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Quantum Algorithms & Applications in Python

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Qiskit Aqua

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Qiskit is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms.

Qiskit is made up elements that work together to enable quantum computing. This element is Aqua (Algorithms for QUantum computing Applications) providing a library of cross-domain algorithms upon which domain-specific applications can be built.

Aqua includes domain application support for:

Note: the chemistry module was the first domain worked on. Aqua version 0.7.0 introduced a new optimization module for solving quadratic problems. At the time of writing the other domains have some logic in them but are not as fully realised. Future work is expected to build out functionality in all application areas.

Aqua was designed to be extensible, and uses a framework where algorithms and support objects used by algorithms, such as optimizers, variational forms, and oracles etc, are derived from a defined base class for the type. These along with other building blocks provide a means for end-users and developers alike to have flexibility and facilitate building and experimenting with different configurations and capability.

Note: Aqua provides some classical algorithms that take the same input data as quantum algorithms solving the same problem. For instance a Hamiltonian operator input to VQE can be used as an input to the NumPyEigensolver. This may be useful for near-term quantum experiments, for problems that can still be solved classically, as their outcome can be easily compared against a classical equivalent since the same input data can be used.

Migration Guide

Aqua has been deprecated:

| Old | New | Library | | :---: | :---: | :---: | | qiskit.aqua.algorithms.amplitudeamplifiers | qiskit.algorithms.amplitudeamplifiers | qiskit-terra | | qiskit.aqua.algorithms.amplitudeestimators | qiskit.algorithms.amplitudeestimators | qiskit-terra | | qiskit.aqua.algorithms.classifiers | qiskitmachinelearning.algorithms.classifiers | qiskit-machine-learning | | qiskit.aqua.algorithms.distributionlearners | qiskitmachinelearning.algorithms.distributionlearners | qiskit-machine-learning | | qiskit.aqua.algorithms.eigensolvers | qiskit.algorithms.eigensolvers | qiskit-terra | | qiskit.aqua.algorithms.factorizers | qiskit.algorithms.factorizers | qiskit-terra | | qiskit.aqua.algorithms.minimumeigensolvers | qiskit.algorithms.minimumeigensolvers | qiskit-terra| | qiskit.aqua.algorithms.VQAlgorithm | qiskit.algorithms.VariationalAlgorithm | qiskit-terra | | qiskit.aqua.aquaglobals | qiskit.utils.algorithmglobals | qiskit-terra| | qiskit.aqua.components.multiclassextensions | | | | qiskit.aqua.components.neuralnetworks | qiskitmachinelearning.algorithms.distributionlearners.qgan | qiskit-machine-learning | | qiskit.aqua.components.optimizers | qiskit.algorithms.optimizers | qiskit-terra | | qiskit.aqua.components.variationalforms | | | qiskit.aqua.operators | qiskit.opflow | qiskit-terra| | qiskit.aqua.QuantumInstance | qiskit.utils.QuantumInstance | qiskit-terra | | qiskit.chemistry | qiskitnature | qiskit-nature| | qiskit.finance | qiskitfinance | qiskit-finance| | qiskit.ml | qiskitmachinelearning | qiskit-machine-learning | | qiskit.optimization | qiskit_optimization | qiskit-optimization |

Installation

We encourage installing Qiskit via the pip tool (a python package manager), which installs all Qiskit elements, including Aqua.

pip install qiskit

pip will handle all dependencies automatically and you will always install the latest (and well-tested) version.

If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Aqua, then you can install from source. To do this follow the instructions in the documentation.

Note: there some optional packages that can be installed such as IBM CPLEX for Aqua and ab-initio chemistry libraries/programs. Refer to Optional Install information in the sections below.


Aqua

The

qiskit.aqua
package contains the core cross-domain algorithms and supporting logic to run these on a quantum backend, whether a real device or simulator.

