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Quantum Computing and Simulation
Quantum technology (QT) is revolutionizing the way we understand and interact with the physical world. It applies principles of quantum mechanics—such as superposition, entanglement, and the no-cloning theorem—to systems involving atoms, ions, electrons, photons, and molecules.
At the core of QT is the quantum bit (qubit), the basic unit of quantum information. Unlike classical bits that are either 0 or 1, a qubit can exist in a superposition of both states simultaneously. This property enables quantum computers to process an immense number of computations in parallel. Additionally, quantum entanglement allows particles to remain correlated across large distances, meaning the state of one directly affects the state of another. The no-cloning theorem ensures the security of quantum information, as it prohibits the duplication of arbitrary unknown quantum states.
One of the most transformative applications of QT is in quantum computation and simulation. Quantum computers harness the power of superposition and entanglement to solve complex problems that are intractable for classical systems. They can evaluate many possible solutions simultaneously, significantly accelerating tasks like number factoring, optimization, and quantum system modeling.
The power of quantum computers depends on the number of qubits and their quality measured by coherence, and gate fidelity. Qubit is very fragile, can be disrupted by things like tiny changes in temperature or very slight vibrations. Coherence measures the time during which quantum information is preserved. The gate fidelity uses distance from ideal gate to decide how noisy a quantum gate is.
We are now in era of Noisy intermediate-scale quantum (NISQ) in which quantum computers are composed of hundreds of noisy qubits that are not error-corrected. They Physical qubits are realized using superconducting Josephson junction qubits and the trapped-ion qubits. Other promising Qubits are Semiconductor based qubits; Topological qubits; and Photonic qubits.
Calculations using these noisy qubits can introduce errors and make long computations impossible. However, these computers still can demonstrate the advantages of quantum computing and various algorithms are being developed in disciplines such as machine learning, quantum chemistry and optimization.
One promising class of problems involves the simulation of quantum systems, with potential applications such as developing materials for batteries, industrial catalysis and nitrogen fixing. Simulating models of the physical world is instrumental in advancing scientific knowledge and developing technologies. Unfortunately, simulation is not always easy. Many important problems in physics, especially low-temperature physics and many-body physics, remain poorly understood because the underlying quantum mechanics is vastly complex. These also include scientific research areas, from high-energy, nuclear, atomic and condensed matter physics to thermal rate constants and molecular energies in chemistry
The Need for Quantum Simulation
Modern science and technology are deeply intertwined with our ability to model and predict the behavior of physical systems. Classical computers have served us well in simulating many aspects of the physical world, but they falter when faced with one of nature’s most fundamental and enigmatic realms: quantum mechanics. As systems grow larger and more entangled, the computational load required to simulate them increases exponentially—quickly exceeding the limits of classical supercomputers.
Enter quantum simulation—an emerging field that leverages controllable quantum systems to model and study other, less accessible or more complex quantum systems. Proposed by Richard Feynman in the early 1980s, quantum simulators promise to provide insight into phenomena that are currently beyond the reach of classical computation. From high-temperature superconductivity to exotic phases of matter and molecular dynamics, quantum simulation opens a new frontier in scientific discovery.
This limitation drives the need for quantum simulators—quantum systems purpose-built to emulate other quantum systems. First proposed by Richard Feynman in 1982, quantum simulation involves using a controllable quantum system to mimic a more complex or less accessible one.
What Is Quantum Simulation?
Quantum simulation can be conducted using two distinct approaches: classical simulation of quantum phenomena and simulation using actual quantum computers. Although often discussed interchangeably within the quantum community, these are fundamentally different methods. Classical simulations rely on traditional computing power, sometimes augmented with artificial intelligence (AI) or machine learning. This approach has been effective, yet it struggles when faced with quantum systems that grow exponentially in complexity.
For instance, modeling a molecule with just 50 electrons would demand tracking around 10³⁰ quantum states—a task far beyond even the most powerful supercomputers. Quantum simulators, in contrast, use qubits that naturally obey quantum rules, allowing them to efficiently mimic these interactions.
