Insider Brief
- Quantum simulation uses quantum computers to model molecules, materials and other quantum systems that can be difficult to simulate efficiently on classical computers.
- Digital and analog quantum simulation take different approaches, with digital systems offering broader flexibility and analog systems targeting specific physical problems.
- Current quantum simulation remains largely at the research stage, with practical applications dependent on larger systems, lower error rates and demonstrated advantages over classical methods.
A molecule with fifty interacting particles needs more numbers to describe its full quantum state than a classical supercomputer can store. That’s the wall classical chemistry keeps hitting, and it’s why physicist Richard Feynman proposed the idea that if you want to simulate a quantum system accurately, build a computer that runs on quantum mechanics too.
Quantum simulation applies this idea to systems that are difficult to model on classical computers. Molecules, materials, and quantum computers all follow the same laws of quantum mechanics, so researchers can use a quantum computer to represent the behavior of a molecule and simulate how that system evolves. This can provide a more direct way to study quantum systems than calculating their behavior entirely on a classical machine.
This article covers what quantum simulation is, why classical computers can’t do this job well, how the process works, what it’s being used for, and how far off practical results really are.
What is Quantum Simulation?
Quantum simulation uses a controllable quantum system to model another quantum system that’s too difficult to study directly. A quantum computer’s qubits get configured to behave like the particles in whatever is being studied, and observing how those qubits evolve reveals how the real system behaves.
Quantum simulation generally takes two approaches. Digital quantum simulation uses a general-purpose quantum computer to represent a target system through sequences of quantum gates. This makes the approach flexible enough to simulate different types of systems as hardware improves. Analog quantum simulation takes a more specialized approach, building a quantum system whose physical behavior directly represents the system being studied. This can make analog simulators less flexible, but they can be easier to design for specific problems.
Which approach makes sense depends on the goal. Digital simulation on a universal quantum computer could eventually tackle a broad set of problems. Analog simulation, using purpose-built devices, could deliver insight into specific systems sooner, without waiting for full general-purpose quantum computing to mature.
Why Can’t Classical Computers Simulate Quantum Systems?
The main challenge is exponential scaling. A classical computer needs to track the possible configurations of a quantum system, and the number of configurations doubles with each additional particle. Ten particles produce about a thousand configurations, while 20 produce about a million. At 50 particles, the number exceeds a quadrillion.
This quickly becomes too much for classical hardware to handle. Storing the complete quantum state of even a modestly sized molecule can require more memory than the world’s largest supercomputers have available. With RAM prices already high enough, needing more memory than a supercomputer has is probably not the upgrade path anyone wants.
Calculating how that state changes over time adds another layer of computational cost.
Classical chemistry deals with this complexity through approximation. Methods such as density functional theory and molecular dynamics simplify the quantum problem so it can be solved with available computing resources. The trade-off is reduced accuracy.
These methods work well for many chemistry problems. They become more difficult to apply to systems where quantum effects play a larger role, including certain chemical reactions, materials with complex electronic behavior, and molecules where strong interactions between electrons are difficult to approximate.
Quantum computers sidestep the storage problem entirely because their qubits already exist in superposition. A quantum computer with a modest qubit count can represent states that would need astronomical classical resources, and that’s the main reason quantum simulation might succeed where classical simulation fails. TQI’s guide to the quantum computing hardware landscape covers how different qubit modalities approach this differently.
How Does Quantum Simulation Work?
The process runs in three stages: encoding, evolution, and measurement.
Encoding: The properties of the system being studied, say the arrangement of electrons in a molecule, get translated into qubit states. Each qubit or group of qubits stands in for some aspect of the quantum system. Getting this mapping right takes care, since a sloppy encoding can misrepresent the physics it’s supposed to capture.
Evolution: The quantum computer applies operations that push its qubits to evolve according to the same rules governing the target system. For a molecule, that means the qubits change over time the way the molecule’s electrons would actually behave. Digital simulation breaks this into gate sequences. Analog simulation produces the evolution directly through the simulator’s own physical dynamics.
Measurement: Once the qubits have evolved, measuring them reveals information about the target system, its energy, structure, or how it responds to a perturbation. Measurement is inherently probabilistic, so the simulation typically runs many times to build reliable statistics.
Most current approaches use hybrid quantum-classical methods designed to work within the limits of noisy hardware. The Variational Quantum Eigensolver (VQE) is a common example. A quantum computer prepares and measures quantum states, while a classical computer adjusts the parameters to search for a molecule’s lowest-energy configuration. Keeping the quantum circuits short helps limit the amount of noise that can affect the results.
What Can Quantum Simulation Be Used For?
Drug Discovery
Understanding how a candidate drug molecule interacts with a protein depends on quantum mechanics. Classical simulations can approximate these interactions, but the calculations become difficult as the systems grow more complex.
In June 2025, IonQ , AstraZeneca, AWS, and NVIDIA demonstrated a hybrid quantum-accelerated workflow that achieved a more than 20-fold improvement in simulation time for the Suzuki-Miyaura reaction, a common step in pharmaceutical synthesis.
In December 2025, Qubit Pharmaceuticals and Sorbonne University showed that quantum algorithms can achieve greater-than-quadratic, and in some cases up-to-exponential, speedups for irreversible processes such as chemical reactions and protein folding. The work extended quantum algorithm research beyond the reversible processes typically considered in earlier theory.
Materials Science
Designing better batteries, solar cells, and superconductors requires a detailed understanding of how electrons behave inside complex materials. Quantum simulation could help researchers predict material properties before committing to expensive synthesis and laboratory testing.
