Insider Brief
- Mind Success has launched two software platforms designed to accelerate quantum hardware development through AI-driven materials discovery and predictive hardware modeling.
- The company’s Quantum Materials Discovery Platform screens more than 150,000 candidate materials and automatically generates density functional theory simulations to evaluate promising options.
- Its Digital Twin Framework models environmental noise around qubits and generates predictive control signals intended to reduce decoherence across multiple quantum computing architectures.
PRESS RELEASE — Mind Success, a startup focused on predictive modeling for quantum technologies, has launched two software platforms aimed at accelerating quantum hardware development by helping researchers identify new materials and reduce environmental noise that affects qubit performance.
The company, based in Saudi Arabia and the United States, said the offerings include an AI-based Quantum Materials Discovery Platform and a Digital Twin Framework designed to model and optimize quantum hardware environments before physical devices are built or tested.
The Materials Discovery Platform uses artificial intelligence to screen more than 150,000 candidate quantum materials and automatically generate density functional theory (DFT) simulations to evaluate the most promising options. According to the company, the platform is intended to reduce the time required to identify materials suitable for applications including quantum processors, sensors and cryogenic components.
Mind Success said the software functions as a searchable database that combines large-scale materials screening with automated first-principles verification, reducing the need for early-stage laboratory experimentation.
The company’s second product, the Digital Twin Framework, is designed to integrate with existing quantum hardware systems. The software creates a predictive model of the environment surrounding qubits, allowing it to forecast sources of decoherence—environmental disturbances that cause qubits to lose their quantum state—and generate feedforward control signals intended to compensate for those effects.
According to Mind Success, the framework can model more than 1,000 environmental noise modes simultaneously and generate control signals within approximately 25 picoseconds. The company said the software is compatible with multiple quantum computing architectures, including superconducting qubits, semiconductor quantum dots, trapped ions and diamond nitrogen-vacancy centers.
Mind Success also said its simulation framework scales linearly with system size, rather than exhibiting the exponential computational growth associated with many conventional quantum system simulations. The company said this enables larger and more detailed environmental models while reducing computational requirements.
Together, the two products are intended to address separate stages of quantum hardware development. The materials platform focuses on identifying candidate materials before fabrication, while the digital twin software is designed to optimize the operating environment of quantum devices after materials have been selected.
The company said the combined approach aims to reduce the lengthy trial-and-error process often associated with developing quantum hardware, where identifying suitable materials and mitigating environmental noise can require years of experimental work.
Mind Success said both software platforms are available as platform-agnostic tools for quantum hardware developers working across multiple qubit technologies.
