Root entrypoint for the quantum-skills package. Use this skill to route requests to the correct simulator or algorithm sub-skill, enforce full skill-chain reading before coding, and avoid duplicating implementation details that are already defined in leaf skills.
A top-level index of quantum algorithms centered on the UnitaryLab implementation, covering quantum primitives, linear systems, cryptography, Hamiltonian simulation, Schrodingerization, quantum machine learning, eigensolvers, gradients, and quantum error correction, with selected Qiskit, PennyLane, and Classiq…
Use when users ask about solving the discrete logarithm problem g^x ≡ y (mod P) with Shor-style two-register Fourier sampling, building/explaining DLP circuits, running simulator demos, or debugging post-processing (continued fractions plus two-dimensional Fourier-sample congruence solving). Triggers: discrete log…
Use this skill when the user asks for Shor integer factorization, quantum order-finding, period estimation, or implementing/running/debugging ShorAlgorithm in this repository (especially matrix/operator methods, IQFT-based phase post-processing, and continued-fraction period recovery). Keywords: shor, factor N, order…
Use for implementing, explaining, running, or debugging Simon's algorithm in this repository, especially for oracle construction, measurement interpretation, GF(2) post-processing, simulator state extraction, and compatible reimplementation. Skill-first for covered code generation, runnable examples, execution…
Exact classical eigensolver for quantum operators using NumPy and SciPy backends, with optional auxiliary operator evaluation and eigenpair filtering. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Variational Quantum Deflation (VQD) eigensolver for computing the lowest excited states of a quantum operator with Qiskit primitives. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Master routing guide for all quantum gradient and geometric tensor methods in this folder. Read this file first, then follow the leaf skill for the chosen method. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Quantum gradient estimation via finite difference method. Supports both Estimator (expectation value gradients) and Sampler (probability distribution gradients) primitives with central, forward, and backward difference schemes. Skill-first for covered code generation, runnable examples, execution, debugging…
Analytic quantum gradient and Quantum Geometric Tensor (QGT) estimation via linear combination of unitaries (LCU). Supports Estimator (expectation value gradients), Sampler (probability distribution gradients), and QGT primitives with configurable derivative types. Skill-first for covered code generation, runnable…
Analytic quantum gradient estimation via the parameter shift rule. Supports both Estimator (expectation value gradients) and Sampler (probability distribution gradients) primitives. Requires circuits composed exclusively of supported gate types. Skill-first for covered code generation, runnable examples, execution…
Compute the Quantum Fisher Information (QFI) matrix for a pure parameterized quantum state using the Quantum Geometric Tensor (QGT). Extracts the real part of the QGT and scales it by 4. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Reverse-mode statevector gradient and QGT computation for parameterized circuits using Qiskit Algorithms classes ReverseEstimatorGradient and ReverseQGT. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Concise guide to the local SPSA estimator and sampler gradient implementations for parameterized quantum circuits. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Quantum Hamiltonian simulation methods for approximating time evolution e^{-iHt}. Includes Trotter-Suzuki decomposition, QDrift randomized sampling, Cartan decomposition, QSP-HS polynomial spectral transformation, and Taylor series LCU expansion. Skill-first for covered code generation, runnable examples, execution…
Simulate the time evolution of a quantum system using Cartan decomposition. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
QDrift randomized Hamiltonian simulation, approximating e^{-iHt} by stochastically sampling Pauli-term evolutions with probability proportional to coefficient magnitude. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
QSP-HS: QSP-based Hamiltonian simulation for approximating e^{-iHt} by block-encoding a Hamiltonian and applying polynomial spectral transformations. Use the basic QSP demo skill instead for single-qubit scalar QSP examples such as cos(tx). Skill-first for covered code generation, runnable examples, execution…
Simulate the time evolution of a quantum system using Taylor series expansion. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
Trotter-Suzuki product-formula Hamiltonian simulation, approximating e^{-iHt} via structured short-time exponential products with controllable order and step count. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.
A set of quantum algorithms for solving linear systems of equations and related Fourier/signal-processing subroutines. This skill includes UnitaryLab implementations and educational resources for HHL, LCU, AQC, QFT, the basic single-qubit QSP demo, QSVT-QLSA, and VQLS. Skill-first for covered code generation, runnable…
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: