Hypnos-i2-32B: Training Language Models with Multi Quantum Randomness

Michael

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What Makes Hypnos Different?

Traditional language models rely on pseudo-random number generators (PRNGs) during training—deterministic algorithms that merely simulate randomness. Hypnos-i2-32B takes a radically different approach: it learns from true quantum randomness extracted from fundamental physical processes.

The Three Quantum Sources

Hypnos-i2 doesn't use just one quantum source—it combines three orthogonal entropy streams:

MATTER: Superconducting Qubits

• Source: IBM Quantum Heron processors (133-qubit systems)
• Physics: Quantum decoherence in superconducting circuits
• Timescale: Microsecond-level fluctuations
• What it adds: Fast-frequency robustness in attention mechanisms

LIGHT: Vacuum Fluctuations

• Source: ANU Quantum Random Number Generator
• Physics: Zero-point energy fluctuations in electromagnetic vacuum
• Timescale: Nanosecond-level noise
• What it adds: High-frequency filtering and noise resistance

NUCLEUS: Radioactive Decay

• Source: Fourmilab HotBits (Strontium-90)
• Physics: Poissonian distribution of nuclear decay events
• Timescale: Fundamental unpredictability
• What it adds: Deep entropy patterns impossible to predict

How It Works: Input-Level Quantum Regularization

Unlike typical dropout or noise injection at the architecture level, Hypnos uses context-level quantum augmentation:

1. Before each training batch, unique entropy sequences are drawn from all three quantum sources
2. These sequences are embedded directly into the context window of training examples
3. The model learns to distinguish meaningful patterns (signal) from quantum noise
4. Attention heads develop inherent resistance to high-entropy perturbations

This creates an effect similar to training a human in a noisy environment—they learn to focus on what matters and ignore distractions.

Real-World Results

The quantum regularization isn't just a theoretical curiosity—it delivers measurable improvements:

Benchmark Performance

• ArenaHard: 94.9 (+1.1 over base Qwen3-32B)
• AIME 2024: 86.2 (+4.8)
• AIME 2025: 79.5 (+6.6)
• LiveBench: 64.1 (+14.8)
• Codeforces: 2045 Elo (+68)

Robustness Breakthrough

The most striking result: 2.3% hallucination rate—dramatically lower than:

• Qwen3-32B Base: 5.9%
• Llama-3.1-405B: 5.2%
• Deepseek-R1: 14.3%
• Llama 4 Maverick: 8.2%

Why This Matters

For Researchers

• First successful implementation of multi-QPU training in LLMs
• Demonstrates that quantum noise can serve as a regularization technique
• Opens new avenues for physics-inspired ML architectures

For Developers

• More reliable outputs in production environments
• Better resistance to adversarial attacks and prompt injection
• Reduced mode collapse and repetitive generation

Try the Hypnos Family

Also you can test smaller 8B version Hypnos-i1 with only superconducting entropy from IBM Quantum!

Start with Hypnos-i1-8B or go full quantum with Hypnos-i2-32B

Built by scientists, for scientists. Trained with the universe's randomness.