quantum-skills

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 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 AQC, HHL, LCU, QFT, the basic single-qubit QSP demo, QSVT-QLSA, and VQLS.

Solve user-provided linear systems Ax = b with the UnitaryLab Variational Quantum Linear Solver, using a hardware-efficient ansatz and selectable local Hadamard-test, local classical, or global cost functions.

A top-level index of quantum algorithms centered on the UnitaryLab implementation, covering quantum primitives, linear systems, state preparation, cryptography, Hamiltonian simulation, Schrodingerization, quantum machine learning, eigensolvers, gradients, and quantum error correction, with selected Qiskit, PennyLane, and Classiq examples included as reference extensions.

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). Relevant trigger terms include discrete log, DLP, Shor discrete logarithm, g^x mod P, modular exponentiation, two-dimensional Fourier sampling, continued fractions, congruence solving, and quantum cryptography demos.

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). Relevant keywords include shor, factor N, order finding, period finding, modular exponentiation, continued fraction, quantum factoring, and ShorAlgorithm.

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.

Quantum algorithms that attack classical cryptographic protocols, including Shor's integer factorization, Shor-style discrete logarithm solving with two-register Fourier sampling, and Simon's hidden subgroup problem.

Simulate the time evolution of a quantum system using Cartan decomposition.

QSP-HS provides 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(t*x).

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.

Simulate the time evolution of a quantum system using Taylor series expansion.

Explains and demonstrates UnitaryLab's small-scale adiabatic quantum linear-system solver. Use it for AQC or QLSP questions, simulator examples, and work involving Householder state preparation, SVD block encoding, adiabatic schedules, post-selection, solution rescaling, or residual checks.

Quantum Singular Value Transformation (QSVT) based Linear System Solver (QLSA) implements matrix inversion through polynomial approximation, offering significant asymptotic complexity advantages.

A quantum algorithm for performing the Hadamard transform, which is a fundamental operation in quantum computing that creates superposition states. This skill includes efficient implementations and educational resources for understanding and utilizing the Hadamard transform in various quantum algorithms and applications.

A collection of fundamental quantum computing primitives implemented using UnitaryLab, including basic gates, state preparation techniques, and measurement protocols. Provides efficient implementations and educational resources for understanding and utilizing quantum primitives in algorithm development.

Guides small open-chain Fermi-Hubbard ground-state calculations with UnitaryLab's VQE workflow, including its Jordan-Wigner mapping, Ry-Rz COBYLA ansatz, exact-energy reference, endianness checks, output contract, and paired-mode spin measurements.

Variational quantum algorithms for optimization and machine learning, including QAOA for combinatorial optimization, VQE for ground-state energy estimation, Fermi-Hubbard VQE for interacting-fermion ground-state simulation, VQC for supervised learning, QCBM for generative modeling, and CVQNN for continuous-variable workflows.

Prepares an arbitrary small complex quantum state using the Möttönen decomposition. This skill explains the amplitude and phase rotations, Gray-code uniformly controlled RY/RZ ladders, bit-order conversion, and phase-invariant validation used by UnitaryLab.

Loads states with a Matrix Product State representation when low-entanglement structure can reduce the preparation cost. It covers state-to-MPS decomposition, bond-dimension truncation, canonicalization, QR-based unitary completion, work-qubit encoding, leakage, and phase-invariant validation in UnitaryLab.

Provides repository-faithful multiplexer state preparation through normalization, zero padding, one internal bit reversal, binary magnitude loading, basis-selective phase loading, and the actual run() contract.

使用固定 Pauli-word 旋转序列，通过保真度优化近似制备目标态，并用全局相位不变的 L2 误差验证结果。

Quantum state-preparation algorithms for loading target amplitude vectors into UnitaryLab circuits, including sparse Superposition, Möttönen decomposition, Matrix Product State preparation, recursive Multiplexer preparation, and variational Pauli-word preparation.

Prepares a normalized state with sparse computational-basis support using compact coefficient preparation followed by a support permutation. The current implementation is exact up to floating-point error but materializes dense matrices.

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.

Concise guide to the SPSA estimator and sampler gradient examples for parameterized quantum circuits.

A statevector-simulated implementation of the HHL quantum linear systems algorithm. HHL can offer exponential dimension advantages only under restrictive assumptions such as efficient state preparation, sparse/block-encoded matrix access, bounded condition number, efficient Hamiltonian simulation, and partial readout of observables rather than full classical-vector recovery.

A subroutine for probabilistically applying a non-unitary operator M = Σⱼ αⱼUⱼ to a quantum state via PREPARE-SELECT-UNPREPARE. A key building block for block encoding, Hamiltonian simulation, and quantum linear solvers — not a standalone linear system solver.

Basic Quantum Signal Processing (QSP) demo for single-qubit signal operators, phase-factor optimization, and scalar polynomial approximation such as cos(t*x). Use the Hamiltonian-simulation QSP-HS skill instead for block-encoded Hamiltonians and e^{-iHt}.

A quantum algorithm that generalizes Grover's search algorithm, allowing for the amplification of the probability of desired outcomes in a quantum state. It is used to find marked items in an unsorted database with quadratic speedup compared to classical algorithms. This skill provides a comprehensive guide to understanding, implementing (using UnitaryLab's quantum simulator), and utilizing amplitude amplification in quantum computing applications.

Grover search algorithm for finding a marked computational-basis state in an unstructured search space with quadratic query speedup. Use this skill for the repository's standalone GroverAlgorithm implementation.

Use this skill when the user asks for Quantum Fourier Transform (QFT), inverse QFT (IQFT), Fourier-basis state conversion, QFT circuit construction, or implementing/running/debugging QFTAlgorithm in the UnitaryLab algorithm library. Prefer the UnitaryLab `QFTAlgorithm` and `unitarylab.core.Circuit` implementation; PennyLane material is reference-only.

A quantum algorithm for estimating the amplitude of a specific state in a quantum superposition, which can be used for various applications such as Monte Carlo simulations and optimization problems. Provides efficient implementations and educational resources for understanding and utilizing amplitude estimation in quantum algorithm development.

A quantum phase estimation algorithm that can estimate the eigenvalues of a unitary operator with high precision, which is a fundamental component in many quantum algorithms such as Shor's algorithm and quantum simulation.

Skill for understanding, using, and implementing the Quantum Circuit Born Machine (QCBM) for learning discrete probability distributions (Bars-and-Stripes) via the QCBMAlgorithm class.

QDrift randomized Hamiltonian simulation, approximating e^{-iHt} by stochastically sampling Pauli-term evolutions with probability proportional to coefficient magnitude.

Trotter-Suzuki product-formula Hamiltonian simulation, approximating e^{-iHt} via structured short-time exponential products with controllable order and step count.

A quantum algorithm that uses the Hadamard test to estimate the expectation value of a unitary operator with respect to a given quantum state. This algorithm is fundamental in quantum computing and has applications in various quantum algorithms, including quantum phase estimation and variational quantum algorithms.

Skill for understanding, using, and implementing the Continuous Variable Quantum Neural Network (CVQNN) for binary classification via the CVQNNAlgorithm class, CVSimulator, and CVClassifier.

Skill for understanding, using, and implementing the Quantum Approximate Optimization Algorithm (QAOA) for Max-Cut problems via the QAOAAlgorithm class.