Quantum simulators exploit superposition and entanglement to represent real quantum phenomena directly. They are particularly useful in studying many-body physics, quantum chemistry, and exotic phases of matter that are otherwise computationally intractable.
Quantum simulation refers to the use of a well-controlled quantum system—such as trapped ions, neutral atoms, or superconducting qubits—to emulate the behavior of another quantum system that may be too complex, disordered, or unobservable in practice. Unlike digital quantum computing, which uses gates and algorithms to perform general computations, quantum simulators are often analog, mimicking the target system by reproducing its physical interactions directly.
There are two main types of quantum simulators. Analog quantum simulators evolve naturally according to Hamiltonians (energy functions) tailored to mimic specific quantum phenomena. Digital quantum simulators are programmable systems that use quantum gates to approximate quantum dynamics step by step, similar to how classical computers simulate processes numerically.
Both approaches aim to address questions like: How do electrons behave in high-temperature superconductors? What quantum phases emerge in disordered or strongly interacting systems? How can we simulate quantum chemistry for drug discovery and materials science?
Digital vs. Analog Quantum Simulation
There are two primary types of quantum simulators:
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Digital quantum simulators: Universal, gate-level quantum computers that can, in theory, execute any quantum algorithm. These systems offer programmability and flexibility but require significant error correction.
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Analog quantum simulators: Purpose-built systems designed to replicate specific quantum behaviors. While less versatile, they are easier to build and often more stable in the NISQ era.
A hybrid approach is emerging—programmable quantum simulators that combine digital flexibility with analog efficiency. These platforms are especially promising for simulating highly entangled states and correlated quantum matter
Technological Platforms for Quantum Simulation
Several platforms are being developed to carry out quantum simulations, each with its strengths and trade-offs.
Trapped ions are highly precise and controllable, making them ideal for small-scale analog or digital simulations. Neutral atoms arranged in optical lattices allow for large-scale arrays that simulate lattice models of quantum matter. Superconducting qubits offer scalability and integration into digital architectures, with strong support from industry leaders in quantum computing. Photonic systems enable quantum walks and boson sampling, with the potential for room-temperature operation. Rydberg atom arrays, with their highly tunable interactions, are excellent candidates for simulating quantum spin models.
Each platform brings different advantages depending on the target simulation and desired level of control, coherence, and scalability.
Applications and Impact Across Disciplines
In 2025, advancements in hardware, algorithms, and quantum error correction are accelerating the field into new frontiers. These breakthroughs are not only expanding our scientific understanding but also providing practical insights into challenges such as high-temperature superconductivity, energy sustainability, and climate dynamics.
Quantum simulation enables atomic-level precision in analyzing chemical reactions and material properties, surpassing the capabilities of classical methods. It allows researchers to dynamically model real-time quantum phenomena, such as electron entanglement or spin dynamics, which are crucial for understanding the fundamental behavior of matter. Moreover, quantum simulators are inherently scalable, making them ideal for exploring systems with thousands of interacting particles—like high-entropy alloys, superconductors, or strongly correlated quantum magnets.
A notable development was Google’s 2020 simulation of a 12-hydrogen atom molecule (H₁₂) using the Variational Quantum Eigensolver (VQE), a technique well-suited for NISQ hardware. Other dominant approaches include Quantum Phase Estimation (QPE) and Quantum Approximate Optimization Algorithms (QAOA).
In condensed matter physics, quantum simulators enable the exploration of exotic phases of matter such as topological insulators, spin liquids, and quantum Hall states. These phases are difficult to access experimentally or simulate classically but may hold the key to new quantum materials and technologies.
In quantum chemistry and materials science, molecules and materials with strong electron correlations are notoriously hard to simulate with classical methods. Quantum simulation could dramatically improve our ability to design new drugs, catalysts, and high-efficiency materials by directly modeling quantum interactions within atoms and molecules.
Quantum Chemistry: Accelerating Drug Discovery
In drug discovery, for instance, quantum simulations can help scientists understand how biomolecules interact, potentially streamlining the development of new medicines. Traditional drug development takes about 12 years and more than $2 billion on average to bring a new drug to market. Quantum simulation can dramatically accelerate this process by enabling researchers to identify target molecules, simulate compound interactions, and optimize therapeutic properties more quickly and accurately, thus reducing the need for physical testing.
