Skill UI

Cirq is Google Quantum AI’s Python framework for building, simulating, and deploying noise-aware quantum circuits on both simulators and real hardware, complete with experiment templates, transformation utilities, and hardware-specific toolkits.

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

QuTiP is a Python toolbox for simulating open and closed quantum systems, offering solvers for master equations, trajectory methods, visualization, and analysis tailored to quantum research, education, and exploratory physics studies.

Assesses organizational readiness for migrating cryptographic systems to post-quantum standards (PQC), aligning with NIST guidelines. The tool performs comprehensive cryptographic inventory scanning to identify quantum-vulnerable algorithms (RSA, ECDH, ECDSA) and evaluates the feasibility of implementing hybrid TLS configurations using standards like ML-KEM and ML-DSA. Essential for implementing crypto-agility across enterprise infrastructure.

Cirq is a Google open-source framework for designing, simulating, and running quantum circuits on quantum computers. It supports NISQ circuits, noise modeling, and hardware integration with Google Quantum AI and partners like IonQ. Ideal for Python-based quantum circuit development and optimization.

Hardware-agnostic quantum machine learning framework with automatic differentiation for training quantum circuits, building hybrid quantum-classical models, and deploying across IBM, Google, Rigetti, IonQ, and simulators.

Qiskit is a Python framework for building, simulating, and executing quantum circuits. It supports local simulation, noisy simulation, and IBM Quantum hardware execution. Users can utilize V2 primitives like Sampler and Estimator for variational algorithms and quantum machine learning tasks.

Simulate closed and open quantum-system models using QuTiP 5. Supports deterministic, trajectory, and steady-state workflows for local quantum-dynamics work requiring explicit physical assumptions and numerical convergence.

A comprehensive guide for engineering the migration from classical public-key cryptography to quantum-resistant standards. This skill covers building a Cryptographic Bill of Materials (CBOM) to inventory assets, prioritizing migration based on Harvest-Now-Decrypt-Later (HNDL) risk, and deploying hybrid PQC key exchange (e.g., X25519MLKEM768) on critical channels like TLS, VPN, and SSH. Essential for achieving long-term quantum readiness and crypto-agility.

This skill mints tamper-evident, post-quantum-signed receipts for consequential agent actions (e.g., deployment, payment, access grant). It records who, what, and under which policy the action occurred, ensuring the evidence cannot be altered later. By providing verifiable proof from the certificate alone, it is crucial for meeting regulatory compliance requirements, such as the EU AI Act, and building auditable, trustworthy systems.

A comprehensive skill designed for authors targeting Classical and Quantum Gravity (CQG). It guides users on the journal's specific scope, methodological rigor (including mathematical proofs, numerical analysis, and observational constraints), and appropriate academic framing for manuscripts in general relativity, cosmology, and quantum gravity. Use this to assess article suitability before submission.