computer can directly simulate these quantum systems, effectively letting scientists “test” new material structures virtually with exact physics.
The magnitude of improvement is illustrated by the case of fertilizers: finding a catalyst to produce ammonia (fertilizer) at low energy could save huge amounts of energy worldwide, but the molecular complexity (e.g. the enzyme Ferredoxin’s reaction) is beyond classical modeling. Quantum computers could solve this chemistry, leading to a catalyst that replaces the century-old Haber-Bosch process (which currently consumes about 2–3% of global CO₂ emissions) – a groundbreaking environmental and economic win.
Similarly, quantum-designed catalysts, drugs, or materials could be discovered in days versus decades, fundamentally accelerating innovation in energy, manufacturing, and healthcare. The long-term vision is that quantum computing becomes a standard part of the materials scientist’s toolkit, routinely delivering breakthroughs (new compounds, stronger materials, greener chemical processes) that were impossible to find before.

Logistics & Transportation

Near-term: Quantum computing excels at complex optimization problems, and logistics is rife with such challenges. Already, specialized quantum annealers have been used in pilot projects to optimize traffic flow and vehicle routing. A notable example is Volkswagen’s trial in Lisbon, where a quantum algorithm calculated the fastest routes for each bus in a fleet in near real-time, reducing commuter travel times and improving city traffic flow. These early successes target scenarios that classical algorithms handle sub-optimally – like coordinating many vehicles or deliveries simultaneously without causing jams or delays, an exponentially hard problem as systems scale.
In supply chain management, companies are testing quantum solvers for things like warehouse picking routes and delivery scheduling, aiming for even a few percent efficiency improvement. (In the automotive industry, for instance, simulations show that optimizing robotic assembly paths via quantum methods could improve productivity by 2–5%, translating to $10–$25 billion saved per year in manufacturing costs)
Over the next 5–10 years, we can expect to see quantum optimization software in use for specific high-value logistics sub-problems – essentially acting as an “optimizer plug-in” alongside classical systems to find better solutions for routing, scheduling, and resource allocation.
Long-term: The logistics sector could be completely transformed by large-scale quantum optimization. With advanced quantum computers, it becomes feasible to compute optimal solutions to problems that are currently unsolvable: think globally optimized supply chains considering every factory, truck, ship, and warehouse in one calculation, or real-time optimization of all traffic lights and routes in a megacity to eliminate congestion.
Such tasks involve astronomically many possibilities (combinatorial explosion) that classical computers can only ever approximate. Quantum computers, however, might handle these at scale, finding routes and schedules that minimize fuel usage and transit time far better than today’s heuristics. This could mean
- cheaper and faster deliveries for consumers (as shipping companies optimize container loading and routing across the world)
- significant fuel and energy savings (benefiting both profitability and the environment), and
- much more reliable transportation networks. In air travel, for example, quantum optimization could schedule flights and crews with zero conflicts and minimal delays across an entire airline’s network, something currently beyond reach.
The unlock here is not just speed but quality of solution – reaching the true optimal or something very close. In the long-term future, every major logistics decision (from urban planning to global distribution) may be vetted through quantum optimization models to ensure maximum efficiency. This promises an industry-wide leap in productivity, cost savings, and capability, making today’s logistical challenges a solved problem in retrospect.

Sources

Coinbase – “Is quantum computing a threat for crypto?” (2023) – Explains how quantum algorithms could break cryptocurrency encryption by cracking private keys, and notes it may take a decade or more for practical attackscoinbase.comcoinbase.com.
Foley & Lardner LLP – “Quantum Computing’s Transcendence: Impacts on Industry” (Nov 2024) – Discusses quantum computing’s impact on various sectors. Highlights for finance: enhanced risk modeling and portfolio optimization with broader variablesfoley.com. Highlights for pharma/biotech: inability of classical supercomputers to model molecules like caffeine and proteins, and quantum’s potential to simulate complex molecules for drug discoveryfoley.comfoley.com.
Deloitte Insights – “Quantum computing in financial services” (2023) – Industry report noting key use cases in finance (Monte Carlo simulations, portfolio optimization, etc.) and that major banks (Goldman Sachs, JPMorgan Chase, HSBC, Barclays) have dedicated quantum teams to develop algorithms for these problemswww2.deloitte.com.
IBM News/Blog – “How will quantum impact the biotech industry?” (2023) – Describes a collaboration with Moderna using a quantum algorithm (CVaR VQE) to predict mRNA structure stability, outperforming a classical solver and aiming at problems classical computing can’t handle as sequence length growsibm.com.
Oxford Economics & IBM – Quantum Computing and Economic Growth (Press Release) (Feb 2025) – Study forecasting that quantum computing could boost the UK’s productivity by up to 7% by 2045 (adding ~£212 billion to GDP) if its potential is realized, underscoring significant long-term economic benefitsoxfordeconomics.com.
Forbes Tech Council – “Could Quantum Computing Better Predict and Prevent Economic Downturns?” (Aug 2022) – Suggests that quantum computers will be able to simulate financial networks at a scope requiring more resources than there are atoms in the universe for a classical computerforbes.com, hinting at their superior capacity for modeling extremely complex economic systems.
Quantum Flagship (EU) – “Fertilizer and other quantum computer chemistry” (2023) – Explains the challenge of modeling the enzyme ferredoxin (involved in biological nitrogen fixation) on classical computers and how a quantum computer could help discover a low-energy alternative to the Haber-Bosch process, which currently consumes 2–3% of global CO₂ emissionsqt.eu.
IonQ Blog – “Improving Battery Chemistry with Quantum Computing” (2022) – Details how classical methods cannot fully simulate even moderately sized molecules (e.g. lithium dioxide) without approximationsionq.com. Describes partnerships with automakers (Daimler, Toyota, Hyundai) using quantum algorithms to explore new battery materials, leveraging quantum computers to tackle chemical reaction simulations that are intractable for classical computersionq.com.
Volkswagen News – “Volkswagen optimizes traffic flow with quantum computers” (Oct 2019) – Reports on a pilot in Lisbon where a D-Wave quantum computer was used to calculate optimal routes for buses in real time, reducing passenger travel times and improving traffic flowvolkswagen-group.com. This was one of the first real-world demonstrations of quantum optimization in transportation.
McKinsey & Co. – “Quantum computing use cases are getting real” (Dec 2021) – Identifies high-impact use cases in industries. Notes that in automotive manufacturing, even a 2–5% productivity gain from quantum optimization (for example, optimizing robot path planning in factories) could translate to $10–$25 billion in value per yearmckinsey.com, illustrating the scale of potential gains in logistics and operations.

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