Quantum engineering of nonlinear phase space.
QPBOS investigates the dynamics of trajectory divergence in chaotic attractors subject to controlled microscopic quantum perturbations. The architecture couples an orchestrating daemon with an accelerated tensor network engine running on dedicated NVIDIA Blackwell hardware, exploiting exponential phase divergence and numerical stabilization.
Hybrid architecture: Role separation and fault tolerance.
Functional isolation of orchestration and acceleration: orchestrating node (CPU) ↔ secured channel ↔ GPU acceleration node. An automated circuit breaker guarantees continuous operation even if accelerator connectivity is interrupted.
- Dedicated RealityKernel daemon (100 ms tick)
- Engineering resource quotas: CPU ≤ 40%, RAM ≤ 800 MB
- Numerical phase stabilizer: Q-LDPC BP-OSD
- Intent registry management and experiment telemetry
- Internal RPC client with automatic Circuit Breaker
Mutual Bearer Token
- NVIDIA GeForce RTX 5070 (8151 MiB VRAM)
- NVIDIA CUDA-Q 0.16.0 execution runtime
- Hard project memory ceiling: ≤ 6144 MB VRAM
- Autonomous router: Statevector / TensorNet / MPS
- Allowlist of numerically validated circuit templates
Strict Network Isolation
The GPU accelerator service binds exclusively to the local loopback interface and is never exposed to the public WAN. Inter-node communication is handled across an encrypted channel authenticated via high-entropy Bearer tokens.
Silent Fallback (Circuit Breaker)
If the GPU accelerator becomes unresponsive or encounters network drops, the orchestrator client transitions immediately to the OPEN state, seamlessly routing requests to a local software generator (SoftwareEntropy / Aer) without stalling the kernel loop.
Closed Template Family
The accelerator rejects arbitrary remote code execution. It accepts exclusively a closed family of parameterized templates (ghz, w_state, linear_entangler, hadamard_entropy, correlated_entropy), strictly validated through Pydantic models.
Why chaotic amplification? Analysis of alternatives (A–D).
Prior to adopting the nonlinear dynamics of the Lorenz attractor, four distinct physical modeling approaches were evaluated. Rejecting competing paradigms is grounded in the hard constraints of quantum mechanics and thermodynamics.
Chaotic Amplification (Lorenz Attractor)
Coupled nonlinear ODE system with 3 degrees of freedom exhibiting a positive maximal Lyapunov exponent (λ > 0). Exponential divergence allows microscopic perturbations to amplify to macroscopic separation in finite time without violating conservation laws.
Predictability horizon: For perturbation |δ0| = 10−10, the Lyapunov horizon is tp = −ln(10−10) / 0,9056 ≈ 25,5 s. Two distinct phase wings (butterfly lobes) provide clean bifurcation between alternative evolution paths.
Uncertainty-Based Steering (Heisenberg)
Modeling Gaussian wave packet dispersion and steering position/momentum variance directly from Heisenberg's fundamental uncertainty relation.
Rejection rationale: At ambient temperature (300 K), thermal environmental decoherence occurs within sub-picosecond scales (τd ~ 10−13 s). Wave packet dispersion cannot be harnessed or directed without complex cryogenic isolation.
Entanglement Network (Bell States)
Attempting to steer macroscopic correlated events via multi-qubit entangled networks (Greenberger–Horne–Zeilinger and Bell state pairs).
Rejection rationale: The No-Communication Theorem proves that entanglement correlations cannot transmit macroscopic information without a classical channel. Relying on raw entanglement to drive physical trajectory steering is physically unviable.
Stochastic Resonance
Utilizing optimal Gaussian noise to amplify sub-threshold signals in bistable potential wells (Benzi et al., 1982).
Rejection rationale: Stochastic resonance modulates signal amplitude rather than providing a mechanism for phase space branch bifurcation. It requires unnaturally fine-tuned noise variance and does not produce distinct trajectory choices.
According to the Sudarshan–Misra theorem (1977), a quantum system continuously observed at high frequency ω → ∞ is frozen in its initial state (Psurvival(NΔt) → 1). In the QPBOS architecture, continuous real-time metric sampling (100 ms tick) counters numerical relaxation of phase divergence, stabilizing the tracked orbit.
Backend router and strict VRAM memory guardrails.
