API Usage Overview

Overview

This page is a navigation guide for the unitarylab_algorithms/ library, summarizing the class names, import paths, key parameters, and .test() signatures of all available algorithms.

This page is not a complete auto-generated API reference. Its purpose is to help you quickly locate the algorithm you need and understand how to call it.

BaseAlgorithm — Shared Base Class

The algorithm classes in the six packages cryptology, fundamental_algorithm, hamiltonian_simulation, linear_algebra, quantum_machine_learning, and state_preparation all inherit from unitarylab_algorithms.algo_base.BaseAlgorithm (the schrodingerization package uses its own separate schrodingerization.base.BaseAlgorithm, and its returned dictionary shape is also different — see the Schrödingerization Methods section for details). Every algorithm instance has the following methods:

Algorithm Index by Category

Cryptology

Fundamental Quantum Algorithms

Hamiltonian Simulation

Linear Algebra

Quantum Machine Learning

State Preparation

See State Preparation Algorithms for details, which includes 9 package-level discrepancy notes such as the backend/device/dtype parameters, and inconsistencies between the target_error default value and parameters.json (MPS, Superposition).

Schrödingerization (PDE Solvers)

.test() Quick Reference

Most algorithm modules provide a standalone test() function that can be used to quickly run a default example or verify that the algorithm is available:

# Run using built-in defaults — no need to instantiate the class
from unitarylab_algorithms.fundamental_algorithm.grover.algorithm import test
test()
 
from unitarylab_algorithms.cryptology.shor.algorithm import test
test(N=15, method='matrix', max_retries=15)
 
from unitarylab_algorithms.linear_algebra.hhl.algorithm import test
test(A=[[0.8, 0], [0, 0.4]], b=[1, 2], d=11)
 
from unitarylab_algorithms.quantum_machine_learning.vqe.algorithm import test
test(n=2, layers=2, max_iter=150)
 
from unitarylab_algorithms.state_preparation.mottonen.algorithm import test
test(Psi=[1, 0, 0, 1], target_qubits=2, target_error=1e-6)

Common Usage Patterns

Passing a Circuit Object

Algorithms such as QPE, LCU, and Amplitude Amplification accept a Circuit object from unitarylab:

from unitarylab.core import Circuit
from unitarylab_algorithms import QPEAlgorithm
 
# Build a unitary circuit whose phase we want to estimate
U = Circuit(1)
U.t(0)  # T gate has phase π/4
 
algo = QPEAlgorithm()
result = algo.run(U=U, d=4)

Passing a Hamiltonian Matrix

Hamiltonian simulation algorithms accept a NumPy array or a 2D Python list:

import numpy as np
from unitarylab_algorithms import TrotterAlgorithm
 
H = np.array([[2, 1], [1, 3]])
algo = TrotterAlgorithm()
result = algo.run(H=H, t=1.0, error=1e-8, order=1, steps=1000)