A 2023 study exemplifies this approach by combining quantum computing, machine learning, and classical systems to aid in computer-aided drug design (CADD) and virtual high-throughput screening. Researchers used this methodology to predict how mutations affect drug binding and unbinding—essentially simulating how drugs attach to and detach from human cells.
Quantinuum’s InQuanto™ platform has recently showcased a scalable, error-corrected workflow for computing molecular energies with remarkable precision. By integrating quantum phase estimation (QPE) with logical qubits, scientists can predict chemical reaction pathways and energy levels far more accurately than before. This quantum approach is already in use by pharmaceutical leaders like Pfizer, who are leveraging it to develop antibiotics and antivirals, cutting research and development timelines by up to 40%.
Material Science: Engineering Exotic States
In material science, quantum simulators are enabling the design of novel materials with exotic properties.
Materials science provides another compelling use case. For decades, researchers have used classical computing to simulate Density Functional Theory (DFT), a method for predicting a material’s electronic properties. DFT involves solving complex equations to approximate a system’s electron density and energy, which is essential for understanding properties such as conductivity, chemical reactivity, and thermal stability. Quantum simulation could dramatically improve DFT analysis, enabling the design of higher-performing and more sustainable materials.
Google Quantum AI, for instance, achieved a major breakthrough in 2025 by using a 69-qubit hybrid analog-digital simulator to model a quantum magnet and observe the Kosterlitz-Thouless transition—a quantum phase change long theorized but never directly simulated. Their results even challenge established physics frameworks, like the Kibble-Zurek mechanism. Simultaneously, advances in twist engineering of van der Waals materials—such as layered graphene and boron nitride—are producing Moiré superlattices that exhibit tunable superconductivity and topological phases, holding potential for next-generation electronic devices.
Climate and Energy: Simulating Earth’s Complexity
Quantum simulation is also being harnessed to better understand and address climate challenges. IBM, in collaboration with The Weather Company, has developed quantum-enhanced models that offer forecasts with five times the resolution of traditional systems—enabling more accurate disaster prediction and response. At the same time, Pasqal’s quantum algorithms are solving complex differential equations to improve the design of carbon capture catalysts, an essential step toward achieving global net-zero emissions goals.
In high-energy and nuclear physics, quantum simulation is being explored to understand lattice gauge theories, which underpin our models of fundamental particle interactions. Emulating these theories on quantum devices could shed light on processes occurring in the early universe or inside neutron stars.
In information science and quantum technology, understanding how quantum systems evolve and decohere—using simulators—can accelerate progress in building robust quantum computers, quantum networks, and error-correcting codes.
Challenges
Despite the remarkable progress, significant challenges remain. Scalability remains a central issue, as researchers work to build simulators with enough quantum elements to tackle large, real-world problems. Noise and decoherence are significant obstacles, as quantum systems are fragile and easily disrupted, which affects the fidelity of simulations. Benchmarking is also difficult—verifying results from quantum simulators can be challenging, especially when classical models cannot provide reference outcomes due to computational intractability.
Noise and decoherence continue to limit the duration and accuracy of quantum simulations, even with sophisticated error correction techniques. Current quantum processors—such as IBM’s 127-qubit chip—still lack the scale to tackle ultra-complex problems like protein folding or full-scale drug interactions. Additionally, the global quantum workforce is insufficient to meet growing demand, with more than three-quarters of organizations citing a lack of qualified talent as a barrier to adoption.
Despite these hurdles, advances in quantum control, fabrication, and error mitigation are rapidly improving the feasibility and reliability of large-scale quantum simulations.
Global Research and Innovation
Looking ahead, several trends are expected to redefine the landscape. Distributed quantum simulations, where multiple NISQ (Noisy Intermediate-Scale Quantum) devices are interconnected, are beginning to take shape. Projects by QuTech and IBM are already demonstrating such quantum networks. In parallel, hybrid systems that blend quantum simulators with exascale classical computing—like those developed by Lockheed Martin—are setting new standards for high-performance computing.
