dense-ev 2023.5.22

Qiskit expectation values using optimal dense grouping.

dense_ev

dense_ev implements expectation value measurements in Qiskit using optimal dense grouping arXiv:quant-ph/2305.xxxx. For an $m$-qubit operator that includes all Pauli strings in its decomposition, it provides a speedup of $2^m$ compared to naive evaluation of all strings, and $(3/2)^m$ compared to grouping based on qubit-wise commuting (QWC) families.

Installation

Create a virtual environment to sandbox the installation (optional):

python3 -m venv test-env && source ./test-env/bin/activate

To install,

pip install dense-ev

Install from GitHub:

pip install git+ssh://[e-mail]/atlytle/dense-ev.git

To run unit tests,

from dense_ev.test_op import run_unit_tests

run_unit_tests()

Qiskit version compatibility

Note that dense_ev specifies qiskit < 0.43.0, as the Opflow and QuantumInstance packages have been deprecated as of Qiskit 0.43.0. We plan to update the code to function with the new primitives as this migration continues and more documentation becomes available.

Usage

Functionality for naive and QWC groupings is provided in Qiskit by the PauliExpectation class. DensePauliExpectation extends the functionality to dense optimal grouping, and may be used as a replacement for PauliExpectation:

from dense_EV import DensePauliExpectation

# Simple expectation values:
...

ev_spec = StateFn(H).compose(psi)
expectation = DensePauliExpectation().convert(ev_spec)

...

# VQE example
...

vqe = VQE(ansatz, optimizer=spsa, quantum_instance=qi,
          callback=store_intermediate_result, 
          expectation=DensePauliExpectation())

result = vqe.compute_minimum_eigenvalue(operator=H)

...