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PauliOpt: A Python library to simplify quantum circuits.

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PauliOpt is a Python library to simplify quantum circuits composed of phase and Pauli gadgets. We currently collect architecture-aware synthesis algorithms for circuits of phase gadgets and Pauli gadgets, and we plan to add more algorithms in the future.

PauliOpt comes with PhaseCircuit and PauliPolynomial classes for representing phase and Pauli polynomials,

image image

and a Circuit class that can convert to and from qiskit and pytket:

Circuit(3, [H(0), H(1), H(2), CRz(π/2, 0, 1), CRz(π/2, 1, 2), CX(0, 1), CX(1, 2)])
image

We view this library as a collection of algorithms for the simplification of quantum circuits. We currently support the following algorithms:

  • "Annealing Optimisation of Mixed ZX Phase Circuits" (arXiv:2206.11839)
  • "Towards a generic compilation approach for quantum circuits through resynthesis" (arXiv:2304.08814)
  • "Architecture-Aware Synthesis of Stabilizer Circuits from Clifford Tableaus" (arXiv:2309.08972)

Please Note: This software library is in a pre-alpha development stage. It is not currently suitable for use by the public.

Installation

You can install the library with pip:

pip install pauliopt

If you already have the library installed and would like the latest version, you can also upgrade with pip:

pip install --upgrade pauliopt

Documentation

The documentation for this library was generated with pdoc. Jupyter notebooks exemplifying various aspects of the library are available in the notebooks folder.

Usage

The goal of this libary is to provide a simple interface and a collection of algorithms relevant for the synthesis of quantum circuits.

Example 1: Simplifying a circuit of phase gadgets

As a first step we can create a PhaseCircuit object, which represents a trotterized circuit of phase gadegets.

from pauliopt.phase import PhaseCircuit, Z, X, pi

circ = PhaseCircuit(4)
circ >>= Z(pi / 2) @ {0, 1}
circ >>= X(pi) @ {0, 2}
circ >>= X(-pi / 4) @ {1, 2, 3}
circ >>= Z(pi / 4) @ {0, 3}
circ >>= X(pi / 2) @ {0, 1, 3}

We can then define the topology of the device we want to map the circuit to. For example, we can define a circle topology with 4 qubits:

from pauliopt.topologies import Topology

topology = Topology.cycle(4)

Finally we can run a simulated annealing algorithm to find a nice optimized circuit:

from pauliopt.phase import OptimizedPhaseCircuit

num_cx_layers = 3
opt_circ = OptimizedPhaseCircuit(circ, topology, num_cx_layers, rng_seed=0)

Example 2: Synthesis of Clifford tableau's

You can create a Clifford tableau and append/prepend operations (H, S, CX), with the following code fragment:

from pauliopt.clifford.tableau import CliffordTableau

ct = CliffordTableau(3)

ct.append_h(0)
ct.append_cnot(0, 2)
ct.append_s(1)

You can visualize the tableau with the following code fragment:

print(ct)

To synthesize the circuit, you can use the following code fragment (note we have used to topology from above):

from pauliopt.clifford.tableau_synthesis import synthesize_tableau

qc, perm = synthesize_tableau(ct, topology, include_swaps=False)
qc = qc.to_qiskit()
print(qc)

Unit tests

To run the unit tests, install the additional requirements using our requirements-dev.txt ( recommended python: 3.9), then to launch then, run:

python -m unittest discover -s ./tests/ -p "test_*.py" -v

(You must run this command from the root directory of the repository.)