Optional Installs

Note: while the packages below can be installed directly by pip install, e.g.

pip install cplex
by doing so via the Aqua extrarequires, in this case

pip 'install qiskit-aqua[cplex]'
will ensure that a version compatible with Qiskit is installed._
  • IBM CPLEX may be installed to allow the use of the
    CplexOptimizer
    classical solver algorithm.
    pip 'install qiskit-aqua[cplex]'
    may be used to install the community version.
  • PyTorch, may be installed either using command
    pip install 'qiskit-aqua[torch]'
    to install the package or refer to PyTorch getting started. PyTorch being installed will enable the neural networks
    PyTorchDiscriminator
    component to be used with the QGAN algorithm.
  • CVXPY, may be installed using command
    pip install 'qiskit-aqua[cvx]'
    to enable use of the
    QSVM
    and the classical
    SklearnSVM
    algorithms.

Creating Your First Quantum Program in Qiskit Aqua

Now that Qiskit is installed, it's time to begin working with Aqua. Let's try an experiment using

Grover
's algorithm to find a solution for a Satisfiability (SAT) problem.
$ python
from qiskit import Aer
from qiskit.aqua.components.oracles import LogicalExpressionOracle
from qiskit.aqua.algorithms import Grover

sat_cnf = """ c Example DIMACS 3-sat p cnf 3 5 -1 -2 -3 0 1 -2 3 0 1 2 -3 0 1 -2 -3 0 -1 2 3 0 """

backend = Aer.get_backend('qasm_simulator') oracle = LogicalExpressionOracle(sat_cnf) algorithm = Grover(oracle) result = algorithm.run(backend) print(result.assignment)

The code above demonstrates how

Grover
’s search algorithm can be used with the
LogicalExpressionOracle
to find one satisfying assignment for the Satisfiability (SAT) problem instance encoded in the DIMACS CNF format. The input string
sat_cnf
corresponds to the following Conjunctive Normal Form (CNF):

x1 ∨ ¬x2 ∨ ¬x3) ∧ (x1 ∨ ¬x2x3) ∧ (x1x2 ∨ ¬x3) ∧ (x1 ∨ ¬x2 ∨ ¬x3) ∧ (¬x1x2x3)

The Python code above prints out one possible solution for this CNF. For example, output

1, -2, 3
indicates that logical expression (x1 ∨ ¬x2x3) satisfies the given CNF.

Further examples

Learning path notebooks may be found in the algorithms tutorials section of the documentation and are a great place to start

Jupyter notebooks containing further examples, for Qiskit Aqua, may be found here in the following Qiskit GitHub repositories at qiskit-tutorials/tutorials/algorithms and qiskit-community-tutorials/aqua.


Chemistry

The

qiskit.chemistry
package supports problems including ground state energy computations, excited states and dipole moments of molecule, both open and closed-shell.

The code comprises chemistry drivers, which when provided with a molecular configuration will return one and two-body integrals as well as other data that is efficiently computed classically. This output data from a driver can then be used as input to the chemistry module that contains logic which is able to translate this into a form that is suitable for quantum algorithms. The conversion first creates a FermionicOperator which must then be mapped, e.g. by a Jordan Wigner mapping, to a qubit operator in readiness for the quantum computation.

Optional Installs

To run chemistry experiments using Qiskit's chemistry module, it is recommended that you install a classical computation chemistry software program/library interfaced by Qiskit. Several, as listed below, are supported, and while logic to interface these programs is supplied by the chemistry module via the above pip installation, the dependent programs/libraries themselves need to be installed separately.

  1. Gaussian 16™, a commercial chemistry program
  2. PSI4, a chemistry program that exposes a Python interface allowing for accessing internal objects
  3. PySCF, an open-source Python chemistry program
  4. PyQuante, a pure cross-platform open-source Python chemistry program

HDF5 Driver

A useful functionality integrated into Qiskit's chemistry module is its ability to serialize a file in hierarchical Data Format 5 (HDF5) format representing all the output data from a chemistry driver.

