Quantum Elements, a startup emerging from stealth this fall, is combining artificial intelligence and digital twin technology to accelerate the development of commercial, fault-tolerant quantum computing. Backed by QNDL Participations and the USC Viterbi School of Engineering, the company has launched its AI-native platform, Constellation, designed to overcome key bottlenecks in quantum development. The platform integrates AI agents, natural language processing, and advanced simulation tools to help organizations generate, run, and test quantum algorithms and applications. A core feature is its ability to create digital twins—high-fidelity virtual replicas—of quantum hardware systems. These digital twins allow developers to simulate quantum processors at scale, including the effects of noise, decoherence, and cross-talk between qubits, all without requiring access to expensive and scarce physical hardware. Izhar Medalsy, co-founder and CEO of Quantum Elements, explains that the lack of scalable simulation tools has long held back progress. “Hardware is scarce, expensive, and constantly evolving,” he says. “With different qubit modalities—superconducting, trapped ions, neutral atoms, photonics, and silicon spin—each behaves differently. You can’t use a one-size-fits-all approach. You need a digital twin to model these systems accurately and predict their behavior under real-world conditions.” Unlike classical computing, where a bit is always a 0 or 1, quantum systems are highly sensitive to their physical environment. Qubits are fragile, and their performance depends on factors like coherence time, gate fidelity, and connectivity. This means that software must be hardware-aware, and development must account for noise and error sources. Quantum Elements’ platform addresses this by simulating the full physical behavior of quantum systems, including error models, and using AI to optimize performance. The company claims its platform delivers a 20X improvement in productivity and 100X faster development speed. In one test, researchers used Constellation to evaluate the impact of qubit cross-talk on Shor’s algorithm—a critical test for quantum cryptography. Without simulation, such an experiment could take four to six months and cost over $100,000, involving physical fabrication, cooling cycles, and repeated testing. With Quantum Elements’ digital twin, the same test was completed in minutes, achieving 99% accuracy—what the company calls a world record. The platform allows users to build virtual quantum processors tailored to specific hardware, such as IBM’s QPU families, and to control parameters like connectivity, noise models, and error correction techniques such as surface codes. This enables rapid iteration and optimization before any physical deployment. Quantum Elements has also secured strong partnerships with major players in the quantum ecosystem, including IBM, AWS, Quantum Machines, Nvidia, and Rigetti, as well as academic institutions like USC and UCLA. The company’s leadership team includes Daniel Lidar, a leading quantum scientist and director of USC’s Center for Quantum Information Science and Technology, and Amir Yacoby, a Harvard professor and member of the National Academy of Sciences. Medalsy emphasizes that AI-driven simulation is not just helpful—it’s essential. “Think about aviation without flight simulators, or classical chip design without Cadence and Ansys,” he says. “AI and digital twins are the missing ingredients for quantum computing. This is the enabling technology that will make the field move from research to real-world applications at scale.”