Automatic selection of optimal execution targets based on qubit count, circuit depth, and hardware topology, strictly enforced to prevent Out-Of-Memory (OOM) process faults.
| Backend / Target | Qubit Range | Memory Complexity | Topology & Use Case | Guardrail Ceiling |
|---|---|---|---|---|
| nvidia (Statevector) | 1 – 28 qubits | O(2ⁿ × 16 B) — exponential | Full statevector, universal exact precision | ≤ 6144 MB VRAM |
| tensornet (cuTensorNet) | 2 – 36 qubits | Tensor network path heuristics | Global topologies, QAOA, random circuits | ≤ 36 qubits |
| tensornet-mps (Matrix Product) | 2 – 50+ qubits | O(n · χ² · 16 · 4 B) — polynomial | 1D local topologies, low-entangled states | Bond dim χ ≤ 64 |
| qpp-cpu (CPU Fallback) | 1 – 14 qubits | O(2ⁿ × 16 B) in host RAM | Local CPU fallback when GPU is offline | FORBIDDEN > 14 qubits |
Interactive Router Decision Calculator
Hard Memory Guardrail: CPU Block Above 14 Qubits and 6 GB VRAM Ceiling
Statevector memory requirements scale strictly exponentially: 15 qubits require ~512 KB, 20 qubits need 16 MB, 26 qubits take 1 GB, 28 qubits demand 4 GB, and 30 qubits would require 16 gigabytes. The accelerator allocates a hard project budget of ≤ 6144 MB VRAM (from 8151 MiB total), unconditionally rejecting tasks that exceed capacity. In fallback mode without GPU, CPU simulation above 14 qubits is strictly forbidden, safeguarding host memory from Linux OOM termination.
GPU benchmark metrics and entropy autocorrelation.
Physical execution times measured on an NVIDIA GeForce RTX 5070 Laptop GPU (driver 570+, CUDA-Q 0.16.0) paired with statistical verification of the quantum entropy source.
Measured Quantum Circuit Execution Times
* The ~17–25 s execution time for 50-qubit MPS reflects SVD decomposition computational complexity when truncating to bond dimension 64; it does not indicate process hang or failure.
Entropy Structure: Hadamard vs GHZ Entanglement
Q-LDPC phase stabilizer: Deterministic BP-OSD decoder.
Numerical phase drift correction grounded in sparse parity-check matrices and Belief Propagation with Ordered Statistics Decoding. This is pure linear algebra, not an AI model.
In the QPBOS system, the phase stabilizer prevents chaotic trajectory dispersion and keeps orbital dynamics within the desired convergence bounds. A deterministic quantum LDPC (Low-Density Parity-Check) code is deployed:
- Sparse parity-check matrix: H ∈ 𝔽2m×n with bounded row and column weights.
- Step 1: Syndrome extraction: Calculate error syndrome vector s = H · eT (mod 2) at every system cycle.
- Step 2: Belief Propagation (BP): Iterative message passing over the bipartite Tanner graph to determine log-likelihood ratios (LLR).
- Step 3: OSD-0 Fallback: If BP fails to reach complete numerical convergence, deterministic Gaussian elimination in Galois field 𝔽2 is triggered over the most reliable bit positions.
- Directed decoherence: Exponential damping of ineffective corrections wt+1 = wt · e−Γ·ΔD as divergence expands.
def correct_phase_tick(state_vector, H_matrix):
syndrome = (H_matrix @ error_vector) % 2
if norm(syndrome) == 0: return state_vector
correction = bp_osd_decode(H_matrix, syndrome)
return state_vector - correction
Hypnos i1-8B advisor: a local language model inside the experiment loop.
An optional analytical layer powered by a local language model, assisting phase trajectory interpretation and safe parameter autotuning within boundaries strictly enforced by a hard allowlist.
Data Origin & Quantum Entropy Fine-Tuning
The Hypnos i1-8B model was fine-tuned via "Data-Driven Stochastic Regularization via Quantum Entropy" using raw bitstrings from physical measurements of entangled GHZ states, acquired on IBM Quantum Heron processors with 133 and 156 qubits (quantum job IDs: d4gcir92bisc73a3d29g, d4gcoqscdebc73f10g3g, d4go61olslhc73d0u1ig).
Direct quote from the model card: "This injection of 'Out-of-Distribution' quantum data forces the model's attention mechanism to adapt to non-linguistic, high-entropy patterns." Training datasets included openai/gsm8k, open-thoughts/OpenThoughts-114k, and nvidia/OpenMathReasoning. Declared intended domain: complex logic, chain-of-thought reasoning, and mathematics. Author declaration (unverified independently): first known language model fine-tuned on raw GHZ state measurement data from 100+ qubits on IBM hardware.