The HDF5 driver accepts such HDF5 files as input so molecular experiments can be run, albeit on the fixed data as stored in the file. As such, if you have some pre-created HDF5 files from created from Qiskit Chemistry, you can use these with the HDF5 driver even if you do not install one of the classical computation packages listed above.

A few sample HDF5 files for different are provided in the chemistry folder of the Qiskit Community Tutorials repository. This HDF5 Driver tutorial contains further information about creating and using such HDF5 files.

Creating Your First Chemistry Programming Experiment in Qiskit

Now that Qiskit is installed, it's time to begin working with the chemistry module. Let's try a chemistry application experiment using VQE (Variational Quantum Eigensolver) algorithm to compute the ground-state (minimum) energy of a molecule.

from qiskit.chemistry import FermionicOperator
from qiskit.chemistry.drivers import PySCFDriver, UnitsType
from qiskit.aqua.operators import Z2Symmetries

Use PySCF, a classical computational chemistry software

package, to compute the one-body and two-body integrals in

molecular-orbital basis, necessary to form the Fermionic operator

driver = PySCFDriver(atom='H .0 .0 .0; H .0 .0 0.735', unit=UnitsType.ANGSTROM, basis='sto3g') molecule = driver.run() num_particles = molecule.num_alpha + molecule.num_beta num_spin_orbitals = molecule.num_orbitals * 2

Build the qubit operator, which is the input to the VQE algorithm in Aqua

ferm_op = FermionicOperator(h1=molecule.one_body_integrals, h2=molecule.two_body_integrals) map_type = 'PARITY' qubit_op = ferm_op.mapping(map_type) qubit_op = Z2Symmetries.two_qubit_reduction(qubit_op, num_particles) num_qubits = qubit_op.num_qubits

setup a classical optimizer for VQE

from qiskit.aqua.components.optimizers import L_BFGS_B optimizer = L_BFGS_B()

setup the initial state for the variational form

from qiskit.chemistry.circuit.library import HartreeFock init_state = HartreeFock(num_spin_orbitals, num_particles)

setup the variational form for VQE

from qiskit.circuit.library import TwoLocal var_form = TwoLocal(num_qubits, ['ry', 'rz'], 'cz')

add the initial state

var_form.compose(init_state, front=True)

setup and run VQE

from qiskit.aqua.algorithms import VQE algorithm = VQE(qubit_op, var_form, optimizer)

set the backend for the quantum computation

from qiskit import Aer backend = Aer.get_backend('statevector_simulator')

result = algorithm.run(backend) print(result.eigenvalue.real)

The program above uses a quantum computer to calculate the ground state energy of molecular Hydrogen, H2, where the two atoms are configured to be at a distance of 0.735 angstroms. The molecular input specification is processed by PySCF driver and data is output that includes one- and two-body molecular-orbital integrals. From the output a fermionic-operator is created which is then parity mapped to generate a qubit operator. Parity mappings allow a precision-preserving optimization that two qubits can be tapered off; a reduction in complexity that is particularly advantageous for NISQ computers.

The qubit operator is then passed as an input to the Variational Quantum Eigensolver (VQE) algorithm, instantiated with a classical optimizer and a RyRz variational form (ansatz). A Hartree-Fock initial state is used as a starting point for the variational form.

The VQE algorithm is then run, in this case on the Qiskit Aer statevector simulator backend. Here we pass a backend but it can be wrapped into a

QuantumInstance
, and that passed to the
run
instead. The
QuantumInstance
API allows you to customize run-time properties of the backend, such as the number of shots, the maximum number of credits to use, settings for the simulator, initial layout of qubits in the mapping and the Terra
PassManager
that will handle the compilation of the circuits. By passing in a backend as is done above it is internally wrapped into a
QuantumInstance
and is a convenience when default setting suffice.

