AI & ML
ZenKO: Guided and Iterative Quantum-Classical Method for Molecular Electronic Structure using Sample-Based Quantum Diagonalization
arXiv cs.DC0 views
arXiv:2610.41146v1 Announce Type: cross Abstract: The electronic structure of molecules is foundational for scientific advancement, however determining it remains challenging due to the growth of the molecular Hilbert space with system size. Recent approaches address this by using quantum circuits to sample candidate configurations and sample-based quantum diagonalization (SQD) to compute the ground state. We propose ZenKO, a novel gradient-based, iterative SQD method that employs a hybrid quantum-classical approach. ZenKO decomposes configuration selection into discovery, validation, and refinement, enabling targeted subspace expansion and guided exploration. Our evaluation demonstrates that, across $H_2O$, $H_{10}$, and $N_2$ molecules, ZenKO improves ground-state accuracy compared to prior iterative methods, while significantly reducing classical cost.
Source:https://arxiv.org/abs/2610.41146PreviousAn Incentive-Aligned Trust-Centric Coordination Layer for Heterogeneous Blockchain InteroperabilityNextARK: Avoiding Routing Collisions for KV Cache Transfer in Disaggregated LLM Inference
Related posts
A small business running 8 projects on Claude Code for 30 days: rules ignored, projects crossing into each other, and what finally helped
0dev.to AI
L'IA trouve les peptides, la VR les révèle : pourquoi Antigravity change la donne pour la découverte de médicaments
0dev.to AI
Why Tesseract Fails on Cursive Handwriting (And How We Built a Two-Tier Vision Pipeline in Node.js)
0dev.to AI
OFX looks like XML. It isn't, and QuickBooks will reject it
0dev.to WebDev
Before You Buy GEO Monitoring Software, Build a 50-Line Baseline
0dev.to Python
Building Self-Improving AI Models Without Blinding Yourself to Failure
0dev.to Python