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https://github.com/atlytle/dense-ev
Expectation value measurements in Qiskit using optimal dense grouping. Dense-ev provides an improvement of ~2^m over naive grouping and (3/2)^m over qubit-wise commuting groups. https://arxiv.org/abs/2305.11847
https://github.com/atlytle/dense-ev
Last synced: 17 days ago
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Expectation value measurements in Qiskit using optimal dense grouping. Dense-ev provides an improvement of ~2^m over naive grouping and (3/2)^m over qubit-wise commuting groups. https://arxiv.org/abs/2305.11847
- Host: GitHub
- URL: https://github.com/atlytle/dense-ev
- Owner: atlytle
- License: apache-2.0
- Created: 2023-05-18T21:58:09.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-22T18:28:47.000Z (about 1 month ago)
- Last Synced: 2024-05-22T21:32:09.464Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 103 KB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-qiskit - dense-ev - Implements expectation value measurements in qiskit using optimal dense grouping. dense-ev provides an improvement of ~2^m over naive grouping and (3/2)^m over qubit-wise commuting groups. based on arxiv:2305.11847. (Community)
README
[](https://opensource.org/licenses/Apache-2.0) [](https://arxiv.org/abs/2305.11847)
# dense_ev
**dense_ev** implements expectation value measurements in Qiskit using optimal
dense grouping [arXiv:quant-ph/2305.11847](https://arxiv.org/abs/2305.11847).
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):
```bash
python3 -m venv test-env && source ./test-env/bin/activate
```
```
Install from GitHub:
```bash
pip install git+ssh://[email protected]/atlytle/dense-ev.git
```
A test function comparing results using abelian and dense grouping
with exact results is available:
```python
from dense_ev.test_estimator import test_random
m = 3 # n_qubits
run_unit_tests(m)
```
### Qiskit version compatibility
**2024-01-07**: Support for dense Pauli grouping
in the Aer `Estimator` primitive merged to `main` branch.
See usage example below.
**2024-05-30**: Functionality using `Opflow` and other deprecated classes
has been removed. Package requirements changed from `qiskit < 0.43.0`
to `qiskit` (v1.0). Added `qiskit_aer` requirement.
## Usage
### Estimator support
The Aer implementation of `Estimator` is extended to incorporate
dense Pauli grouping, and can be invoked using the keyword
argument `abelian_grouping="DENSE"`. See `test_estimator.py`
for a more complete listing.
```python
from dense_ev.estimator_from_aer import Estimator
...
# Dense result.
nshots = 200000
approx = False
grouping = "DENSE"
run_options = {"shots": nshots, "seed": seed}
estimator = Estimator(
run_options=run_options, abelian_grouping=grouping, approximation=approx)
result_dense = estimator.run(state, H, shots=nshots).result().values
...
```