Further examples

Learning path notebooks may be found in the chemistry tutorials section of the documentation and are a great place to start

Jupyter notebooks containing further chemistry examples may be found in the following Qiskit GitHub repositories at qiskit-tutorials/tutorials/chemistry and qiskit-community-tutorials/chemistry.


Finance

The

qiskit.finance
package contains uncertainty components for stock/securities problems, Ising translators for portfolio optimizations and data providers to source real or random data to finance experiments.

Creating Your First Finance Programming Experiment in Qiskit

Now that Qiskit is installed, it's time to begin working with the finance module. Let's try an experiment using Amplitude Estimation algorithm to evaluate a fixed income asset with uncertain interest rates.

import numpy as np
from qiskit import BasicAer
from qiskit.aqua.algorithms import AmplitudeEstimation
from qiskit.circuit.library import NormalDistribution
from qiskit.finance.applications import FixedIncomeExpectedValue

Create a suitable multivariate distribution

num_qubits = [2, 2] bounds = [(0, 0.12), (0, 0.24)] mvnd = NormalDistribution(num_qubits, mu=[0.12, 0.24], sigma=0.01 * np.eye(2), bounds=bounds)

Create fixed income component

fixed_income = FixedIncomeExpectedValue(num_qubits, np.eye(2), np.zeros(2), cash_flow=[1.0, 2.0], rescaling_factor=0.125, bounds=bounds)

the FixedIncomeExpectedValue provides us with the necessary rescalings

post_processing = fixed_income.post_processing

create the A operator for amplitude estimation by prepending the

normal distribution to the function mapping

state_preparation = fixed_income.compose(mvnd, front=True)

Set number of evaluation qubits (samples)

num_eval_qubits = 5

Construct and run amplitude estimation

backend = BasicAer.get_backend('statevector_simulator') algo = AmplitudeEstimation(num_eval_qubits, state_preparation, post_processing=post_processing) result = algo.run(backend)

print('Estimated value:\t%.4f' % result.estimation) print('Probability: \t%.4f' % result.max_probability)

When running the above the estimated value result should be 2.46 and probability 0.8487.

Further examples

Learning path notebooks may be found in the finance tutorials section of the documentation and are a great place to start

Jupyter notebooks containing further finance examples may be found in the following Qiskit GitHub repositories at qiskit-tutorials/tutorials/finance and qiskit-community-tutorials/finance.


Machine Learning

The

qiskit.ml
package simply contains sample datasets at present.
qiskit.aqua
has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for experiments, and there is also QGAN (Quantum Generative Adversarial Network) algorithm.

Creating Your First Machine Learning Programming Experiment in Qiskit

Now that Qiskit is installed, it's time to begin working with Machine Learning. Let's try an experiment using VQC (Variational Quantum Classified) algorithm to train and test samples from a data set to see how accurately the test set can be classified.

from qiskit import BasicAer
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import VQC
from qiskit.aqua.components.optimizers import COBYLA
from qiskit.aqua.components.feature_maps import RawFeatureVector
from qiskit.ml.datasets import wine
from qiskit.circuit.library import TwoLocal

seed = 1376 aqua_globals.random_seed = seed

Use Wine data set for training and test data

feature_dim = 4 # dimension of each data point _, training_input, test_input, _ = wine(training_size=12, test_size=4, n=feature_dim)

feature_map = RawFeatureVector(feature_dimension=feature_dim) vqc = VQC(COBYLA(maxiter=100), feature_map, TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3), training_input, test_input) result = vqc.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'), shots=1024, seed_simulator=seed, seed_transpiler=seed))

print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy']))

Further examples

Learning path notebooks may be found in the machine learning tutorials section of the documentation and are a great place to start

Jupyter notebooks containing further Machine Learning examples may be found in the following Qiskit GitHub repositories at qiskit-tutorials/tutorials/machine_learning and qiskit-community-tutorials/machine_learning.


