Q-Route-70B

⚛️ Q-Route-70B: Quantum Hardware Compiler Model Card

Q-Route-70B is a domain-adapted frontier AI compiler model fine-tuned on Llama-3.3-70B-Instruct via the Adaption Labs AutoScientist platform. It compiles abstract OpenQASM 2.0 quantum circuits to strictly adhere to physical target QPU connectivity graphs (Line, Ring, Star, Grid, and IBM Heavy-Hex hardware topologies).

Model Training

A LORA adapter for meta-llama/Llama-3.3-70B-Instruct-Reference. This model was trained with SFT using Adaption's AutoScientist on the quantum_circuit_routing dataset.

AutoScientist Config

{
  "job_id": "72858b84-6e51-44fa-b887-56fd54517a8a",
  "training_experiment_id": "dcc1dae2-3122-4888-99a8-3c296afd1020",
  "original_model_name": "meta-llama/Llama-3.3-70B-Instruct-Reference",
  "trained_model_name": "adaption_quantum_circuit_routing",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 64,
    "n_evals": 5,
    "n_epochs": 2,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.05,
    "learning_rate": 0.0001,
    "max_grad_norm": 1,
    "base_model_size": "70B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "all-linear"
  }
}

Training Data

The model was trained on 14,583 rows of adapted data with the following domain distribution: code (79%), science (21%), technology (0%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

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Model Details

• Model Name: Q-Route-70B
• Model Type: Fine-Tuned PEFT/LoRA Causal Language Model
• Base Model: meta-llama/Llama-3.3-70B-Instruct-Reference (70B parameters)
• Language/Domain: OpenQASM 2.0 Spatial Graph Compiler Code

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Performance & Benchmark Summary (Q-Route-100 Eval)

Evaluated on the Q-Route-100 Benchmark (100 novel circuit/topology pairs):

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How to Get Started with the Model

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "meta-llama/Meta-Llama-3.3-70B-Instruct-Reference"
adapter_id = "jay2219/Q-Route-70B"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

system_prompt = "You are Q-Route, an enterprise-grade deterministic Quantum Hardware Compiler. Translate abstract OpenQASM 2.0 to hardware-compliant QASM."
user_prompt = """### Physical Hardware Specification:
- Target Topology: Star-15
- Valid Physical Edges (Coupling Map): [[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [0, 13], [0, 14]]

### Abstract Input OpenQASM 2.0:
```qasm
OPENQASM 2.0;
include "qelib1.inc";
qreg q[15];
creg c[15];
cx q[1], q[5];
measure q -> c;

Compiled Hardware-Compliant OpenQASM 2.0:

prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=False)

compiled_qasm = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(compiled_qasm)

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Training Hyperparameters

• LoRA Rank ($r$): 64
• LoRA Alpha ($\alpha$): 128
• Dropout: 0.0
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Dataset: ~15,000 Qiskit ground-truth pairs across 5 hardware topology families.
• Platform: Adaption Labs AutoScientist platform.