Neutral atoms, although less mature than superconducting qubit platforms like IBM’s 127-qubit system, have demonstrated impressive capabilities. In 2021, a team led by Harvard University showcased a 256-qubit programmable quantum simulator using optical tweezers and Rydberg interactions. The atoms were arranged in diverse geometries (square, honeycomb, triangular), enabling the exploration of different quantum phases and phenomena.
Perhaps most promisingly, the development of topological qubits by Quantinuum and Harvard represents a leap toward fault-tolerant quantum simulation, where data is protected by the topology of quantum states rather than sheer redundancy.
Institutions and companies around the world are pushing the boundaries of quantum simulation. At Pacific Northwest National Laboratory (PNNL), researchers are developing quantum simulators that help test advanced algorithms, bypassing the limitations of current quantum hardware. These simulators enable early-stage validation of quantum algorithms for chemistry and physics before deploying them on actual quantum processors.
Interestingly, the Quantum Flagship report emphasizes the near-term potential of analog quantum simulators. These specialized hardware systems are designed to emulate quantum systems with fewer qubit requirements than fully digital quantum simulators. This makes them more accessible for applications in automotive, pharmaceuticals, chemicals, oil and gas, and photovoltaics, where simulating molecular and atomic interactions is critical.
QAI Ventures is at the forefront of quantum innovation, supporting startups through its Quantum Funds and Accelerator Program.
Among the 2024 accelerator cohort is QPerfect, a French quantum computing company spun out from the European Center for Quantum Sciences (CESQ) in Strasbourg. QPerfect’s flagship product, MIMIQ, is a virtual quantum computer designed to simulate large-scale quantum systems using state-of-the-art methods like Matrix Product States. It bridges the gap between classical and quantum computing, enabling the testing and refinement of algorithms for future use on fault-tolerant quantum machines.
Another standout in the 2024 cohort is Commutator Studios, a startup focused on optimizing the performance of quantum applications on current quantum processors. Their proprietary methods identify and compensate for previously overlooked errors in quantum software, allowing third-party applications to run more effectively. They are also pioneering new developer tools tailored specifically for the unique challenges of quantum programming.
QAI Ventures is also actively building community through educational and collaborative events. One such initiative is the GenQ Quantum Computing Hackathon, co-hosted with Quantum City and the University of Calgary. Scheduled for October 4–6, 2024, in Alberta, Canada, the event follows the qConnect conference and aims to bring together innovators, students, and professionals to accelerate quantum development.
Hybrid Analog-Digital Architectures
A major enabler of these advances is the emergence of hybrid quantum architectures. Google’s Sycamore processor exemplifies this approach by combining analog dynamics, which enable rapid entanglement of qubits, with digital precision for state preparation and measurement. This configuration has significantly reduced simulation errors—by up to 90% in studies of quantum magnetism—and has delivered results previously unattainable through classical computation alone.
Error-Corrected Logical Qubits
Another key innovation lies in the use of error-corrected logical qubits. Companies like Microsoft and Quantinuum have pioneered methods to encode logical qubits across multiple physical qubits, effectively suppressing noise and preserving coherence. These error-resilient systems are essential for scaling quantum simulations to address industrially relevant problems. Quantinuum’s H2 system, for instance, now supports full-stack chemistry workflows that were once considered years away.
AI-Quantum Synergy
Artificial intelligence is playing a transformative role in enhancing quantum simulations. Machine learning algorithms help optimize quantum circuits and interpret complex simulation data. SpinQ has demonstrated quantum neural networks that analyze real-world logistics to optimize supply chains, while IBM’s Qiskit integrates AI to automate noise mitigation in condensed matter simulations. This symbiosis between AI and quantum systems is accelerating research timelines and improving simulation reliability.
Two spin-offs from neutral-atom quantum research—Pasqal (Paris) and QuEra Computing (Cambridge, MA)—are commercializing programmable quantum simulators. QuEra raised $17 million in funding, signaling growing investor confidence in this technology.
