Sreekuttan Lalimol Sukesh

How Global Businesses Can Start with Quantum—Without the Headache?

Sreekuttan Lalimol Sukesh Co-founder and CEO of Bloq Quantum

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About Sreekuttan Lalimol Sukesh

Sreekuttan Lalimol Sukesh is the Co-founder and CEO of Bloq Quantum, a deep-tech startup accelerating enterprise adoption of quantum computing through a user-friendly low-code platform. With a background in Physics from IISER Pune and hands-on experience in building quantum products at leading organizations like The Quantum Insider and Qkrishi, he brings a unique blend of technical depth and product strategy.

He has authored peer-reviewed research papers, filed patents, and led his teams to win multiple international quantum competitions. At Bloq, he focuses on bridging the gap between cutting-edge quantum tech and real-world business needs—helping companies explore quantum without the complexity.

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Accelerate quantum projects using our drag-and-drop interface and pre-built libraries, designed for users of all levels. Our intuitive low-code environment simplifies your quantum computing journey.

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Frequently Asked Questions

Who is Sreekuttan Lalimol Sukesh?
Sreekuttan Lalimol Sukesh is the Co-founder and CEO of Bloq Quantum, a deep-tech startup accelerating enterprise adoption of quantum computing through a user-friendly low-code platform. With a background in Physics from IISER Pune and hands-on experience in building quantum products at leading.
What is the lecture "How Global Businesses Can Start with Quantum—Without the Headache?"?
This presentation by Sreekuttan Lalimol Sukesh at the Quantum Security Defence World Symposium covers key concepts, challenges, and developments in quantum computing, quantum machine learning.
What is quantum computing?
Quantum computing uses superposition and entanglement to perform certain computations substantially faster than classical computers. Key applications include optimisation, simulation, and cryptanalysis.
What is quantum machine learning?
Quantum machine learning explores how quantum computing can accelerate machine learning algorithms. Most practical applications remain in the near-term hybrid quantum-classical category.

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