Optimization

The

qiskit.optimization
package covers the whole range from high-level modeling of optimization problems, with automatic conversion of problems to different required representations, to a suite of easy-to-use quantum optimization algorithms that are ready to run on classical simulators, as well as on real quantum devices via Qiskit.

This optimization module enables easy, efficient modeling of optimization problems using docplex. A uniform interface as well as automatic conversion between different problem representations allows users to solve problems using a large set of algorithms, from variational quantum algorithms, such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the GroverOptimizer leveraging fundamental algorithms provided by Aqua. Furthermore, the modular design of the optimization module allows it to be easily extended and facilitates rapid development and testing of new algorithms. Compatible classical optimizers are also provided for testing, validation, and benchmarking.

Optional Installs

  • IBM CPLEX may be installed using
    pip install 'qiskit-aqua[cplex]'
    to allow the use of the
    CplexOptimzer
    classical solver algorithm, as well as enabling the reading of
    LP
    files.

Creating Your First Optimization Programming Experiment in Qiskit

Now that Qiskit is installed, it's time to begin working with the optimization module. Let's try an optimization experiment to compute the solution of a Max-Cut. The Max-Cut problem can be formulated as quadratic program, which can be solved using many several different algorithms in Qiskit. In this example, the MinimumEigenOptimizer is employed in combination with the Quantum Approximate Optimization Algorithm (QAOA) as minimum eigensolver routine.

import networkx as nx
import numpy as np

from qiskit.optimization import QuadraticProgram from qiskit.optimization.algorithms import MinimumEigenOptimizer

from qiskit import BasicAer from qiskit.aqua.algorithms import QAOA from qiskit.aqua.components.optimizers import SPSA

Generate a graph of 4 nodes

n = 4 graph = nx.Graph() graph.add_nodes_from(np.arange(0, n, 1)) elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)] graph.add_weighted_edges_from(elist)

Compute the weight matrix from the graph

w = nx.adjacency_matrix(graph)

Formulate the problem as quadratic program

problem = QuadraticProgram() _ = [problem.binary_var('x{}'.format(i)) for i in range(n)] # create n binary variables linear = w.dot(np.ones(n)) quadratic = -w problem.maximize(linear=linear, quadratic=quadratic)

Fix node 0 to be 1 to break the symmetry of the max-cut solution

problem.linear_constraint([1, 0, 0, 0], '==', 1)

Run quantum algorithm QAOA on qasm simulator

spsa = SPSA(max_trials=250) backend = BasicAer.get_backend('qasm_simulator') qaoa = QAOA(optimizer=spsa, p=5, quantum_instance=backend) algorithm = MinimumEigenOptimizer(qaoa) result = algorithm.solve(problem) print(result) # prints solution, x=[1, 0, 1, 0], the cost, fval=4

Further examples

Learning path notebooks may be found in the optimization tutorials section of the documentation and are a great place to start.

Jupyter notebooks containing further examples, for the optimization module, may be found in the following Qiskit GitHub repositories at qiskit-tutorials/tutorials/optimization and qiskit-community-tutorials/optimization.


Using a Real Device

You can also use Qiskit to execute your code on a real quantum chip. In order to do so, you need to configure Qiskit to use the credentials in your IBM Quantum Experience account. For more detailed information refer to the relevant instructions in the Qiskit Terra GitHub repository .

Contribution Guidelines

If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and use the Aqua Slack channel for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.

Next Steps

Now you're set up and ready to check out some of the other examples from the Qiskit Tutorials repository, that are used for the IBM Quantum Experience, and from the Qiskit Community Tutorials.

Authors and Citation

Aqua was inspired, authored and brought about by the collective work of a team of researchers. Aqua continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.

Please note that if you do not like the way your name is cited in the BibTex file then consult the information found in the .mailmap file.

License

This project uses the Apache License 2.0.

However there is some code that is included under other licensing as follows:

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