Quarkitech secures ₹2 crore pre-seed funding, ₹1.5 crore grant for data compression tech
Chennai-based deep-tech startup Quarkitech has raised ₹2 crore in a pre-seed funding round from Artha Access, a programme under Artha Venture Fund II, and Finvolve. The company, headquartered at the IIT Madras Research Park, will use the capital to advance its proprietary data compression technology for defence, aerospace, and other data-intensive applications.
In addition to the funding, Quarkitech has received a ₹1.5-crore grant from the IITM-CDOT Samgnya Technologies Foundation under the National Quantum Mission. The mission, approved by the Union Cabinet in 2023, aims to foster research and development in quantum technologies in India.
The company focuses on compressing sensor data at the point of capture, before it is stored, processed, or transmitted. As India expands its drone, satellite, and defence operations, onboard sensors such as radar, LiDAR, and hyperspectral imagers generate more data than platforms can continuously store or transmit, particularly in remote areas without a live connection to a ground station. This creates a trade-off between data volume, quality, transmission speed, and cost for operators.
Quarkitech's algorithms compress sensor data by 10 to 100 times, depending on the sensor type, while retaining mission-relevant information. This allows platforms to move more usable data within the same bandwidth and power constraints, without requiring additional hardware or communication infrastructure. The product is a software library that runs on a platform's existing onboard compute, with separate tuning for radar, LiDAR, hyperspectral, and electro-optical streams. It can be integrated into UAV downlinks, satellite payloads, or ground-segment archives.
The technology is based on tensor networks, mathematical frameworks originally developed to model complex quantum systems. By using these frameworks, Quarkitech identifies and removes redundancy in high-volume sensor data. Sanyam Parashar, CEO and Founder of Quarkitech, said the algorithms are inspired by the mathematical framework used in quantum many-body physics. This enables high-dimensional data to be represented in extremely compressed form, resulting in faster and cheaper computation, and 10 to 100 times more efficient data transmission from high-value sensors.
Shashikant Singh Kunwar, Co-founder and CTO of Quarkitech, said the company is applying mathematical frameworks used to model complex quantum systems to untangle massive data bottlenecks in modern defence and aerospace. He said the company is translating the theoretical power of tensor networks into an immediate, real-world tactical advantage.
Artha invested through its Artha Access programme, which co-invests alongside academic incubators and accelerators that have already backed a company, on terms set by the partner institution. Anirudh A. Damani, Director of Artha Group, said sensor redundancy is a physics problem before it is a software problem, and Quarkitech is attacking it at the point of capture instead of after the data is already stuck onboard.
In laboratory testing, Quarkitech reports a 26 times reduction in data volume while retaining 98% of mission-critical information, measured on drone-captured images. The company is now working to validate these results under real-world operating conditions, including moving platforms and environments with heat, vibration, limited onboard power, and intermittent connectivity.
The target users are operators whose sensors generate more data than their links can handle, including UAV and surveillance platforms, satellite earth-observation payloads, radar and LiDAR mapping fleets, and ground segments that store and process data. The compression approach also extends to other bandwidth-constrained environments across communications and infrastructure.
Ashish Bhatia, Co-founder of Finvolve, said classical computing has scaled remarkably but is now hitting hard limits on cost, energy, and latency. He noted that many problems India needs solved sit exactly at that intersection, making Quarkitech's work relevant beyond the immediate funding.
