WQT50M

WestQuant Transformer 50M — Quantum Representation Scheduler

AI schedules. Deterministic mathematics executes. Independent verification certifies.

WQT50M is a 50M-parameter Transformer that ranks quantum transformations. Trained on real Qiskit transpilation outputs — not synthetic data. It predicts which transformation is most promising given a circuit, backend, and optimization objective.

WQT50M is a proof-of-concept that a 50M-parameter Transformer can learn structured quantum optimization preferences from real compiler outputs. Researchers building better compiler-optimization models can use this as a baseline.

What It Does — 3 Tested Examples

Example 1: 62% Gate Reduction (Random Circuit, Fidelity-Focused)

Problem:  8-qubit random circuit (depth 30) on grid topology
          Naive transpilation → cost 1231.2 (fidelity-focused objective)

          WQT50M schedules ZX_SIMPLIFY
          → cost 467.4 (62.0% reduction)

          Random selection → cost 869.6 (29.4% reduction)
          Oracle (exhaustive) → cost 467.4 (62.0%)
          WQT50M matches oracle ✓

Example 2: Objective-Aware Scheduling (QAOA, Linear Backend)

The same QAOA circuit gets different best actions depending on the objective:

QAOA-6q-p2 on linear backend:

  Objective            WQT50M picks
  ─────────────────    ─────────────────
  balanced             → CANCEL_GATES
  2q_focused           → CANCEL_GATES
  depth_focused        → NATIVE_GATESET
  fidelity_focused     → FUSE_ROTATIONS
  time_focused         → MERGE_ADJACENT

  4 different actions for 5 objectives ✓

Example 3: Calibrated Value Prediction

WQT50M predicts costs in the real magnitude range (not a narrow band):

State:    <DOMAIN:circuit_optimization> <LEVEL:CIRCUIT> <N_QUBITS:8> ...
          <BACKEND:SUPERCONDUCTING> <TOPO:grid> <T1:180us> <T2:90us>
          <OBJ_TYPE:fidelity_focused>

Action:   ZX_SIMPLIFY
          Predicted cost: 467.4
          Actual cost:    467.4
          Error:          0.0%

Calibration ratio: 0.999 (predicted range matches actual range)
Spearman correlation: 0.981

Quick Start

pip install transformers torch

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("WestQuantStudio/WQT50M")
tokenizer = AutoTokenizer.from_pretrained("WestQuantStudio/WQT50M")
device = "mps" if torch.backends.mps.is_available() else "cpu"
model = model.to(device).eval()

# Predict cost for a state-action pair
state = ("<DOMAIN:circuit_optimization> <LEVEL:CIRCUIT> "
         "<N_QUBITS:8> <ENTANGLEMENT:0.5000> "
         "<RES:n_q=8 D=30 G1=100 G2=20 T=120 M=0 A=0.2000 E=0.0050 C=0.5000> "
         "<BACKEND:SUPERCONDUCTING> <TOPO:grid> "
         "<T1:180us> <T2:90us> <READOUT_ERR:0.0120> "
         "<OBJ_TYPE:fidelity_focused> <STEP:0/5>")

text = f"<PREDICT> {state} <ACTION> ZX_SIMPLIFY <COST> "
ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(device)
with torch.no_grad():
    for _ in range(8):
        out = model(input_ids=ids)
        nxt = out.logits[0, -1].argmax().unsqueeze(0).unsqueeze(0)
        ids = torch.cat([ids, nxt], dim=1)
        if nxt.item() == tokenizer.eos_token_id:
            break

print(tokenizer.decode(ids[0].tolist()).split("<COST>")[-1].strip())
# → predicted cost in real magnitude range

What WQT50M Predicts

Model

Standard HuggingFace LlamaForCausalLM. Compatible with AutoModelForCausalLM, AutoTokenizer, and SafeTensors.

Validation Results

450 tests: 15 circuits × 5 objectives × 6 backends

By objective

By circuit type

Biggest improvements

Production Gap Coverage

WQT50M addresses 6 of the 9 production gaps identified in WQT20M-Beta:

Remaining gaps for future models

• Legality accuracy: 79.2% (target >90%) — needs a dedicated classification head
• Multi-step trajectory planning: Data generated but not yet evaluated end-to-end
• Active learning feedback loop: Not yet implemented
• Production deployment as Qiskit plugin: Not yet implemented

Training Data

WQT50M is trained on real Qiskit transpilation outputs:

Coverage

15 quantum optimization domains:

Limitations

• Legality: 79.2% — Below the 90% target. The model sometimes marks illegal actions as valid.
• Not a circuit compiler — The model works on structured state representations, not raw Qiskit/TKET circuits. Plugins translate between the model's representation and real frameworks.
• Single-step evaluation — While trajectory data was generated, the model has not been evaluated on multi-step planning end-to-end.
• No active learning — The model is static; it does not improve from user compilations.

License

Apache 2.0

Citation

@misc{westquant2026wqt50m,
  title={WQT50M: A 50M-Parameter Transformer for Quantum Representation Scheduling},
  author={Vesterlund, David},
  year={2026},
  url={https://huggingface.co/WestQuantStudio/WQT50M},
  note={WestQuant Open Source Project}
}