Advisor Role in QPBOS & Methodological Caveat
The QPBOS entropy kernel correlated_entropy constructs GHZ blocks with parameterized coupling. Hypnos was fine-tuned on physical measurements of precisely this class of states from a physical quantum processor. The Hypnos advisor and the controlled subsystem operate on the same signal class: non-linguistic, high-entropy bit patterns from GHZ states. This is a design rationale, not a measured advantage — the project has not conducted comparative benchmark evaluations between Hypnos and non-fine-tuned baseline models.
Lineage distinction: the base model is NousResearch/Hermes-3-Llama-3.1-8B — the advisor is not an autonomous agent system nor part of the OpenClaw framework.
Authorship and credit. Hypnos i1-8B is the work of squ11z1, released under Apache 2.0. The idea and the execution are theirs — fine-tuning a language model on raw measurements from quantum hardware is not an obvious move, and without it this layer of QPBOS would not exist. The author announced the project on r/LocalLLaMA. QPBOS merely uses the model; it had no part in creating it.
Default Dormancy
The Hypnos advisor module remains entirely dormant. The system loop makes zero network or inference calls, ensuring zero execution overhead and 100% deterministic numerical execution.
Evaluation & Feedback
Following completed runs, the Hypnos advisor reviews telemetry vectors (divergence, autocorrelation, coherence metrics) and produces parameter suggestions for operator inspection and manual approval.
Hard Allowlist Filter
The Hypnos advisor suggests parameter adjustments directly in the experiment loop. Every proposal must clear a rigid numerical allowlist filter. Any value outside defined ranges is immediately dropped.
Allowlist Bounded Ranges (Whitelist Bounds)
The Hypnos advisor has no access to underlying code or executive control. It is restricted to suggesting numeric scalar values strictly within hard-coded limits:
perturbation_magnitude: 1.0e-12 ≤ ε ≤ 1.0e-4entropy_window: 16 ≤ w ≤ 4096 bitsstabilizer_penalty: 0.0 ≤ p ≤ 1.0max_transitions: 10 ≤ t ≤ 1,000,000retry_delay: 0.01 ≤ d ≤ 60.0 s
Local Execution & Privacy
The advisor language model runs entirely inside the local laboratory environment. No telemetry data, intent signals, or operational parameters leave the private premises. Zero transmission to third-party public clouds.
Fault-Tolerant Circuit Breaker
The advisor client features an independent circuit breaker. After 3 consecutive failed calls, timeouts (3.0 s), or schema parsing errors, the circuit trips to OPEN and silently drops back to advisor-less execution without stalling the experiment loop.
Research hypothesis: Phase dynamics and estimator self-calibration.
QPBOS explores whether microscopic quantum correlations in nonlinear systems with positive Lyapunov exponents can reproducibly bias macroscopic evolution trajectories.
Estimator self-calibration: Detecting and minimizing discrepancy from observation
The system measures trajectory divergence rather than assuming a priori alignment with intent. When an operator confirms that an intent has been reflected in observation, the system consumes this as feedback: it updates the ritual/intent registry and reinforcement material, and subsequently adjusts its own computational parameters so that future runs correlate more closely with the reality branch corresponding to the confirmed intent. In essence, the architecture is engineered to detect its own discrepancy from observation and iteratively reduce it via adaptive feedback.
This framework is falsifiable by design: discrepancy between prediction and observation is continuously logged in divergence journals and subjected to formal statistical tests, allowing objective evaluation of whether the self-calibration process converges over extended trial runs.
Physical hardware modules — the avalanche noise breakdown TRNG circuit and the biofeedback (BCI) interface — have not yet been fabricated; however, complete engineering schematics and a full bill of materials (BOM) are established, with hardware assembly scheduled in the next project phase. The entire software stack (the EntropySource abstraction, serial port detection, intent vector validation and REST API normalization) is fully implemented, unit-tested, and ready for physical hardware attachment. Until connected, the system operates reliably on local software entropy generators.
The control layer: what would have to happen for the hypothesis to be rejected.
The divergence Δ(t) from the previous section is a telemetry signal for the engineering loop — it is not the endpoint of the study. With a positive Lyapunov exponent, trajectories separate under any perturbation whatsoever, including floating-point rounding error. What decides the question is something else: whether the invariant measure on the attractor changes.
Full method description in chapter 12 of the technical documentation: Measurement rigour: controls, blinding and the invariant measure.
As an artistic companion to QPBOS engineering, abstract physical and mathematical concepts (the uncertainty principle, entanglement, phase decoherence, bifurcation) are metaphorically translated across 6 hip-hop tracks with raw urban themes, exploring crossroads and life choices.