QpiAI Open-Sources Quantum SDK for 8- and 25-Qubit Computer Access

QpiAI has released its Quantum SDK as open-source software, giving developers a pathway to run algorithms on the company’s 8-qubit and 25-qubit quantum computers via QpiAI-QCloud. The Python-based toolkit includes both local state-vector and density matrix simulators, allowing for algorithm prototyping and validation before utilizing actual quantum hardware. This move is designed to expand access to quantum software development for a global audience, from researchers and startups to enterprise innovation teams. “Quantum computing will scale only when developers can experiment, learn, and deploy without friction,” says Lakshya Priyadarshi, VP, Quantum Platforms & Solutions at QpiAI.

QpiAI Quantum SDK Enables Algorithm Development and Hardware Access

QpiAI has empowered developers with access to quantum computing resources through the open-source release of its Quantum SDK, providing a pathway to algorithm prototyping and direct hardware execution. The software, available at https://github.com/qpiai/quantum-sdk, represents a deliberate effort to democratize quantum software development, extending its reach beyond established research institutions to a global network of developers, startups, and enterprise innovation teams. QpiAI intends the SDK to serve as a foundation for building specialized quantum solutions across diverse fields including finance, logistics, materials science, and artificial intelligence. The Python-based SDK streamlines the development process with features designed for both novice and experienced quantum programmers. The SDK is engineered to support AI-assisted and agentic development workflows, enabling faster prototyping and implementation of quantum applications. QpiAI is actively targeting educational institutions, offering a ready-made foundation for quantum computing coursework, research projects, and developer training programs, with early adopters eligible for preferential commercial terms through the QpiAI Academic & Innovation Network.

The release of the SDK is strategically aligned with India’s growing prominence in quantum technologies and its National Quantum Mission. “India is entering a defining decade for quantum technologies, and open-source software will be critical to building the talent, research, and innovation base that national leadership requires,” said Dr. Nagendra Nagaraja, Founder and CEO, QpiAI. “India, a leader in software for the last decade and globally known for its software capability, will aggressively open-source software and enable innovation in quantum computing across the globe.” Lakshya Priyadarshi, VP, Quantum Platforms & Solutions, QpiAI, emphasized the importance of accessibility, stating, “Quantum computing will scale only when developers can experiment, learn, and deploy without friction.” By prioritizing a robust software foundation, QpiAI aims to foster a diverse and globally competitive quantum application ecosystem.

Quantum computing will scale only when developers can experiment, learn, and deploy without friction.

Lakshya Priyadarshi, VP – Quantum Platforms & Solutions, QpiAI
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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