Solve complex materials-science problems with the new Fire Opal quantum-dynamics simulator Quantum computers allow us to directly model the quantum mechanical interactions that govern chemistry and materials, unlike classical simulators, which are forced to rely on approximations that lose precision as the problem grows in complexity. As a result, research scientists are embracing quantum computers as powerful new tools with the potential to accelerate the development of high-performance electric vehicle batteries and energy-efficient industrial catalysts, while opening access to new regimes of energy transfer and materials science that the community has not been able to explore before. This opportunity is impeded by the reality that quantum computers face a real bottleneck—errors which accumulate and cause algorithms to fail. This is an incredibly significant challenge that has placed a limit on how useful quantum computers could be—and it’s exactly what Q-CTRL solves. Delivering on our mission to make quantum technology useful, we recently broke through this barrier thanks to our performance-management software, Fire Opal. In a breakthrough demonstration on the IBM Quantum Platform, Q-CTRL achieved practical quantum advantage in quantum computing, solving a real problem in materials discovery 3,000x faster than the best available alternative classical software. With this demonstration, we have the first real evidence that quantum computers can be used to solve a real problem and deliver a better outcome than the existing conventional alternatives. Learn more about the details in the technical manuscript (updated July 2026) and our technical blog. Roughly one-third of all global supercomputer time is currently consumed by chemistry and materials science simulations. From designing room-temperature superconductors to uncovering carbon-neutral materials, the energy and industrial sectors face massive classical computational bottlenecks. And now quantum computers can help! The challenge Most quantum-dynamics simulations involve understanding how an interacting collection of particles like electrons evolve in time