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researcher

Simon See

5 papers hereh-index 133 citations8 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • quant-ph3
  • cs.LG1
  • math.NA1
same name
  • Simon See — 15 papers, h 8
  • Simon See — 14 papers, h 6
  • Simon See — 13 papers, h 5
  • Simon See — 4 papers, h 3
  • Simon See — 4 papers, h 5
  • Simon See — 3 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

works on
fast-weight programming 1matrix gating 1quantum dynamics forecasting 1quantum-inspired networks 1sequence models 1

From the 1 of 5 linked papers with an AI index.

collaborators
Showing quant-phShow all

3 papers · 1 filter

quant-ph2026

Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting

Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14

The paper proposes Complementary Matrix Gating, a coordinate‑wise gating scheme for fast‑weight programmers built on quantum‑inspired Kolmogorov‑Arnold networks, and shows it impro…

quant-ph2026

Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs

Rushat Rai, Pei-Jie Chang, Doan Viet Nguyen +8

Optimizing recessed-gate AlGaN/GaN MIS-HEMTs requires accurate multi-characteristic models, but experimental semiconductor datasets remain costly and encode process-induced variabi…

quant-ph2025

Validating Large-Scale Quantum Machine Learning: Efficient Simulation of Quantum Support Vector Machines Using Tensor Networks

Kuan-Cheng Chen, Tai-Yue Li, Yun-Yuan Wang +6

We present an efficient tensor-network-based approach for simulating large-scale quantum circuits, demonstrated using Quantum Support Vector Machines (QSVMs). Our method effectivel…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.