qlass

qlass is a package to compile quantum algorithms on photonic devices. Part of the Quantum Glass-based Photonic Integrated Circuits (QLASS) project funded by the European Union.

Installing qlass

Stable release (PyPI)

To install the latest stable release of qlass from PyPI:

pip install qlass

Development install

To install the development version, set the working directory to the top level of the repository and run:

uv sync --all-groups

If you don't have uv installed, you can checkout the installation instructions here. Or, you can install it via pip:

pip install uv

qlass builds upon open-source scientific software packages in Python: scipy for numerical optimization, pyscf and openfermion for quantum chemistry, qiskit for quantum computing, and perceval for quantum photonics compilation. Optionally, one can also use the piquasso package for quantum optics simulations, which can offer improved performance compared to perceval in certain regimes.

Getting started

You can use this demo notebook on the variational quantum eigensolver (VQE) to get started with the qlass package, or check out the example script.

Features

Circuit Compilation

qlass provides a convenient function to compile Qiskit quantum circuits to Perceval processors:

from qiskit import QuantumCircuit
from qlass import compile

# Create a Qiskit circuit
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)

# Compile to Perceval processor
processor = compile(qc)

Variational Quantum Eigensolver (VQE)

qlass includes tools for implementing the Variational Quantum Eigensolver on photonic quantum computers:

from qlass.vqe import VQE, le_ansatz
from qlass.utils import loss_function
from qlass.quantum_chemistry import LiH_hamiltonian

from perceval.algorithm import Sampler

# Generate a Hamiltonian for the LiH molecule
hamiltonian = LiH_hamiltonian(num_electrons=2, num_orbitals=1)

# Define an executor function that uses the linear entangled ansatz
def executor(params, pauli_string):
    processor = le_ansatz(params, pauli_string)
    sampler = Sampler(processor)
    samples = sampler.samples(10_000)
    return samples

# Initialize the VQE solver
vqe = VQE(
    hamiltonian=hamiltonian,
    executor=executor,
    num_params=4, # Number of parameters in the linear entangled ansatz
)

# Run the VQE optimization
vqe_energy = vqe.run(
    max_iterations=10,
    verbose=True
)

Quantum Chemistry

The package provides tools for working with quantum chemistry Hamiltonians:

from qlass.quantum_chemistry import LiH_hamiltonian, brute_force_minimize

# Generate a Hamiltonian for the LiH molecule
hamiltonian = LiH_hamiltonian(num_electrons=2, num_orbitals=1)

# Calculate the exact ground state energy for comparison
exact_energy = brute_force_minimize(hamiltonian)

Module Structure

The qlass package is organized into several modules:

• qlass.compiler: Functions for compiling quantum circuits to photonic processors
• qlass.quantum_chemistry: Tools for generating and manipulating Hamiltonians
• qlass.vqe: VQE ansatz implementations for photonic quantum computing
• qlass.utils: Utility functions for executing algorithms and processing results

Documentation

The main functions of the package are commented using the Google style format and can be found here.

Contributing

qlass is developed by the Unitary Foundation, in collaboration with QLASS performers.

You can join the UF Discord server for community support.

For a guide to opening a PR, checkout the contributing guide.

Funding

Funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.