qaoalib
Implementations of VQA simulations for combinatorial optimization (mainly with graph and Max-cut).
v0.2 works towards adapting the circuit simulations with new Qiskit primitives: Sampler and Estimator (starting from Qiskit 1.0).
How to install
You can install from the PyPI:
pip install --upgrade qaoalib
Usage
Solving Max-cut with VQE:
import networkx as nx
from qaoalib.solver import VQE
from qiskit_optimization.applications import Maxcut
G = nx.random_regular_graph(3, 6) # 3-regular graph wtih 6 nodes
qp = Maxcut(G).to_quadratic_program()
ansatz = # some preferred ansatz
solver = VQE(qp, ansatz)
result = solver.solve()
print(result)
Usage (legacy)
Calculate Max-cut expectation with Qmc or QmcFastKron (faster version):
import networkx as nx
from qaoalib.qaoa import Qmc, QmcFastKron
G = nx.fast_gnp_random_graph(10, 0.5) # Random graph with 10 nodes
params = [0.2, 0.4, 0.3, 0.5] # 4 params, depth = 2
qmc = Qmc(G, params) # or QmcFastKron(G, params)
qmc.run()
print(qmc.expectation)
Plot landscapes of the QAOA Max-cut expectation function:
import networkx as nx
from qaoalib.qaoa.landscape import HybridFast
G = nx.fast_gnp_random_graph(10, 0.5)
prev_params = [0.1, 0.2] # previous parameters (gamma1, beta1)
ins = HybridFast(G, prev_params) # plots the landscape wrt gamma2 and beta2 with previous parameters given.
ins.create_grid()
ins.show_landscape()
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Metadata
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Release files for qaoalib 0.3.1
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| qaoalib-0.3.1.tar.gz | 17.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| qaoalib-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 40.5 kB
Release files / qaoalib-0.3.1.tar.gz
| Download URL | qaoalib-0.3.1.tar.gz |
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| Size | 17.6 kB |
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