qumeas

quMeas

quMeas is a high-performance multi-threaded library for computing expectation values of Pauli strings using randomized measurement techniques and cumulant expansion of Pauli operators (strings). It combines classical shadow tomography with statistical cumulant expansion to efficiently estimate Pauli expectation values. Designed with a multi-layered parallelization strategy and optimized C++ backend, quMeas scales efficiently on multi-core systems, making it ideal to incorporate in large-scale quantum algorithms such as VQE for molecular simulation, QAOA for combinatorial optimization, or any other quantum algorithm that requires expectation value of Pauli operators, particularly long Pauli strings.

Features

• Randomized Measurements: Implements an efficient classical shadow tomography quantum measurement to estimate expectation values with randomized measurement bases.
• Cumulant Expansion: Computes expectation values of Pauli operators from a truncated cumulant average expansion utilizing a non-crossing partitioning of the Pauli operators.
• High-Performance Architecture: Built with C++ for performance-critical tasks and exposed to Python via Pybind11, with a robust, multi-threaded layered parallelization strategy.

Installation

quMeas can be installed from pip

pip install qumeas

Pre-built binary wheels for Linux and MacOS are also available at PyPI. Alternatively, to build the package and install from source,

1. Clone the repository:

   git clone --recursive https://github.com/oimeitei/qumeas.git
   

2. Navigate to the project directory, build wheel and install by running the following:

   python -m build --wheel
   pip install dist/qumeas-*.whl	
   

Check installation guide in the documentation for more low-level installation options.

Basic Usage

Check out usage in the documentation as well the Python scripts in /examples for more details

from qumeas import PauliContainer, RandomShadow, QCumulant

# Get measurement basis and outcomes(basis, bits) from general quantum computing
# packages. See documentation & examples for more details

myPauli = PauliContainer(Nqubit=N, #Qubits
                 	pauli_list=plist, # list of Pauli strings
		 	pauli_list_coeff=clist) # list of coeffs for plist

# Compute expectation with classical shadow tomography
myRandom = RandomShadow(PauliObj=myPauli)
expectation_random = myRandom.compute_expectation(basis, bits)

# Compute expectation with cumulant expansion
myCumu = QCumulant(PauliObj=myPauli,
		  measure_basis=basis,
		  measure_outcome_bits=bits)
myCumu.generate_partitions(num_threads=4)
expectation_cumulant = myCumu.compute_expectation_bits()

Documentation

Documentation on Python API, libmeas which expose C++ functions as well as installation instruction and usage are available at /docs. To build the documentation locally, simply navigate to docs and build using make html or make latexpdf.

Latest documentation is available online at quemb.readthedocs.io.