Menu
Skills in this repository
SkillsMP has collected 4,114 skills from hiyenwong/ai_collection. Open a skill to review its source and details.
hiyenwong/ai_collectionShowing 40 of 4,114 collected skills.
Benchmark evaluation of 15 frontier AI chatbots for psychiatric emergency triage using 112 clinical vignettes. Assesses accuracy, under-triage/over-triage rates, and risk-level specific performance. Triggers: psychiatric triage, emergency mental health, AI…
Pulse-level Quantum Fourier Models (QFMs) for quantum machine learning. Optimizes variational quantum algorithms by using pulse parameters instead of gate-level angles, providing higher-dimensional escape routes in the optimization landscape. Use when:…
Pulse-level quantum computing skill — design, optimize, and analyze pulse-level variational quantum algorithms beyond the gate abstraction. Covers pulse parameterization, expressibility, Fourier coefficient correlation (FCC), composite gate sub-angle…
Pulse-level Quantum Fourier Models (QFMs) for quantum machine learning. Use when: (1) implementing variational quantum algorithms at the pulse/hardware level, (2) optimizing QFM training landscapes, (3) designing pulse-parameterized quantum circuits, (4)…
Quantum Deeply-Supervised Spiking Neural Network (QDS-SNN) methodology for energy-efficient traffic sign recognition. Integrates QNNs with SNNs using TSA-LIF neurons and QACM module, achieving 99.72% accuracy with 55.77% energy reduction.
QLAM: Quantum Long-Attention Memory methodology for long-sequence token modeling. Combines quantum linear algebra with attention mechanisms to overcome O(n²) scaling of transformer attention. Based on arXiv:2605.13833.
Path-space regularization for open quantum control using Girsanov's theorem. Use when designing quantum control policies under decoherence, computing trajectory likelihood ratios for monitored quantum systems, or optimizing open quantum system control via…
Quantum measurement return statistics methodology analyzing quantized mean return time under strong and weak monitoring. Connects winding number topology with statistical properties of quantum state recurrence.
Quantum associative memory simulation on photonic processors methodology. Demonstrates Hopfield network dynamics with multi-body interactions realized via multiphoton processes on programmable photonic quantum simulators. arXiv:2605.22922
Four-generation framework for quantum biomedical sensors based on quantum resource utilization. Covers clinical translation challenges, noise limits, ensemble vs single-particle sensing. Use when: quantum biosensors, biomedical quantum sensing, clinical…
Quantum Boltzmann Machine via Bilevel Optimization methodology. Extends QAOA circuit to bilevel optimization for fully connected QBMs, overcoming the fixed target Hamiltonian barrier. Use when building quantum generative models, training quantum Boltzmann…
Quantum-inspired neural network for vision-brain understanding using voxel controlling, phase shifting, and measurement-like projection in Hilbert space. Maps brain region connectivity via quantum-inspired modules for fMRI analysis. Use when: (1) analyzing…
Machine Learning methodology for constructing quantum circuits for sets of matrices. Uses interpretable ML to extract circuit design patterns and build quantum algorithms systematically.
Joint qubit leasing and quantum circuit distribution optimization methodology. ILP formulation for multi-QC quantum network resource allocation, NP-completeness analysis, greedy algorithm with local search. Covers qubit allocation, gate execution placement,…
Quantum circuit simulation of compartmental drug dynamics using variational algorithms for population pharmacokinetics. Reformulates PK/PD models as open quantum systems implemented with PennyLane circuits. Use when: quantum drug simulation, pharmacokinetic…
Generative quantum circuit synthesis from Gate Set Tomography (GST) data using diffusion models and set-vision transformers. Bypasses traditional two-step pipeline (GST characterization + unitary decomposition) by directly learning generative concept spaces…
Extreme Quantum Cognition Machines (EQCM) methodology — quantum learning architectures for deliberative decision making that tolerate noisy and contradictory training data using fixed quantum dynamics with dynamical attention.
Quantum circuit simulation of compartmental pharmacokinetic/pharmacodynamic (PK/PD) models using variational quantum algorithms. Reformulates classical ODE-based drug dynamics as open quantum systems. Use when: quantum pharmacokinetics, quantum PK/PD…
Quantum learning models naturally preserve plasticity in continual learning due to unitary constraints confining optimization to compact manifold, unlike classical networks with unbounded weight growth leading to landscape ruggedness.
