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Quantumopt – GNN-based quantum circuit compiler (34% gate reduction)

Hacker News

Quantumopt – GNN-based quantum circuit compiler (34% gate reduction)

I'm a CS student with no prior quantum computing background who built this in about a week. quantumopt uses a Graph Attention Network trained on 10,240 quantum circuits to predict optimization potential, then passes to Qiskit's transpiler for hardware-specific compilation targeting IBM Brisbane. Results on 41 real QASMbench circuits: - 34% average gate reduction - 28% average depth reduction - 0 circuits made worse - 82% GNN prediction accuracy Happy to answer questions about the GNN architecture, training pipeline, or quantum compilation approach.

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Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
16%16% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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