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Mycelial Quantum Tomography: First Biological Decoder for Quantum Black Boxes

Zubair E Chowdhury

Published Dec 23, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.5281/zenodo.18035081

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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.
World-first simulation of mycelial networks (Pleurotus ostreatus) as biological quantum tomography decoders for black-box superpositions. **Key Results:**- Fidelity: 0.142 ± 0.05 (biological noise model)- Corr_AB: 0.151 < 0.5 → Local biology confirmed (92% probability)- Nonlocal field hypothesis rejected (p<0.01) **Novel Protocol:** Fungal stabilizer projections map quantum density matrices ρ onto electrical spiking via selective decoherence. Tests universal field access via cross-colony synchronization under Faraday shielding. **Files Included:**- mqt_fixed.py (3.2KB) - Reproducible NumPy code- mqt_paper5.png (319KB) - Figure 1 (300 DPI)- mqt_results.json (253B) - Raw data- mqt.py (3.4KB) - Development version **Hardware:** AlmaLinux 8, cPanel/WHM production server (GoMaa Hosting, UK) **Novelty:** First mycelial quantum tomography simulator. Zero precedents found. **Next:** Pleurotus ostreatus wet-lab validation (Q1 2026).
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.
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.
This dataset and code repository accompanies the publication introducing BA-QEC, the first quantum-error-correction decoder explicitly inspired by biological immune-system architecture. BA-QEC integrates a Bayesian prior derived from human TCRβ CDR3 length distributions and an adaptive clonal-expansion memory mechanism to improve decoding performance in topological quantum codes. Simulations of a distance-7 rotated surface code demonstrate 22% threshold improvement from the biological prior alone, and up to 61% enhancement when combined with clonal memory under temporally correlated (1/f-type) noise. All code is open-source (MIT license) and fully reproducible in <10 minutes on Google Colab. The repository includes: Python notebooks for Stim-based and PyMatching-based simulations, Clonal-expansion cache implementation, Scripts for reproducing figures and pseudothreshold plots, Documentation on integrating the biological prior into MWPM decoding. This work establishes a novel link between adaptive immunity and quantum error correction, offering a new paradigm for biologically inspired, efficient, and adaptive decoders.
Characterizing the memory properties of the environment has become critical for the high-fidelity control of qubits and other advanced quantum systems. However, current non-Markovian tomography techniques are either limited to discrete superoperators, or they employ machine learning methods, neither of which provide physical insight into the dynamics of the quantum system. To circumvent this limitation, we design learning architectures that explicitly encode physical constraints like the properties of completely-positive trace-preserving maps in a differential form. This method preserves the versatility of the machine learning approach without sacrificing the efficiency and fidelity of traditional parameter estimation methods. Our approach provides the physical interpretability that machine learning and opaque superoperators lack. Moreover, it is aware of the underlying continuous dynamics typically disregarded by superoperator-based tomography. This paradigm paves the way to noise-aware optimal quantum control and opens a path to exploiting the bath as a control and error mitigation resource.
Quantum error-correction decoders are typically benchmarked against synthetic circuit-level noise, under the assumption that a decoder's ranking under such noise transfers to hardware and improves as the noise model becomes more realistic. The Willow processor, the first to operate below the surface-code threshold, allows us to test this assumption. We rank a panel of six decoders using a four-rung ladder of noise models with increasing fidelity, evaluated against real data across three code distances, two bases, and fifteen round counts. Rank agreement with hardware appears once the noise model gives each operation type its own error rate. Calibrating the model to the device improves absolute error rates but not rank agreement. We additionally provide the first independent evaluation of NVIDIA's Ising pre-decoder on hardware, at code distances below its training receptive field and via a mapping onto the lattice on which it was trained. Under these conditions, it holds no accuracy-latency advantage: another panel decoder matches or improves on it in both per-cycle error rate and decode latency in 278 of the 280 evaluations. We release the full pipeline and the per-shot outcome of every evaluation, so future decoders and devices can be compared.