QELM Research Notes

Research notes for QELM, a hybrid quantum/classical language-modeling framework built around token encoding, parameterized circuits, quantum attention-like blocks, measurement, and classical post-processing.

Research brief

Token encoding, parameterized circuits, quantum attention-like blocks, measurement, and classical post-processing.

Problem: Quantum language-modeling experiments need clear architecture and limitation notes.
Hypothesis: A compact hybrid quantum/classical NLP framework can be explored openly with Qiskit and Python.
Current boundary: Benchmark claims should be shown only when data is published.

Quantum language modeling

Study Overview

QELM explores whether language-model components can be expressed through a hybrid quantum/classical path: token encoding, circuits, attention-like blocks, measurement, and post-processing.

Current work focuses on architecture, reproducibility, backend behavior, and controlled comparison.

QELM also acts as a flagship project inside R&D BioTech Alaska because it connects open-source software, quantum experimentation, and the lab's broader research umbrella.

Primary stack: Python, Qiskit, OpenQASM, Aer, NumPy, and local chat UI work.
Core question: what parts of NLP can be usefully represented with quantum-inspired or quantum-backed components?
Boundary: benchmark results must be published before performance claims.

Variables

Input representation

Token encoding

Token maps decide how language is compressed before entering the quantum/classical path.

Pipeline Lens

Choose the first QELM run to make reproducible.

Input clarity

Token-map experiments show how representation choices affect the rest of the pipeline.

Study record

Build

Hybrid architecture

QELM connects token encoding, circuits, measurement, and classical post-processing.

Study

Benchmark discipline

The next stage is reproducible comparisons with clear baselines and settings.

Bridge

Brain and local AI

QELM research can feed future local learning systems without skipping validation.

Next experiments

01
Publish a small benchmark notebook with fixed data and baseline settings.
02
Separate Aer simulator records from IBM backend records.
03
Compare token-map and circuit-depth variants.
04
Add model cards for successful and failed runs.

Related research area All studies