QCut 1.0.1

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

• QCut
• Installation
• Usage
  • Manual Usage
  • Usage shorthand
  • Automatic cuts
  • Running on IQM fake backends
  • Running on FiQCI
  • Running on other hardware
• Documentation
• Acknowledgements
• License

QCut

QCut is a quantum circuit knitting package built on top of qiskit for performing gate cuts and resetless wire cuts allowing simulation of larger quantum circuits on smaller quantum devices or simulators at the cost of a circuit overhead. QCut has been designed and tested to work with IQM's qpus, and the Finnish Quantum Computing Infrastructure (FiQCI).

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

For installation a UNIX-like system is currently needed due to pymetis being used for automatic cut finding. On Windows use WSL

Pip:
Installation should be done via uv

uv pip install QCut
#or
uv add QCut

If using other than the default Qiskit version (newest) it is recommended to install Qiskit first before installing QCut.

Uv can be installed with

#Linux / mac
curl -LsSf https://astral.sh/uv/install.sh | sh

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

uv pip install pylatexenc
#or
uv add pylatexenc

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

cd QCut
uv pip install .
#or
uv sync --no-dev

#or with dev deps
uv sync

Usage

Manual Usage

1: Import needed packages

import QCut as ck
from QCut import cut, cutGate
from qiskit import QuantumCircuit, transpile
from qiskit.circuit.library import CXGate
from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import CXGate
from qiskit_aer import AerSimulator
from qiskit.primitives import StatevectorEstimator, BackendEstimatorV2 as BackendEstimator
from iqm.qiskit_iqm import IQMFakeAdonis

2: Start by defining a QuantumCircuit just like in Qiskit

circuit  =  QuantumCircuit(4)

mult = 1.635
circuit.r(mult*0.46262, mult*0.1446, 0)
circuit.cx(0,1)
circuit.cx(1,2)
circuit.cx(2,3)
   
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.

from qiskit.circuit.library import CXGate

cut_circuit = QuantumCircuit(4)

mult = 1.635
cut_circuit.r(mult*0.46262, mult*0.1446, 0)
cut_circuit.append(**cutGate(CXGate(), 0, 1)) 
cut_circuit.append(cut, [1])
cut_circuit.cx(1,2)
cut_circuit.cx(2,3)

cut_circuit.decompose(gates_to_decompose=["CutGate"]).draw("mpl")

Note that currently QCut only supports cutting Cz gates so cut gates get transpiled down to CutCZ gates (hence the added H gates)

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

cut_circuit = ck.get_locations_and_subcircuits(cut_circuit)

Now we can draw our subcircuits.

cut_circuit.subcircuits[0].draw("mpl")

cut_circuit.subcircuits[1].draw("mpl")

cut_circuit.subcircuits[2].draw("mpl")

5 Define backend and transpile the cut circuit

fake = IQMFakeAdonis() #noisy
sim = AerSimulator() #ideal

transpiled = ck.transpile_subcircuits(cut_circuit, fake, optimization_level=3)

6: Generate experiment circuits

observables = SparsePauliOp(["IIIZ", "IIZI", "IZII", "IIZZ"])

cut_experiment = ck.get_experiment_circuits(transpiled, observables)

Note that both CutCircuit and CutExperiment classes implement the assing_parameters() function of Qiskit.QuantumCircuit

7: Run the experiment circuits

results = ck.run_experiments(cut_experiment, backend=fake)

8: Define observables and calculate expectation values

expectation_values = ck.estimate_expectation_values(results, cut_experiment.expv_data())

9: Finally calculate the exact and noisy expectation values of the original circuit and compare them to the results calculated with QCut

obs = [ob.to_label() for ob in observables.paulis]

estimator = Estimator()
exact_expvals = [e.data.evs for e in
    estimator.run([(x) for x in zip([circuit] * len(obs), obs)]).result()
]


tr = transpile(circuit, backend=fake)

tr_obs = observables.apply_layout(tr.layout)

tr_obs_separate = [
    SparsePauliOp(pauli.to_label()) for pauli in tr_obs.paulis
]

fake_estimator = BackendEstimator(backend=fake)
exps = [e.data.evs for e in
    fake_estimator.run([(x) for x in zip([tr] * len(tr_obs_separate), tr_obs_separate)]).result()
]

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"Noisy expectation values with fake backend:{np.array(exps)}")
print(f"Exact expectation values with ideal simulator :{np.array(exact_expvals)}")

QCut expectation values:[0.704039 0.615275 0.554269 0.808868]

Noisy expectation values with fake backend:[0.587891 0.669922 0.500000 0.777344]

Exact expectation values with ideal simulator :[0.727323 0.727323 0.727323 1.000000]

As we can see QCut is able to accurately reconstruct the expectation values and be more accurate that just using the fake backend as is. (Note that since this is a probabilistic method the results vary a bit each run)

Additionally we can execute QCut using the ideal Aer simulator and see that we get (practically) exact results:

QCut expectation values:[0.699436 0.713172 0.713172 0.979377]

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:

sim = AerSimulator()
observables = SparsePauliOp(["IIIZ", "IIZI", "IZII", "IIZZ"])

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

Automatic cuts

QCut comes with functionality for automatically finding good cut locations that can place both wire and gate cuts.

from QCut import find_cuts

cut_circuit = find_cuts(circuit , 3, cuts="both")

estimated_expectation_values = ck.run_cut_circuit(cut_circuit, observables, sim)

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

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

QCut expectation values:[0.699436 0.713172 0.713172 0.979377]

Exact expectation values with ideal simulator :[0.727323 0.727323 0.727323 1.000000]

Running on IQM fake backends

To use QCut with IQM's fake backends it is required to install Qiskit IQM for Qiskit <= 1.2 or IQM client for Qiskit > 1.2.

Installation can be doen via uv:

uv pip install qiskit-iqm==17.8

or

uv pip install iqm-client[qiskit]

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

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

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

cd docs
uv 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