SandboxAQ ‘s partnership with NVIDIA to simulate molecular behavior for drug discovery, battery design, and energy research. The practical question is whether current quantum hardware can simulate materials large enough to produce useful results.
Catalyst and Chemical Design
Catalysts are used across industrial chemistry to control and speed up chemical reactions. Better catalysts could also reduce the energy required for many industrial processes.
Quantum simulation could help researchers model how catalysts interact with molecules at the quantum level. This may reveal properties or reaction pathways that are difficult to capture with classical methods.
Fundamental Physics
Quantum simulation can help physicists study systems that are difficult to observe directly. These include exotic states of matter and particle behavior under extreme conditions.
The same techniques could also support research that leads to new physics experiments or future technologies.
Current Hardware Limits
All four applications face the same practical constraint. Useful quantum simulations require enough qubits and low enough error rates to model systems that classical computers cannot handle efficiently.
Current quantum computers remain limited to relatively small simulations. TQI’s guide to real-world quantum computing use cases covers where these and other quantum applications stand today.
What Are the Challenges of Quantum Simulation?
Hardware Limitations
Useful quantum simulations need more qubits and lower error rates than current machines can provide. Noise limits how large and complex a simulation can become before the results become unreliable.
Encoding Complexity
A molecule or material cannot be mapped onto qubits automatically. The encoding needs to capture the relevant physics while fitting within the available qubit budget.
Inefficient mappings can consume qubits that are already in short supply, leaving fewer available for the actual simulation.
Measurement Overhead
Quantum simulations often need to be run many times to collect enough measurements for reliable statistics. More complex systems can require a large number of measurements, increasing the time and computing resources needed to get a useful result.
Verification Difficulty
Verifying a quantum simulation can be difficult because the problems being simulated may be too complex for classical computers to solve independently. Researchers therefore often compare quantum results with smaller versions of the same problem that classical computers can still simulate.
Competition From Classical Methods
Classical simulation methods continue to improve, and modern classical computers can handle many problems that once seemed out of reach. For quantum simulation to provide a practical advantage, it needs to outperform the best classical methods on problems with real commercial value.
That has not yet been demonstrated at a commercially meaningful scale. Interest in quantum simulation remains high because some applications could become practical before other quantum computing applications, particularly if hardware and error rates continue to improve.
When Will Quantum Simulation Become Practical?
Small-scale quantum simulations already run on current devices, but these are mainly research demonstrations rather than sources of commercially useful insight. Simulating systems that are genuinely beyond classical reach will require larger quantum computers with lower error rates.
The timeline depends heavily on the problem being simulated. A 2022 resource estimate by Goings et al. (PNAS) found that fully simulating a cytochrome P450 active site could require around 4,900 logical qubits. A 2026 synthesis by PostQuantum , drawing on those estimates, puts that level of simulation around 2035-2038, while smaller simulations of catalytic fragments could become possible earlier.
Reaching useful quantum simulation will require progress on several fronts. Qubits need to become more reliable, systems need to grow, and algorithms and encoding methods need to use those resources more effectively. Quantum simulations will also need to show clear advantages over the best classical methods on specific problems.
Early applications are therefore more likely to focus on narrow, high-value problems than broad simulations. A particular molecule, reaction, or material may become useful before quantum computers can simulate large chemical systems in full.
There is no fixed schedule for this progress. Hardware setbacks could push timelines out, while improvements in error correction, algorithms, or system architecture could bring some applications closer sooner.
For readers looking to go deeper, TQI’s coverage of real-world quantum computing use cases , the quantum computing hardware landscape , and how quantum computers can accelerate drug design is worth checking out.
Frequently Asked Questions
What is quantum simulation?
Quantum simulation is the use of quantum computers to model the behavior of quantum systems such as molecules, materials, and chemical reactions. These systems obey quantum mechanics, which classical computers model poorly. Quantum computers can represent quantum states directly, letting them simulate systems that classical machines struggle with.
Why can’t classical computers simulate quantum systems?
The information needed to describe a quantum system grows exponentially with particle count. A modestly sized molecule can need more memory than the largest supercomputers hold. Classical methods rely on approximations to cope, and those approximations break down when quantum effects are strong. Quantum computers avoid the problem because their qubits already exist in superposition.
What is quantum simulation used for?
Drug discovery, materials science, catalyst and chemical design, and fundamental physics. Each depends on understanding quantum behavior with practical stakes: how a drug binds to a protein, what properties a new material will have, how a catalyst performs, or how an exotic quantum system behaves. All of it depends on quantum computers with enough qubits and low enough error rates to outpace classical methods.
Is quantum simulation available today?
Small-scale quantum simulations run on current quantum computers, but they function as research demonstrations, not sources of commercially valuable insight yet. Simulating systems that classical computers genuinely cannot handle, in ways that provide practical value, likely requires larger and higher-quality quantum computers that remain several years out.
Who first proposed quantum simulation?
Richard Feynman proposed the idea in 1982, arguing that accurately simulating quantum systems would require a computer that itself runs on quantum principles, since classical computers can’t efficiently represent quantum behavior. That insight helped launch the broader field of quantum computing.
How is quantum simulation different from other quantum computing applications?
It’s often considered one of the nearest-term applications, since some simulation problems may be solvable on smaller quantum computers than what breaking encryption or other demanding algorithms would need. It also fits the hardware naturally: quantum computers and the systems being simulated run on the same physical laws.