Quantum-informational modeling of mental markers using the I-field (information field) approach. Applies Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlations in cognition and decision-making. Does NOT assume physical quantum processes in the brain. Use when: quantum-like cognition, mental contextuality, decision dynamics, quantum cognition modeling, I-field theory, mental markers.
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Install skill "quantum-like-mental-markers" with this command: npx skills add hiyenwong/skillsmp-hiyenwong-hiyenwong-quantum-like-mental-markers
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SPATE methodology for spiking-phase adaptive temporal encoding in quantum machine learning. Converts real-valued data into leaky integrate-and-fire spike trains and maps spike statistics to quantum rotations with temporal qubits. Use when: quantum ML encoding, spike-driven temporal encoding, quantum feature preparation, temporal qubits, QML pipeline enhancement.
Event-driven asynchronous retrieval in high-capacity kernel Hopfield networks. KLR Hopfield networks achieve P/N ≈ 30 storage capacity with asynchronous updates, enabling energy-efficient neuromorphic deployment. Event count matches initial Hamming distance — minimal spurious oscillations. Activation: Hopfield network, kernel associative memory, event-driven computation, asynchronous retrieval, neuromorphic memory, storage capacity, KLR Hopfield, margin maximization.
Use Physics-Informed Neural Networks (PINNs) for quantum pulse optimization and noise-aware gate fidelity. Specifically for optimizing quantum control pulses in exchange-only spin qubit systems, handling charge noise, and maximizing gate-level fidelity through noise-averaged training. Use when: optimizing quantum pulses, PINN-based quantum control, spin qubit noise mitigation, exchange-only qubits, quantum gate pulse design, charge noise optimization, silicon spin qubits.
Time-bin-encoded Quantum Photonic Neural Networks (QPNN) architecture. Reconfigurable nonlinear photonic circuits inspired by the brain, trained to process quantum information. Time encoding requires constant number of photonic elements regardless of network size/depth. Use when: quantum photonic circuits, time-encoded QNN, photonic neural networks, quantum dot nonlinearities, Bell-state analysis, Kerr nonlinearity.