Vishal Chatrath, CEO and Co-Founder of QuantrolOx

The Quantrolux platform turns the painstaking, PhD‑level bring‑up of superconducting qubits into a push‑button operation. By automatically running the full suite of single‑ and two‑qubit experiments—Rabi, Ramsey, entanglement checks, randomized benchmarking—the software reduces a week‑long manual process to under thirty minutes. Vishal Chatrath likens this leap to the birth of electronic‑design‑automation (EDA) tools that made semiconductor mass production possible. As quantum chips move from laboratory prototypes toward commercial volumes, such automation becomes the cornerstone of industrial‑scale quantum hardware, delivering repeatable quality and accelerating the path to scalable processors.

Quantrolux distinguishes itself with an open‑architecture SDK that lets customers dissect, modify, and re‑assemble the automation pipeline. This flexibility addresses a key market demand: engineers need to run individual experiments or integrate proprietary algorithms without being locked into a single vendor stack. The same framework also supports real‑time calibration, continuously tracking qubit drift and feeding corrected microwave pulses back to the device. By exposing APIs for quantum error‑correction software, Quantrolux bridges the gap between physical‑qubit characterization and logical‑qubit performance, enabling tighter coupling between control electronics, CPUs, and QPUs across diverse hardware platforms.

The company’s three‑year journey illustrates both the promise and the turbulence of deep‑tech entrepreneurship. Early skepticism over open architecture slowed fundraising, yet a successful A1 round in 2025 unlocked rapid customer adoption—rising from six‑figure revenues in 2024 to seven‑figure sales by early 2027. Scaling demands thousands of chip characterizations per generation, a workload Quantrolux’s automation can sustain. Looking ahead, the team plans hardware‑software bundles, support for microwave and laser‑based qubits, and broader integration with quantum‑error‑correction frameworks. Their model shows how engineering‑first automation can lower quantum hardware costs and accelerate the transition from research labs to commercial quantum computing.