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Even as we push forward into new frontiers of technological innovation, researchers are revisiting some of the most fundamental ideas in the history of computing.
Alan Turing began theorizing the potential capabilities of digital computers in the late 1930s, initially exploring computation and later the possibility of modeling natural processes. By the 1950s, he noted that simulating quantum phenomena, though theoretically possible, would demand resources far beyond practical limits — even with future advances.
These were the initial seeds of what we now call quantum computing. And the challenge of simulating quantum systems with classical computers eventually led to new explorations of whether it would be possible to create computers based on quantum mechanics itself.
For decades, these investigations were confined within the realms of theoretical physics and abstract mathematics — an ambitious idea explored mostly on chalkboards and in scholarly journals. But today, quantum computing R&D is rapidly shifting to a new area of focus: engineering.
Physics research continues, of course, but the questions are evolving. Rather than debating whetherquantum computing can outpace classical methods — it can, in principle — scientists and engineers are now focused on making it real: What does it take to build a viable quantum supercomputer?
Theoretical and applied physics alone cannot answer that question, and many practical aspects remain unsettled. What are the optimal materials and physical technologies? What architectures and fabrication methods are needed? And which algorithms and applications will unlock the most potential?
As researchers explore and validate ways to advance quantum computing from speculative science to practical breakthroughs, highly advanced simulation tools — such as those used for chip design — are playing a pivotal role in determining the answers.
Visualizing the atomistic behavior of materials helps advance quantum computing research
In many ways, the engineering behind quantum computing presents even more complex challenges than underlying physics. Generating a limited number of "qubits" — the basic units of information in quantum computing — in a lab is one thing. Building a large-scale, commercially viable quantum supercomputer is quite another.
A comprehensive design must be established. Resource requirements must be determined. The most valuable and feasible applications must be identified. And, ultimately, the toughest question of all must be answered: Will the value generated by the computer outweigh the immense costs of development, maintenance, and operation?
The latest insights were detailed in a recent preprint, "How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits, by Mohseni, et. al. 2024," which I helped co-author alongside Synopsys principal engineer John Sorebo and an extended group of research collaborators.
Today’s quantum computing research is driven by fundamental challenges: scaling up the number of qubits, ensuring their reliability, and improving the accuracy of the operations that link them together. The goal is to produce consistent and useful results across not just hundreds, but thousands or even millions of qubits.
The best "modalities" for achieving this are still up for debate. Superconducting circuits, silicon spins, trapped ions, and photonic systems are all being explored (and, in some cases, combined). Each modality brings its own unique hurdles for controlling and measuring qubits effectively.
Numerical simulation tools are essential in these investigations, providing critical insights into how different modalities can withstand noise and scale to accommodate more qubits. These tools include:
Synopsys QuantumATK delivers powerful atomic-scale modeling and simulations to accelerate quantum computing R&D
The design of qubit devices — along with their controls and interconnects — blends advanced engineering with quantum physics. Researchers must model phenomena ranging from electron confinement and tunneling in nanoscale materials to electromagnetic coupling across complex multilayer structures.
Many issues that are critical for conventional integrated circuit design and atomic-scale fabrication (such as edge roughness, material inhomogeneity, and phonon effects) must also be confronted when working with quantum devices, where even subtle variations can influence device reliability. Numerical simulation plays a crucial role at every stage, helping teams:
By accurately capturing both quantum-mechanical behavior and classical electromagnetic effects, simulation tools allow researchers to evaluate design alternatives before fabrication, shorten iteration cycles, and gain deeper insight into how devices operate under realistic conditions.
Advanced numerical simulation tools such as QuantumATK, HFSS, and RaptorQu are transforming how research groups approach computational modeling. Instead of relying on a patchwork of academic codes, teams can now leverage unified environments — with common data models and consistent interfaces — that support a variety of computational methods. These industry-grade platforms:
Simulation tools like QuantumATK, HFSS, and RaptorQu are not just advancing individual research projects — they are accelerating the entire field, enabling researchers to test new ideas and scale quantum architectures more efficiently than ever before. With Ansys now part of Synopsys, we are uniquely positioned to provide end-to-end solutions that address both the design and simulation needs of quantum computing R&D.
Despite the progress in quantum computing research, many teams still rely on disjointed, narrowly scoped open-source simulation software. These tools often require significant customization to support specific research needs and generally lack robust support for modern GPU clusters and machine learning-based simulation speedups. As a result, researchers and companies spend substantial effort adapting and maintaining fragmented workflows, which can limit the scale and impact of their numerical simulations.
In contrast, mature, fully supported commercial simulation software that integrates seamlessly with practical workflows and has been extensively validated in semiconductor manufacturing tasks offers a clear advantage. By leveraging such platforms, researchers are freed to focus on qubit device innovation rather than spending time on infrastructure challenges. This also enables the extension of numerical simulation to more complex and larger-scale problems, supporting rapid iteration and deeper insight.
To advance quantum computing from research to commercial reality, the quantum ecosystem needs reliable, comprehensive numerical simulation software — just as the semiconductor industry relies on established solutions from Synopsys today. Robust, scalable simulation platforms are essential not only for individual projects but for the growth and maturation of the entire quantum computing field.
"Successful repeatable tiles with superconducting qubits need to minimize crosstalk between wires, and candidate designs are easier to compare by numerical simulation than in lab experiments," said Qolab CTO John Martinis, who was recently recognized by the Royal Swedish Academy of Sciences for his groundbreaking work in quantum mechanics. "As part of our collaboration, Synopsys enhanced electromagnetic simulations to handle increasingly complex microwave circuit layouts operating near 0K temperature. Simulating future layouts optimized for quantum error-correcting codes will require scaling up performance using advanced numerical methods, machine learning, and multi-GPU clusters."