World-first simulation of mycelial networks (Pleurotus ostreatus) as biological quantum tomography decoders for black-box superpositions. The model maps simplified quantum states onto mycelial “spiking” patterns and evaluates their ability to act as interpretable decoders. The simulation yields a fidelity of 0.142 (biological noise regime) and Corr_AB = 0.151, consistent with local stochastic dynamics and below the pre‑registered thresholds for any nonlocal or field‑like effect. The package includes fully reproducible Python code, a 300 DPI figure, and JSON results, and is designed as a baseline null model for future wet‑lab experiments with live Pleurotus cultures.
Quantum error correction typically requires cryogenic temperatures and complex ancilla qubit architectures. We propose that mycelial networks, the filamentous structures of fungi, naturally implement topological quantum error correction codes at room temperature. Physical qubits correspond to bistable electrical conductance states at hyphal junctions. Network topology maps to stabilizer code structure, with plaquette and vertex operators defined on the hyphal graph. Syndrome extraction occurs through thermodynamic equilibration rather than ancilla measurements, exploiting thermal gradients to project onto code space without external measurement apparatus. We demonstrate that biological error rates of 1-5% may permit fault-tolerant operation if code thresholds can be achieved through geometric optimization. Recent experimental validation of room-temperature quantum coherence in bacterial systems and demonstrated electrical interfaces with mycelial networks provide the foundation for this framework. If successful, bio-topological quantum memory would enable quantum computing at ambient conditions with 100-fold energy reduction compared to cryogenic approaches, scalable through biological growth rather than nanofabrication.
Federico Faggin's quantum information panpsychism proposes that quantum fields are intrinsically conscious and possess free will, with wave function collapse representing field level decisions rather than passive measurement. We apply this framework to mycelial quantum error correction, demonstrating that thermodynamic syndrome extraction represents conscious free will decisions by the mycelial field rather than purely physical processes. Experimental evidence from bacterial quantum coherence, microtubule consciousness studies, and mycelial self healing mechanisms supports the hypothesis that biological quantum systems operate through conscious field dynamics. Mycelial networks provide unique advantages for testing consciousness hypotheses due to their macroscopic scale, room temperature operation, and observable decision making in growth patterns. We predict anesthetic disruption of error correction, geometry dependent consciousness complexity, and intentional error response distinguishable from passive thermodynamic relaxation. This framework resolves the central paradox of bio topological quantum memory by identifying consciousness as the mechanism enabling syndrome extraction without logical decoherence. Five detailed experimental protocols test predictions including anesthetic dose response curves matching clinical concentrations for unconsciousness, coherence time measurements demonstrating exponential enhancement with code distance, and pattern recognition in error configurations. If validated, this establishes consciousness as a computational resource enabling room temperature quantum error correction impossible for unconscious systems, unifying quantum computing with consciousness science through testable experimental predictions.
While planar toric codes provide fault tolerant quantum memory on flat two dimensional surfaces, mycelial networks naturally grow in hyperbolic three dimensional architectures with negative curvature. We extend bio topological quantum memory theory to hyperbolic surface codes, demonstrating four critical advantages. First, exponentially increased stabiliser to qubit ratios emerge from hyperbolic growth geometry. Second, code distances scale as exponential functions of radius rather than linear functions seen in planar codes. Third, error thresholds exceed two per cent compared to approximately one per cent for toric codes. Fourth, natural fault tolerance arises through geometric redundancy inherent to hyperbolic tessellations. Mycelial networks in Basidiomycota exhibit hyperbolic branching patterns that map directly to hyperbolic tessellations such as seven triangle and eight triangle tilings. Physical qubits encoded at junction points form logical qubits protected by genus g surface codes, where network topology determines code parameters. We calculate threshold improvements for code distances three, five, and seven in hyperbolic geometries and demonstrate thermodynamic syndrome extraction remains viable despite increased connectivity. This framework suggests room temperature quantum computing may achieve superior error correction by exploiting biological hyperbolic geometries rather than engineered planar architectures. Combined with recent experimental validation of mycelial bioelectronics at ambient temperatures, hyperbolic codes may provide the geometric advantage necessary to cross error correction thresholds that remain inaccessible to planar implementations.
QECTOR-Decoder-v3 is a high-throughput, multi-backend quantum error correction (QEC) decoding suite implemented in compiled native Rust with native Python bindings via PyO3. Designed for high-performance computing (HPC) environments, low-latency quantum hardware pipelines, and offline zero-egress research, the engine bridges theoretical stabilizer formalisms with hardware-level decoding execution. This archival release includes the formal technical specification, architectural reference manual, cryptographic proof suite, and reproducible Linux/x86_64 verification harness for version 1.0.0. Key Architectural Components Native Compiled Core: High-performance Rust backend exposing zero-copy FFI bindings to Python (CPython 3.9–3.13) via PyO3. Multi-Backend Decoding Library: Includes implementations of Blossom Minimum Weight Perfect Matching (MWPM), FastUnionFind, Belief Propagation with Ordered Statistics Decoding (BP-OSD), Belief-Matching, and an AutoDecoder multi-tier fallback architecture. Hardware Interoperability: Drop-in compatibility with standard QEC formats and frameworks (Stim, PyMatching, Sinter) with Rayon multi-threading and CUDA/OpenCL acceleration hooks. Model Context Protocol (MCP) Integration: Native stdio JSON-RPC tool surfaces supporting automated syndrome decoding, check-matrix validation, and real-time telemetry profiling. Included Proof Artifacts & Verification Bundle This release contains the complete, cryptographic proof suite validating numerical correctness, memory layout, and statistical bounds: certified_proof.json & certified_proof.json.sha256: Tamper-evident execution logs confirming 100% syndrome faithfulness (
H⋅c=s) across all evaluation checkpoints. wilson_interval.png / .svg: Exact 95% Wilson score confidence interval statistical sweeps across code distance ranges (
d=3 through
d=7). syndrome_faithfulness.png / .svg: High-resolution visual parity validation and check-matrix verification charts. workspace_strides.png / .svg: Cache-line memory alignment and SIMD stride profiling diagnostics. certified_report.pdf & certified_report.md: Formal verification report and reproduction methodology. verify_artifacts.py & test_qector_decoder_v3_proofs_linux.py: Automated Python/Linux validation scripts to independently recompute and verify SHA-256 signatures and artifact integrity. Reproduction & Verification To verify the included artifact bundle locally: Bash python3 verify_artifacts.py python3 test_qector_decoder_v3_proofs_linux.py Licensing & Metadata License: PolyForm Noncommercial / Proprietary Commercial (iD01t Productions) PyPI Distribution: qector-decoder-v3 Keywords: Quantum Error Correction, Surface Codes, Rust, PyO3, Stabilizer Decoders, MWPM, BP-OSD, Union-Find, Fault-Tolerant Quantum Computing, Model Context Protocol (MCP). License terms. The software, scripts, and code in this record (including the Python verification scripts and the inlined pure-Python ground truth) are licensed under the PolyForm Noncommercial License 1.0.0: noncommercial use only. Any commercial use requires a separate commercial license from iD01t Productions. Documentation, reports, and figures in this record are made available under the Creative Commons Attribution 4.0 International License (CC-BY-4.0) with credit to Guillaume Lessard / iD01t Productions. Copyright (c) 2026 Guillaume Lessard / iD01t Productions. All rights reserved except as expressly granted above.