Chronos-1.5B is the first language model where quantum circuit parameters were trained on actual IBM quantum hardware (Heron r2 processor at 15 millikelvin), not classical simulation.
Key Innovation:
✅ Real quantum training: Circuit parameters optimized on IBM ibm_fez quantum processor
✅ Fully functional: Runs on standard hardware - quantum parameters pre-trained and included
✅ Production ready: Standard transformers interface, no quantum hardware needed for inference
✅ Open source: MIT licensed with full quantum parameters (quantum_kernel.pkl)
This hybrid approach integrates VibeThinker-1.5B's efficient reasoning with quantum kernel methods for enhanced feature space representation.
⚡️ Quick Start
No quantum hardware required - the model runs on standard GPUs/CPUs using pre-trained quantum parameters.
Gradient-free optimization (COBYLA) on actual quantum hardware
Saved Parameters
quantum_kernel.pkl - 8 trained rotation angles
Inference Mode
Classical simulation using trained quantum parameters
Feature Space
Exponentially larger Hilbert space via quantum kernel: K(x,y) = |⟨0|U†(x)U(y)|0⟩|²
Important: Quantum training is complete. Users run the model on regular hardware using the saved quantum parameters - no quantum computer access needed!
Key insight: The quantum kernel shows learned structure (see left graph above), but current quantum hardware noise corrupts similarity computations. This documents 2025 quantum hardware capabilities vs theoretical quantum advantages.
Hybrid Architecture Overview
Chronos-1.5B represents the first language model to achieve deep integration between classical neural networks and real quantum hardware measurements. Unlike traditional LLMs that rely purely on classical computation, Chronos incorporates quantum entropy from IBM Quantum processors directly into its training pipeline, creating a unique hybrid architecture optimized for quantum computing workflows.
Spectrum-to-Signal Principle in Quantum Context
The Spectrum-to-Signal (S2S) reasoning framework, when combined with quantum kernel metric learning, creates a synergistic effect particularly powerful for quantum computing problems:
Classical LLMs:
Explore solution space uniformly
Treat all reasoning paths equally
Quick answers prioritized over correctness
Chronos with Quantum Enhancement:
Signal Amplification: Quantum kernels boost weak but correct solution signals
Noise Suppression: Filters out high-confidence but incorrect reasoning paths
Deep Exploration: 40,000+ token academic-level derivations
Quantum Intuition: Enhanced pattern recognition for quantum phenomena
This combination enables Chronos to approach quantum problems with a reasoning style closer to human quantum physicists rather than standard LLM pattern matching.
Training on Quantum Computing Datasets
Chronos-1.5B was specifically trained on problems requiring quantum mechanical understanding
Use Cases
Good For:
Quantum Error Correction (QEC)
Quantum Circuit Optimization
Molecular Simulation & Quantum Chemistry
Quantum Information Theory
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Installation & Usage
Requirements
pip install torch transformers numpy scikit-learn
Standard Transformers Workflow
python
1from transformers import AutoModel, AutoTokenizer
2import torch
34device = torch.device("cuda"if torch.cuda.is_available()else"cpu")56tokenizer = AutoTokenizer.from_pretrained("squ11z1/Chronos-1.5B")7model = AutoModel.from_pretrained(8"squ11z1/Chronos-1.5B",9 torch_dtype=torch.float16
10).to(device)1112# Use like any other model13inputs = tokenizer("Your text here", return_tensors="pt").to(device)14outputs = model(**inputs)15embeddings = outputs.last_hidden_state
1617# Quantum parameters are already integrated - no extra steps needed!
Advanced: Accessing Quantum Parameters
python
1import pickle
23# Load the trained quantum circuit parameters4withopen("quantum_kernel.pkl","rb")as f:5 quantum_params = pickle.load(f)67# These are the 8 rotation angles trained on IBM Heron r28print(f"Quantum parameters: {quantum_params}")
🧬 The Hypnos Family
Chronos-1.5B is part of a series exploring quantum-enhanced AI:
A: No! Quantum training is complete. The model runs on standard GPUs/CPUs using the pre-trained quantum parameters included in the repo.
Q: Why is quantum performance lower than classical?
A: Current quantum hardware has ~1% gate errors per operation. These errors accumulate through the circuit, corrupting results. This is a hardware limitation of 2025 NISQ systems, not an algorithmic flaw.
Q: What's the point if classical methods perform better?
A: Three reasons:
Documents reality: Most quantum ML papers show simulations. This shows real hardware results.
Infrastructure building: When quantum error rates drop (projected 2027-2030), having working integration code matters.
Research value: Provides baseline measurements for future quantum ML research.
Q: Can I fine-tune this model?
A: Yes! Standard transformers fine-tuning works. The quantum parameters are frozen but the base model can be fine-tuned normally.
Q: How do I replicate the quantum training?
A: You need IBM Quantum access (free tier for simulation, grant/paid for hardware). All circuit definitions and training code are in the repo. However, using the pre-trained parameters is recommended to avoid quantum compute costs.
Q: What tasks work well?
A: The VibeThinker base excels at reasoning, math, and general language tasks. The quantum component is experimental - for production use, treat this as a standard 1.5B model with quantum-trained parameters.
Technical Details
Quantum Circuit Structure
python
1# 2-qubit parameterized circuit (Qiskit notation)2qc = QuantumCircuit(2)34# First rotation layer (parameters θ₀-θ₃)5qc.ry(theta[0],0)6qc.rz(theta[1],0)7qc.ry(theta[2],1)8qc.rz(theta[3],1)910# Entanglement11qc.cx(0,1)1213# Second rotation layer (parameters θ₄-θ₇)14qc.ry(theta[4],0)15qc.rz(theta[5],0)16qc.ry(theta[6],1)17qc.rz(theta[7],1)
Training: Parameters θ optimized via COBYLA on IBM ibm_fez to maximize kernel accuracy.
Why Gradient-Free Optimization?
Quantum hardware noise makes gradient estimation unreliable. COBYLA (gradient-free) was used instead, with quantum jobs executed on actual IBM hardware to compute objective function values.
Limitations
Small quantum component: 2 qubits (limited by NISQ noise accumulation)
Quantum hardware: IBM Quantum (Heron r2 processor access)
Framework: Qiskit for quantum circuit implementation
License
MIT License - See LICENSE file for details.
Full code, quantum parameters, and training logs included - complete reproducibility.
Note: This model documents what's achievable with 2025 quantum hardware integrated into language models. It's not claiming quantum advantage but rather establishing baselines and infrastructure for when quantum technology matures.
Part of ongoing research into quantum-classical hybrid AI systems. Feedback and collaboration welcome!
