Quantum computing will likely reshape several major industries, but not all at once and not overnight. The technology is progressing through distinct phases, with experts estimating that systems capable of delivering reliable, error-corrected results on real commercial problems won’t arrive until the mid-2030s. In the meantime, early pilot projects in drug discovery, battery design, climate science, and cryptography are already showing where quantum’s impact will hit first and hardest.
Where Things Stand Right Now
Today’s quantum computers are in what researchers call the NISQ era: noisy intermediate-scale quantum. That means they can run useful experiments but make too many errors to tackle the largest, most complex problems. IBM’s current roadmap centers on its Nighthawk processor, a modular chip with 120 qubits capable of running 5,000 quantum logic operations per calculation. That’s a meaningful step forward, but still far from the thousands of stable, error-corrected qubits needed for the transformative applications people are most excited about.
The hardware itself is extraordinarily demanding. Superconducting quantum processors, the type IBM and Google build, operate at temperatures between 10 and 20 millikelvin, colder than outer space. Cooling these systems consumes far more energy than the actual computation. Trapped-ion systems, like those from IonQ, run warmer (around 4 to 5 kelvin) and require roughly 300 times less cooling power, but each approach has its own trade-offs in speed and scalability.
Drug Discovery: Simulating Molecules From Scratch
Pharmaceutical companies are placing some of the earliest and biggest bets on quantum computing, and the reason is simple. Designing a new drug means understanding how molecules interact at the quantum level, something classical computers can only approximate for all but the smallest molecules. Quantum processors can, in theory, simulate these interactions directly.
AstraZeneca has partnered with Amazon Web Services, IonQ, and NVIDIA to build a quantum-accelerated chemistry workflow for synthesizing small-molecule drugs. Boehringer Ingelheim is working with PsiQuantum to calculate the electronic structures of metalloenzymes, proteins that play a critical role in how the body metabolizes drugs. Merck KGaA and Amgen are collaborating with QuEra to predict how well drug candidates will work based on their molecular structure. IBM and Moderna have already simulated mRNA sequences using a hybrid approach that splits the work between quantum and classical systems. Biogen is using quantum tools from 1QBit to speed up molecule comparisons for neurological conditions like Alzheimer’s and Parkinson’s.
None of these projects have yet produced a drug that couldn’t have been developed classically. What they’re doing is building the workflows, testing the algorithms, and identifying exactly where quantum gives an edge, so that when the hardware catches up, pharmaceutical companies can move fast.
Better Batteries Through Quantum Chemistry
The most expensive component in an electric vehicle is the battery, and improving battery chemistry requires simulating how lithium compounds behave at the atomic level. Hyundai and IonQ have partnered to build what they aim to make the most advanced battery chemistry model ever run on a quantum computer, focusing on lithium oxide in lithium-air batteries.
Lithium-air batteries are especially promising because they have a higher energy density than lithium-sulfur batteries, meaning more potential range per charge. The challenge is that the chemistry is complex enough that classical simulations hit a wall. IonQ’s approach uses at least 12 qubits and over 100 gate operations for the project. For context, a previous Daimler-IBM partnership studying lithium-sulfur batteries used only four qubits. The Hyundai project aims to improve cost, durability, capacity, safety, and charging behavior, essentially every metric that matters for making EVs more practical.
Climate Modeling and Carbon Capture
Climate science is full of problems that are technically solvable but computationally overwhelming. Simulating atmospheric chemistry, ocean dynamics, and carbon cycle feedbacks at high resolution pushes classical supercomputers to their limits. Quantum systems could eventually handle these simulations with greater accuracy.
One near-term target is carbon capture. Researchers at CERN’s Open Quantum Institute are exploring how quantum computing can more accurately model how CO2 interacts with materials called metal-organic frameworks (MOFs), which are used to trap carbon dioxide. Quantum simulations could also improve understanding of catalytic reactions and the energetics of CO2 interacting with ammonia, a common carbon capture chemistry. Better models mean better materials, and better materials mean cheaper, more efficient carbon removal. High-resolution climate simulations that are currently beyond classical reach are projected to become feasible by 2035.
Cybersecurity: The Threat and the Fix
Quantum computing creates one of the clearest and most urgent risks in technology: the ability to break widely used encryption. The same computational power that makes quantum useful for simulating molecules could eventually crack the math protecting bank transactions, medical records, and government communications.
NIST released its first three finalized post-quantum encryption standards in August 2024, algorithms specifically designed to resist attacks from future quantum computers. These standards are ready for immediate use, and NIST is urging system administrators to begin transitioning now because full integration across complex systems will take years. The message is straightforward: the quantum threat to current encryption isn’t here yet, but waiting until it arrives means being too late.
Finance and Optimization Problems
Financial institutions deal constantly with optimization, finding the best portfolio allocation across thousands of assets while managing risk, regulatory constraints, and transaction costs. These combinatorial problems grow exponentially harder as the number of variables increases, making them a natural fit for quantum algorithms. Firms including 1QBit have explored portfolio optimization on quantum annealing hardware, and MIT researchers have developed quantum algorithms specifically for this class of financial problem. The results so far are more proof-of-concept than production-ready, but the financial sector’s appetite for even small speed advantages means it will likely be an early adopter once the hardware matures.
Genomics and Personalized Medicine
The bottleneck in genomics has shifted. Sequencing a genome is now fast and cheap. Making sense of the data is the hard part. As genomic databases grow into the petabyte range, searching and analyzing them becomes a massive computational challenge. Quantum algorithms, particularly Grover’s algorithm for searching unstructured databases, could dramatically speed up this process. Researchers have already designed quantum circuits to identify nucleotides in single-molecule sequencing data and built hybrid quantum-classical approaches for related problems.
That said, reviews of the current literature find that only a limited number of studies have clearly demonstrated a quantum advantage over classical computing in bioinformatics. The potential is real, but the gap between theoretical speedup and practical, working systems remains wide.
The Realistic Timeline
The progression from lab curiosity to industrial tool will happen in stages. Between 2025 and 2027, pilot quantum systems will appear in select corporate environments, not replacing existing tools but proving their value in specific, high-impact scenarios. By 2030, major hardware companies including Intel, Rigetti, and IonQ aim to demonstrate logical qubits with reliable error correction, a milestone that would unlock computation across thousands of operational qubits.
The real inflection point comes in the early-to-mid 2030s. By then, quantum computing is expected to shift from experimental novelty to a strategic industrial asset, with advantages moving from isolated demonstrations to real-world applications solving problems that classical computers simply cannot. Universal, fault-tolerant quantum computing on the scale of science fiction remains beyond 2035, but the practical, targeted quantum advantage that will reshape drug development, energy, finance, and climate science is closer than most people realize.