Oq

Oqtopus: Open-Source Full-Stack Quantum Cloud Platform

Hacker News

Oqtopus: Open-Source Full-Stack Quantum Cloud Platform

We're excited to announce the open source release of a comprehensive quantum cloud platform — OQTOPUS. This project delivers an end-to-end solution for quantum computing by integrating both cloud and edge-server components, bridging the gap between user-facing quantum software and the underlying system operations. Key Highlights: - Cloud Layer: Provides RESTful APIs based on OpenAPI and FastAPI, along with robust user authentication and job scheduling. Infrastructure is automated through an Infrastructure as Code (IaC) approach using Terraform. - Edge-Server Component: Manages critical preprocessing tasks such as quantum circuit transpilation (via a versatile multi-format transpiler framework), and handles sophisticated post-processing like readout error mitigation to improve result accuracy. - End-to-End Integration: Seamlessly processes user-submitted quantum circuits—from job queuing, through circuit transformation and device execution, to final result correction—making quantum computing more accessible and efficient. By releasing OQTOPUS as open source, the aim is to lower the entry barrier for quantum system software development and foster a vibrant community of contributors and innovators in quantum computing. The full project details and source code are available on GitHub. Contributions and feedback are welcome to drive the next generation of quantum cloud solutions. GitHub: https://github.com/oqtopus-team/ Website: https://oqtopus-team.github.io/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks, using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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