A compilation of quantum-native solver techniques that can be used to map and run on a quantum computer. Compiled by Onri.
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Updated
Apr 4, 2026 - Jupyter Notebook
A compilation of quantum-native solver techniques that can be used to map and run on a quantum computer. Compiled by Onri.
Controlled interpolation between classical and quantum learning. Binarized Quantum Neural Network benchmark harness for systematic sweeping a quantumness parameter to map learning phase transitions.
An extensible register of student-scale local practical quantum advantage projects
Reproducibility artifact for 'Sharp Target-Domain Certificates for Quantum-Kernel Advantage under Distribution Shift': sharp finite-batch certificates, controlled quantum-vs-classical kernel benchmark (EMBER, UNSW-NB15, ToN-IoT, TableShift), frozen specifications, prediction locks, manuscript sources. Zenodo v1.1.6.
Zero-overhead quantum error suppression via tetrahedral deficit correction. Hardware-validated +16.9% fidelity. Works with Amazon Braket & Qiskit.
Quantum Neural Network (QNN) Comparative Study on MNIST Dataset
PennyLane reproduction of 'On verifiable quantum advantage with peaked circuit sampling' (Aaronson & Zhang, arXiv:2404.14493), Section 3 — peakedness 0.20 reproduced at n=12
Interaction-graph decomposition for solving peaked quantum circuits. MIT iQuHACK 2026.
Deep research audit of Huang et al. (2025) 'The vast world of quantum advantage' (arXiv:2508.05720) — engagement with QNFO's quantum computing critique
Do we really need quantum computing? A runnable essay: one matrix-free dial — the effective rank Φ₁ — flags which quantum speedups survive a classical attack, and which were never quantum at all.
⚛️ Explore quantum-native solvers that enable efficient mapping and execution on quantum computers for advanced computational techniques.
A tool for simulating 1D peaked circuits with exact statevectors and an MPS with swappable SVD truncation rules. Benchmarks them against top-χ on chi_break.
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