The Road Ahead: A New Era of Discovery
Recent work published in Nature underscores the importance of hybrid quantum simulation strategies. Researchers from the University of Strathclyde, Max Planck Institute, Ludwig Maximilian University, and Microsoft argue for a dual-track development, akin to how analog and digital classical computing co-evolved over decades.
According to Professor Andrew Daley of Strathclyde, practical quantum advantage will be reached when quantum devices solve real-world problems that are intractable for classical supercomputers. In the short term, this will likely occur within analog or hybrid quantum simulators tackling highly specific quantum problems.
“Analogue quantum simulators are moving from qualitative demonstrations to quantitative problem-solving,” Daley states. “Combining digital and analog techniques offers a path to efficient, scalable, and accurate quantum simulation.”
Quantum simulation stands at the convergence of quantum physics, computer science, and engineering. As quantum simulators grow in capability, they will become indispensable tools for understanding the fundamental laws of nature and designing technologies previously thought impossible. Initiatives like Europe’s Quantum Flagship, DARPA’s ONISQ, and NSF’s Quantum Leap underscore the global race to harness quantum simulation for scientific and strategic leadership.
In the coming decade, quantum simulators may help us uncover the mysteries of high-temperature superconductivity, engineer designer materials from first principles, and unlock new methods of energy generation and storage. These advances will ripple far beyond laboratories—reshaping computing, medicine, defense, and our fundamental understanding of the universe.
Predictions about the timeline for scalable, fault-tolerant quantum computers vary. IBM has announced plans to deliver a fully error-corrected 200-qubit quantum system by 2029. Microsoft, meanwhile, envisions reaching one million quantum operations per second within the next decade. The European Commission’s Quantum Flagship program has laid out a roadmap to create a fully integrated quantum ecosystem in Europe by 2030.
Conclusion: Simulating the Unsimulatable
Quantum simulators represent one of the most powerful and practical near-term applications of quantum computing, offering the ability to model complex quantum systems with a precision that classical computers can never match. By harnessing the unique properties of qubits—such as superposition and entanglement—quantum simulation opens new frontiers across science and industry.
Quantum simulation is not merely a scientific tool—it represents a paradigm shift in how we interrogate and interact with the universe. By moving beyond the constraints of classical approximation, quantum simulation allows us to study the very fabric of reality through systems that mirror its behavior. From decoding the complexities of quantum materials and molecules to confronting global challenges like energy transition and climate modeling, quantum simulators are reshaping the frontiers of science and technology.
As the field matures, the convergence of more robust hardware, smarter algorithms, and hybrid classical-quantum simulation techniques will usher in a new era of practical quantum advantage. From accelerating the discovery of life-saving pharmaceuticals to engineering novel materials and unlocking deeper cosmological insights, quantum simulation is poised to transform how we explore and shape the world around us.
Rather than approximating quantum complexity through limited classical means, we are now building systems that speak the same language as nature itself. In doing so, quantum simulation redefines what is computable—and reveals a future where even the most intricate dynamics of the universe can be understood, optimized, and harnessed. It is not merely a research tool, but a foundational technology for the 21st century.
As Microsoft’s Quantum Ready initiative emphasizes, now is the time for organizations, governments, and researchers to prepare for a quantum-enabled future. The next decade will witness a dramatic transformation as quantum simulators evolve from experimental platforms into indispensable engines of discovery and innovation. For those ready to embrace this future, the possibilities are limited only by the laws of physics themselves.
References
Feynman, R. P. (1982). Simulating physics with computers.
Georgescu, I. M., Ashhab, S., & Nori, F. (2014). Quantum simulation.
Preskill, J. (2018). Quantum computing in the NISQ era and beyond.
National Quantum Initiative (NQI) Reports and Roadmaps
Quantum Flagship (EU), ONISQ (DARPA), QLCI (NSF) Programs
References and Resources also include:
https://physicsworld.com/a/highly-programmable-quantum-simulator-operates-with-up-to-256-qubits/
https://www.techexplorist.com/developing-next-generation-quantum-algorithms-materials/47960/
https://research.ibm.com/blog/quantum-entanglement-forging
https://www.sciencedaily.com/releases/2022/07/220729173217.htm
