QCut 0.2.2

A package for performing wire cuts of hardware without reset-gates or mid-circuit measurements. Built on top of qiskit

QCut

QCut is a quantum circuit knitting package for performing wire cuts especially designed to not use reset gates or mid-circuit measurements since on early NISQ devices they pose significant errors, if available at all.

QCut has been designed to work with IQM's qpus, and therefore on the Finnish Quantum Computing Infrastructure (FiQCI), and tested with an IQM Adonis 5-qubit qpu. Additionally, QCut is built to be combatible with IQM’s Qiskit fork iqm_qiskit.

QCut has been built at CSC - IT Center for Science (Finnish IT Center for Science).

Check out jooniv.github.io/QCut/ for documentation and more examples.

Installation

Pip:
Installation should be done via pip

pip install QCut

Using pip is the recommended install method.

Note: for drawing circuits you might have to install pylatexenc. This can also be done with pip.

pip install pylatexenc

Install from source
It is also possible to use QCut by cloning this repository and including it in your project folder.

Usage

1: Import needed packages

import QCut as ck
from QCut import cut
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
from qiskit_aer.primitives import Estimator

2: Start by defining a QuantumCircuit just like in Qiskit

circuit  =  QuantumCircuit(3)
circuit.h(0)
circuit.cx(0,1)
circuit.cx(1,2)
   
circuit.measure_all()

circuit.draw("mpl")

3: Insert cut operations to the circuit to denote where we want to cut the circuit

Note that here we don't insert any measurements. Measurements will be automatically handled by QCut.

cut_circuit = QuantumCircuit(3)
cut_circuit.h(0)
cut_circuit.cx(0,1)
cut_circuit.append(cut, [1])
cut_circuit.cx(1,2)

cut_circuit.draw("mpl")

4. Extract cut locations from cut_circuit and split it into independent subcircuit.

cut_locations, subcircuits, map_qubit = ck.get_locations_and_subcircuits(qc_cut)

Now we can draw our subcircuits.

subcircuits[0].draw("mpl")

subcircuits[1].draw("mpl")

5: Generate experiment circuits by inserting operations from a quasi-probability distribution for the identity channel

experiment_circuits, coefficients, id_meas = ck.get_experiment_circuits(subcircuits, cut_locations)

6: Run the experiment circuits

Here we are using the qisit AerSimulator as our backend but since QCut is backend independent you can choose whatever backend you want as long as you transpile the experiment circuits accordingly. QCut provides a function transpile_experiments() for doing just this.

Since QCut is a circuit knitting package the results are approximations of the actual values.

backend = AerSimulator()
results = ck.run_experiments(experiment_circuits, cut_locations, id_meas, backend=backend)

7. Define observables and calculate expectation values

Observables are Pauli-Z observables and are defined as a list of qubit indices. Multi-qubit observables are defined as a list inside the observable list.

If one wishes to calculate other than Pauli-Z observable expectation values currently this needs to be done by manually modifying the initial circuit to perform the basis transform.

observables = [0,1,2, [0,2]]
expectation_values = ck.estimate_expectation_values(results, coefficients, cut_locations, observables, map_qubit)

8: Finally calculate the exact expectation values and compare them to the results calculated with QCut

paulilist_observables = ck.get_pauli_list(observables, 3)

estimator = Estimator(run_options={"shots": None}, approximation=True)
exact_expvals = (
    estimator.run([circuit] * len(paulilist_observables),  # noqa: PD011
                  list(paulilist_observables)).result().values
)

import numpy as np

np.set_printoptions(formatter={"float": lambda x: f"{x:0.6f}"})

print(f"QCut expectation values:{np.array(expectation_values)}")
print(f"Exact expectation values with ideal simulator :{np.array(exact_expvals)}")

QCut circuit knitting expectation values: [0.007532 0.007532 -0.003662 1.010128]

Exact expectation values with ideal simulator :[0.000000 0.000000 0.000000 1.000000]

As we can see QCut is able to accurately reconstruct the expectation values. (Note that since this is a probabilistic method the results vary a bit each run)

Usage shorthand

For convenience, it is not necessary to go through each of the aforementioned steps individually. Instead, QCut provides a function run() that executes the whole wire-cutting sequence.

The same example can then be run like this:

backend = AerSimulator()
observables = [0,1,2, [0,2]]

estimated_expectation_values = ck.run(cut_circuit, observables, backend)

Running on IQM fake backends

To use QCut with IQM's fake backends it is required to install Qiskit IQM. QCut supports version 15.6. Installation can be done with pip:

pip install qiskit-iqm

After installation just import the backend you want to use:

from iqm.qiskit_iqm import IQMFakeAdonis()
backend = IQMFakeAdonis()

Running on FiQCI

For running on real hardware through the Lumi supercomputer's FiQCI partition follow the instructions here. If you are used to using Qiskit on jupyter notebooks it is recommended to use the Lumi web interface.

Running on other hardware

Running on other providers such as IBM is untested at the moment but as long as the hardware can be accessed with Qiskit QCut should be compatible.

Documentation

The docs are built with sphinx using the sphinx book theme. To build the docs:

cd docs
pip install -r requirements-docs.txt
sphinx-build -v -b html . build/sphinx/html -W

HTML files can then be found under build/sphinx/html/

Acknowledgements

This project is built on top of Qiskit which is licensed under the Apache 2.0 license.

License

Apache 2.0 license