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This note covers checkpoint 6ae6d8d081cfb6eefda299d2335554168082fd39 with native context 256.
Originally reported. Preliminary examples and approximate accuracy by circuit type. The original card gives approximate results by circuit family. The v2 comparison reports 67.2% syntax and 23.4% semantic pass@1 for this model.
On the original long wording, all 384 inputs exceeded the 256-token window. That arm is ineligible for ranking.
Evidence gap. No fixed case list or item-level results accompany the original examples. The 354M comparison gives different v1 figures, 68.2% syntax and 13.8% semantic pass@1.
Measured on the compact development suite. The checkpoint returned 23/384 syntax-valid answers, 4/384 answers with the requested structure, and 0/384 fully correct answers. It passed 0/257 on the fixed panel with verified short reference answers.
The compact suite uses one greedy raw answer per task, the native prompt transport and context, and task-specific numerical checks for the full circuit contract. Its 384 problems are synthetic and have been used for development.
The benchmark manifest SHA-256 is 320535d4ff74d9f07db024c1e86c0e8208a8fd93c294f47332cb9c38fadf9243. Evaluation settings, context stops and full panel counts
QuantumGPT-124M is a GPT-2 architecture language model trained specifically for generating quantum circuits in OpenQASM 2.0 format from natural language descriptions.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("merileijona/quantumgpt-124m")tokenizer = AutoTokenizer.from_pretrained("merileijona/quantumgpt-124m") prompt = "<|user|>Create a Bell state with two qubits<|end|>\n<|assistant|>"inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id,) text = tokenizer.decode(outputs[0], skip_special_tokens=False)qasm = text.split("<|end|>", 1)[0]print(qasm)Expected Output:
qasm
OPENQASM 2.0;include "qelib1.inc";qreg q[2];creg c[2];h q[0];cx q[0],q[1];measure q -> c;The model was trained on 92 distinct categories of quantum circuits:
Single-Qubit Operations (14 categories):
Two-Qubit Operations (11 categories):
Three-Qubit Operations (6 categories):
Quantum Algorithms (15 categories):
Variational Circuits (15 categories):
Plus: Arithmetic circuits, error correction codes, special states, and more.
The model was trained using explicit conversation delimiters:
text
<|user|>{description}<|end|><|assistant|>{qasm}<|end|>These markers are literal text tokens, not special tokenizer tokens.
Generation should begin with:
text
<|assistant|>and stop at the first occurrence of:
text
<|end|>If <|assistant|> is omitted, generation quality may degrade.
| Circuit Type | Accuracy | Notes |
|---|---|---|
| Basic gates (H, X, Y, Z) | 95-100% | Near-perfect on simple gates |
| 2-qubit entanglement | 90-95% | Strong on Bell states, CNOT patterns |
| 3-qubit states (GHZ, W) | 85-90% | Good semantic understanding |
| Arithmetic circuits | 75-85% | Moderate accuracy on adders/incrementers |
| Complex algorithms | 70-80% | Struggles with QFT, Grover's |
| 4+ qubit circuits | 60-70% | Limited training data for large systems |
Perfect Generation:
python
Prompt: "Apply Hadamard gate to single qubit"Output: ✅ OPENQASM 2.0; ... h q[0]; measure q[0] -> c[0]; Prompt: "Create Bell state with two qubits"Output: ✅ OPENQASM 2.0; ... h q[0]; cx q[0],q[1]; measure q -> c; Prompt: "Generate GHZ state with three qubits"Output: ✅ OPENQASM 2.0; ... h q[0]; cx q[0],q[1]; cx q[0],q[2]; measure q -> c;Qubit Count: Optimized for 1-3 qubit circuits. Performance degrades for 4+ qubits due to limited training data.
Complex Algorithms: May generate syntactically valid but semantically incorrect circuits for advanced algorithms (e.g., full quantum teleportation, complex QFT implementations).
Parametric Gates: Limited support for gates with specific angle parameters. May substitute similar gates (e.g., RY → Y, S → T).
No Execution Guarantee: Generated circuits are syntactically valid QASM 2.0 but not guaranteed to execute correctly on quantum hardware without validation.
✅ Educational Tools: Generate example circuits for quantum computing education ✅ Rapid Prototyping: Quick circuit templates for experimentation ✅ Code Completion: Assist developers writing QASM code ✅ Benchmarking: Generate diverse circuits for compiler/simulator testing
❌ Production Quantum Computing: Not suitable for critical quantum applications ❌ Large-Scale Circuits: Limited to small qubit counts (1-4 qubits) ❌ Hardware Deployment: Requires validation before running on actual quantum hardware
The model was trained on a custom dataset of 8,129 quantum circuits:
If you use this model in your research, please cite:
bibtex
@misc{quantumgpt124m, author = {Merilehto, Juhani}, title = {QuantumGPT-124M: Quantum Circuit Generation with GPT-2}, year = {2026}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/merileijona/quantumgpt-124m}}, note = {GPT-2 model trained on 8,129 quantum circuits for OpenQASM 2.0 generation}}Juhani Merilehto
This model is released under the MIT License.
Model Version: 1.0
Release Date: February 2026
Last Updated: February 27, 2026
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("merileijona/quantumgpt-124m")tokenizer = AutoTokenizer.from_pretrained("merileijona/quantumgpt-124m") prompt = "<|user|>Create a Bell state with two qubits<|end|>\n<|assistant|>"inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id,) text = tokenizer.decode(outputs[0], skip_special_tokens=False)qasm = text.split("<|end|>", 1)[0]print(qasm)OPENQASM 2.0;include "qelib1.inc";qreg q[2];creg c[2];h q[0];cx q[0],q[1];measure q -> c;Prompt: "Apply Hadamard gate to single qubit"Output: ✅ OPENQASM 2.0; ... h q[0]; measure q[0] -> c[0]; Prompt: "Create Bell state with two qubits"Output: ✅ OPENQASM 2.0; ... h q[0]; cx q[0],q[1]; measure q -> c; Prompt: "Generate GHZ state with three qubits"Output: ✅ OPENQASM 2.0; ... h q[0]; cx q[0],q[1]; cx q[0],q[2]; measure q -> c;@misc{quantumgpt124m, author = {Merilehto, Juhani}, title = {QuantumGPT-124M: Quantum Circuit Generation with GPT-2}, year = {2026}, publisher = {HuggingFace}, howpublished = {\url{https://huggingface.co/merileijona/quantumgpt-124m}}, note = {GPT-2 model trained on 8,129 quantum circuits for OpenQASM 2.0 generation}}