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Installation instructions

pyqpp is a Python 3 wrapper for Quantum++. pyqpp requires the same dependencies as Quantum++, and can be installed using pip

pip install git+https://github.com/softwareQinc/qpp

Creating python stubs for IDE autocompletion and static type checking

In case autocompletion (or static type checking via mypy) does not work properly in your editor/IDE, you may need to create python stubs for the package. To do this, execute

mkdir ~/python_stubs
export MYPATH=$MYPATH:~/python_subs # put this in your .profile or .bashrc
. ~/venv/bin/activate
stubgen -p pyqpp -o ~/python_stubs
ln -s ~/python_stubs/pyqpp ~/venv/lib/python3.11/site-packages

In the above, we assumed that your platform is UNIX/UNIX-like, and that you have pyqpp installed in a virtual environment under ~/venv. Please modify accordingly for your system.

Overview

pyqpp includes Bit_circuit, Dynamic_bitset, QCircuitT, QEngineT, QNoisyEngineT, and several other derived Engine classes. Additionally, pyqpp provides commonly used quantum gates and states, and some basic Eigen operations.

Example:

import numpy as np
from pyqpp import *

print("Qubit teleportation quantum circuit simulation\n")

# quantum circuit with 3 qubits and 2 classical bits
qc = QCircuit(3, 2)
# set the qubit 0 to a random state
U = randU(2)
# apply the gate U with name randU to qubit 0
qc.gate(U, 0, "randU")

# set the MES between qubits 1 and 2
qc.gate(gates.H, 1)
qc.CTRL(gates.X, 1, 2)

# perform the Bell measurement between qubits 0 and 1
qc.CTRL(gates.X, 0, 1)
qc.gate(gates.H, 0)
qc.measure([0, 1])

# apply the classical controls
qc.cCTRL(gates.X, 1, 2)
qc.cCTRL(gates.Z, 0, 2)

# initialize the quantum engine with a circuit
engine = QEngine(qc)

# display the quantum circuit and its corresponding resources
print(qc)
print()
print(qc.get_resources())
print()

# execute the entire circuit
engine.execute()

# display the measurement statistics
print(engine)
print()

# verify that the teleportation was successful
psi_in = np.matmul(U, states.z0)
psi_out = engine.get_state()
print("Teleported state:")
print(dirac(psi_out))
print("Norm difference:\n", norm(psi_out - psi_in))

OpenQASM circuits

Use pyqpp.qasm.read_from_file to obtain the QCircuit representation of an OpenQASM 2.0 file.

Custom Bindings

pyqpp was created using pybind11, see pyqpp/qpp_wrapper.cpp. To wrap a custom function, use pybind11::module::def.

template<typename Func, typename ...Extra>
module &def(const char *name_, Func &&f, const Extra&... extra)

Func can be a plain C++ function, a function pointer, or a lambda function.

For example, consider the qpp::randU method

cmat randU(idx D = 2);

which is wrapped as

PYBIND11_MODULE(pyqpp, m) {
    ...

    m.def("randU", &qpp::randU, "Generates a random unitary matrix",
          py::arg("D") = 2);

    ...
}

Template methods

We cannot wrap templated functions; instead, we must explicitly instantiate them. For example, consider the qpp::norm method

template <typename Derived>
double norm(const Eigen::MatrixBase<Derived>& A);

One way to wrap this is

PYBIND11_MODULE(pyqpp, m) {
    ...

    m.def("norm", [](const cmat& A) { return qpp::norm(A); }, "Frobenius norm");
    m.def("norm", [](const ket& A) { return qpp::norm(A); }, "Frobenius norm");

    ...
}

This creates the overloaded pyqpp.norm function, which can accept cmat or ket types. To avoid repetition of boilerplate code, we can templatize the binding:

template<typename T>
void def_norm(pybind11::module &m) {
    m.def("norm", [](const T& A) { return qpp::norm(A); }, "Frobenius norm");
}

PYBIND11_MODULE(pyqpp, m) {
    ...

    def_norm<cmat>(m);
    def_norm<ket>(m);

    ...
}