While the concept of quantum computing has been discussed for more than 40 years, only recently have experiments indicated that a practical quantum computer may be possible. Recent developments in this area have captured headlines with dramatic claims—and equally dramatic rebuttals. Google’s Willow chip demonstrated error-corrected operations in late 2024, while D-Wave’s assertion of quantum supremacy in early 2025 sparked both excitement and skepticism.

Despite the industry’s growing pains, many researchers feel that practical quantum computing is inevitable. Large-scale quantum computers might be able to solve some types of problems exponentially faster than a traditional (classical) computer, such as physical simulations and breaking encryption methods. Most likely, however, the most revolutionary and transformative applications of quantum computing will be in areas we cannot yet imagine.

No matter the ultimate usage domain, a fundamental prerequisite for all quantum computers is the development of quantum bits (qubits), which can be in a coherent superposition of multiple states simultaneously, unlike the classical bit with two discrete states.

Challenges in developing qubits

While the attributes of qubits give quantum technology its theoretical advantages, it has proven difficult to create qubits in the physical world. Isolating qubits to prevent noise from introducing errors in the results is challenging, although using superconductors shows promise, and other novel implementations are emerging. Current implementations are, however, mainly experimental and may not easily scale to the levels necessary to solve the real problems for which they should someday excel. Achieving this will require optimization of current concepts and a continued search for novel materials platforms to base qubits on. Both of these are areas where atomistic modeling plays a crucial role, by giving a way to predict the behavior of new materials using simulations, before they are even synthesized, and providing in-depth insight into the quantum phenomena the guide the qubit operation.

Fig. 1: Modeling qubits in a realistic way involves large-scale atomistic models with possibly amorphous materials, disorder, interdiffusion, localized defects, and so on, in order to capture all effects that may contribute to noise, such as thermal effects and interface scattering.

The most common atomistic simulation method on the quantum-mechanical level, density functional theory (DFT), has been used very successfully for decades to predict electronic properties, chemical reactions, material strength, phase transitions, thermal transport, and many other key material properties. DFT alone is, however, not enough for the study of materials for quantum computing, but must be supplemented both with algorithms that provide higher accuracy and more approximate methods to allow for large-scale simulations with sufficiently short turnaround time.

An effective materials exploration platform for qubits needs to provide flexibility to transition seamlessly between all these methods and maximize the synergy between them, to allow materials engineers to:

run large-scale molecular dynamics and structure prediction based on machine-learned potentials (MLPs);
use DFT for electronic structure and defect level analysis;
switch to tight-binding models, calibrated against DFT, to handle much larger structures;
employ non-equilibrium Green’s functions (NEGF) for quantum transport calculations;
validate DFT results against higher-accuracy models such as GW; and
implement custom algorithms to study specific quantum-mechanical properties such as many-body spectra.

All these simulation methods should be combined under a unified interface, with common data structures, eliminating the friction of moving data between disparate codes and enabling truly synergetic, multiscale workflows.

For high-performance simulations, the calculations should furthermore be able to take advantage of hardware acceleration on graphics processing units (GPUs), which have been proven to provide 20X faster training and 5–15X faster execution of MLPs, with routine simulations of 100,000+ atoms at near-DFT accuracy. GPUs can also provide 5–10X speedups for DFT and tight-binding calculations, especially for larger models, and up to 100X gains for NEGF transport simulations.

A working solution using atomistic simulation

Synopsys QuantumATK atomistic modeling and simulation software meets all these requirements, bridging classical and quantum frontiers in materials science. It is a unified ecosystem integrating multiple modeling approaches within a single framework. For more than a decade, QuantumATK has been in wide use by engineers at leading companies who rely on its robustness for critical tasks and a steady pace of performance improvements.

Integrating machine learning (ML) into atomistic simulation is more than a speedup play—it reshapes how new materials are discovered. The QuantumATK framework provides built-in moment tensor potential (MTP) and neural-network models trained on DFT data, and also allows external MLP calculators to plug directly into the framework. These models can achieve near-quantum accuracy while running 1000–100,000X faster than untrained methods. The platform also offers users a flexible application programming interface (API) to develop custom ML models in Python within a unified environment, with direct access to all the aforementioned atomistic methods and carefully designed data classes.

In addition to modeling qubits, QuantumATK can address problems of interest to quantum algorithms. While the timeline to general-purpose quantum computing remains debated—with expert surveys pointing to 2035–2040 for practical materials application—the Fermi-Hubbard model, a key testbed for early quantum hardware, can be studied classically in large lattices today using tight-binding and DFT parameterization. Recent quantum experiments have probed small Hubbard systems (16–20 qubits), observing metal-insulator transitions and magnetic ordering. QuantumATK can simulate significantly larger systems, providing correctness checks for quantum results and a performance baseline against which to measure quantum speedups. Moreover, classical improvements via GPU and MLFF may outpace early quantum hardware gains, ensuring that classical tools remain competitive as the quantum ecosystem matures.

Real-world results

A paper at the recent GOMACTech 2025 conference illustrates QuantumATK’s prowess in quantum device modeling. By simulating Al/AlOx/Al superconducting tunnel junctions with both stoichiometric and random variance in the atomic structures, engineers quantified how oxygen coordination effects can change the interface resistance and critical current by orders of magnitude. Such “what-if” atomistic methodology guides fabrication optimization in ways experiments alone cannot.

Fig. 2: Many-body probability density for an atomistic model (200,000 atoms) of a two-level system formed by two Si quantum dots, with a finite detuning potential applied.

The same paper reported the results of leveraging surface Green’s function techniques combined with DFT—unique to QuantumATK—to model topological insulator interfaces and estimate superconducting critical temperatures in MgB₂-type diborides with excellent agreement to experiment. This provides confidence that DFT can be used to screen material candidates combining a high critical temperature and the desired topological properties. Additionally, the paper showed that applying strain can enhance the critical temperature, providing novel avenues for realizing high-temperature superconductivity.

As a final example, the paper considered an atomistic representation of the most fundamental model of a qubit, the quantum-mechanical two-level system (TLS). This method can account directly for material-specific factors such as spin-orbit coupling strength, valley splitting due to strain, and detailed band structure effects like nonparabolicity or finite band width far from the Gamma point. Most interestingly, QuantumATK provides a fully atomistic calculation to describe the many-body interaction between two interacting electrons in a double quantum dot system, containing more than one million atoms.

Conclusion

The race to understand matter at its most fundamental level has never been more critical. As the industry grapples with challenges such as next-generation semiconductors and high-temperature superconductors, precise atomistic simulation is essential. Although quantum computing is still in its early stages, atomistic modeling and simulation is key for guiding qubit development. Engineers must be able to screen novel materials for desired properties and obtain a deeper understanding of the intrinsic physical behavior of these complex materials.

Synopsys QuantumATK stands at the intersection of classical computational power and emerging quantum technologies, with a combination of atomistic simulation and advanced first-principles methods. It is a comprehensive platform that not only addresses today’s materials science challenges but also positions engineers for the quantum computing revolution ahead. Hybrid workflows—using quantum hardware for strongly correlated subproblems and classical methods for the remainder—represent a promising path for future materials simulation.