organic-magnetic-field-free-quantum - SKILL.md Agent Skill

name: organic-magnetic-field-free-quantum description: "Magnetic-field-free quantum computing and quantum reservoir computing framework using engineered organic materials based on the 3-Layer Quantum Brain Hypothesis. Covers SVILC qubits, CQEC error correction, and four implementation paths. Use when: organic quantum computing, quantum reservoir computing, spin-vortex qubits, magnetic-field-free quantum architectures, quantum neuroscience, or engineered organic quantum materials."

Organic Magnetic-Field-Free Quantum Computing

Unified framework for magnetic-field-free quantum computing and quantum reservoir computing using engineered organic materials, based on the 3-Layer Quantum Brain Hypothesis and SVILC qubits.

Metadata

Source: arXiv:2605.00026
Authors: Hikaru Wakaura, Taiki Tanimae
Published: 2026-04-22

Core Innovation

Extends the spin-vortex-induced loop-current (SVILC) qubit and the 3-Layer Quantum Brain Hypothesis to engineered organic materials, enabling quantum computing without any applied magnetic field — drastically reducing infrastructure overhead.

Four Implementation Paths

Path	Material/System	Focus
P1	Flavin–nitroxide radical-pair reservoir	Quantum reservoir computing
P2	PTM radical array in covalent organic framework	High-fidelity gate operations
P3	SVILC analogue on κ-(BEDT-TTF)₂Cu[N(CN)₂]Br	Conditional on SVILC confirmation
P4	Su–Schrieffer–Heeger soliton on trans-polyacetylene	Topological soliton qubits

Key Results

CQEC Error Correction

Covariant-purification Quantum Error Correction (CQEC) demonstrates recovery past entangbreaking threshold
Peak fidelity gain at γ=0.5: ΔF = +0.303 for Shor-Regev (d=64)
100 trials per configuration; p<10⁻⁵ across all 16 path × algorithm pairs

Quantum Advantage

Bernstein-Vazirani: P2–P4 achieve CQEC-corrected one-query success rates ≥0.95 vs. classical 2⁻ⁿ
7.6–31× advantage for n=3–5

Hardware Efficiency

10–40× manufacturing cost reduction vs. competing platforms
10–200× power consumption reduction
Gate fidelity: CZ ≥ 0.987 for P2–P4 (diarylethene photoswitch)

Framework Components

SVILC Qubit Verification

All eight SVILC conditions must be verified:

Spin-vortex formation in organic π-conjugated system
Loop-current generation without external magnetic field
Qubit coherence time sufficient for gate operations
Two-qubit coupling mechanism
Readout mechanism
Initialization protocol
Gate operation fidelity
Scalability pathway

CQEC Simulator

# Conceptual CQEC simulation workflow
def cqec_simulation(path, algorithm, gamma=0.5, n_trials=100):
    """
    Simulate CQEC-corrected quantum algorithm on organic platform.
    
    Parameters:
    - path: P1, P2, P3, or P4 implementation
    - algorithm: quantum algorithm to test
    - gamma: decoherence parameter
    - n_trials: number of simulation runs
    
    Returns:
    - fidelity_gain: improvement from CQEC
    - success_rate: algorithm success probability
    """
    # 1. Initialize organic material model
    # 2. Apply decoherence model (gamma)
    # 3. Run CQEC recovery channel
    # 4. Execute algorithm
    # 5. Measure fidelity and success rate
    pass

Applications

Scalable quantum computing with minimal infrastructure
Quantum reservoir computing for neuromorphic applications
Low-power quantum edge devices
Brain-inspired quantum information processing
Organic quantum sensor networks

Pitfalls

P3 is conditional: Requires experimental confirmation of SVILC before implementation
Toy-scale benchmarks: Current quantum advantage demonstrated only for n=3–5
Theoretical framework: Many predictions await experimental validation
Material synthesis: Organic material engineering for P2–P4 requires specialized chemistry
Decoherence modeling: Gamma parameter must be calibrated for each material system

Related Skills

quantum-neuromorphic-computing
quantum-reservoir-computing
quantum-brain-neural-architecture
quantum-neuroscience-analysis
neuromimetic-perceptual-compression