Analysis of dephasing effects on quantum correlations and coherence dynamics in oscillating quantum systems. Covers quantum steering, logarithmic negativity, and coherence measures under environmental decoherence. Use when analyzing quantum system robustness…
Quantum-Driven Neuromorphic Computing methodology for million-qubit-scale workloads — synergistic integration of quantum computing and neuromorphic architectures for large-scale computational tasks.
Quantum Hopfield associative memory methodology — photonic quantum simulation of p-body Hopfield models for associative memory retrieval and spin-glass phase analysis.
Quantum kernel methods for medical AI embeddings and foundation models. Use quantum support vector machines (QSVM) with frozen embeddings from medical foundation models (MedSigLIP, RAD-DINO, ViT) for medical imaging classification tasks. Applies quantum…
Quantum-like benchmark framework for context-sensitive associative memory with adaptive plasticity. Provides controlled methodology for comparing quantum-like vs classical associative memory models under weak structural support, order-sensitive recall, and…
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…
Testing quantum-like markers in neural dynamics methodology — investigating quantum probability signatures in brain activity patterns
Analysis of hidden bottleneck in classical and quantum linear reservoir computing. Identifies fundamental information processing capacity limits when reservoir features and readout are both linear. Use when: reservoir computing design, quantum reservoir…
Reinforcement learning for quantum processes with hidden memory. Agent interacts with environment maintaining hidden quantum states evolving via unknown quantum channels, using quantum instruments for sequential intervention. Proves O~(sqrt(K)) regret bound…
Methodology for computing fundamental quantum-metabolic limits on noninvasive brain imaging (MEG/EEG) information capacity. Derives technology-independent bounds from Planck constant, neural metabolism, and geometry. Use when analyzing limits of…
Multitime memory methodology beyond quantum regression theorem for sequential measurement statistics. Use when analyzing non-Markovian quantum processes, multi-time correlation functions, quantum memory effects, or sequential quantum measurement scenarios…
Neural network quantum state architecture for grand canonical ensemble simulations. Use when representing symmetric bosonic wavefunctions in Fock space, studying quantum many-body systems with variable particle number, computing one-body reduced density…
Design patterns for quantum-neuromorphic computing including QHDC mappings, three-layer quantum brain CQEC analysis, LMG Hamiltonian phase transitions, and photonic quantum memristors.
Quantum neuromorphic computing patterns — combining quantum computing with brain-inspired neural architectures. Covers quantum brain modeling, quantum reservoir computing for neural dynamics, brain-inspired quantum neural architectures, spiking-phase quantum…
Quantum optical neuron methodology — camera-free image classification via Hong-Ou-Mandel interference of spatially programmable single photons. Two-photon coincidences directly report overlap between input image mode and learned template. Use when building…
Efficient classical training of model-free quantum photonic reservoirs. Implements quantum extreme learning machines with classical-light training and quantum inference. Activation: quantum photonic reservoir, quantum ELM, classical training quantum reservoir
Quantum Reservoir Computing (QRC) methodology for financial time series forecasting. Uses transverse-field Ising Hamiltonian as reservoir with distinct input and memory qubits to capture temporal dependencies. Benchmarked against econometric models and ML…
Rademacher complexity-based generalization error bounds for quantum reservoir computing (QRC). Covers parameter-dependent bounds for quantum reservoir classes, qubit-scaling analysis, and polynomial readout function risk bounds. Use when: quantum reservoir…
Quantum Reservoir Computing (QRC) methodology for financial time-series forecasting. Uses small-scale quantum systems (≤6 qubits) as nonlinear reservoirs for stock trend classification with >86% accuracy. Platform-agnostic across superconducting circuits and…
Resource-efficient Quantum Reservoir Computing framework for time-series forecasting. Combines fixed quantum reservoir transformation with post-training quantized classical readout for deployment on edge/limited-memory devices.
Controllable quantum memory capacity methodology for quantum reservoir computing using tunable partial-SWAP gates. Unifies feedback-based and recurrent QRC architectures through partial-SWAP interpolation parameter, enabling controllable trade-off between…