https://github.com/JoelHBierman/SSVQE

An implementation of the SSVQE algorithm in Qiskit
https://github.com/JoelHBierman/SSVQE

Last synced: 17 days ago
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An implementation of the SSVQE algorithm in Qiskit

• Host: GitHub
• URL: https://github.com/JoelHBierman/SSVQE
• Owner: JoelHBierman
• License: apache-2.0
• Created: 2023-08-29T20:08:17.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2023-12-10T17:08:31.000Z (7 months ago)
• Last Synced: 2024-04-18T11:27:56.639Z (2 months ago)
• Language: Python
• Size: 48.8 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-qiskit - SSVQE - The ssvqe algorithm (arxiv:1810.09434) is a generalization of vqe to find low-lying eigenstates of a hermitian operator. this specific implementation of ssvqe carries out one optimization procedure using weights. (Community)

README

        
# SSVQE

This repo contains an implementation of the SSVQE algorithm (see https://arxiv.org/abs/1810.09434) in Qiskit. SSVQE is a generalization of VQE which takes a set of mutually orthogonal states, applies the same parameterized ansatz circuit to all of them, then minimizes a weighted sum of the expectation values of the operator with respect to these states. Because inner products of states are invariant under unitary transformations, the set of states remains orthogonal under ideal conditions and thus the global minimum corresponds to the low-lying eigenstates. This implementation may be installed by cloning the repo and installing with pip. Here is a simple example of how one may use this implementation:

```

from qiskit import QuantumCircuit

from qiskit.quantum_info import SparsePauliOp

from qiskit.primitives import Estimator

from qiskit.circuit.library import RealAmplitudes

from qiskit.algorithms.optimizers import SPSA

from ssvqe import SSVQE

operator = SparsePauliOp(['ZZ'])

input_states = [QuantumCircuit(2), QuantumCircuit(2)]

input_states[0].x(0)

input_states[1].x(1)

ssvqe_instance = SSVQE(k=2,

                            estimator=Estimator(),

                            optimizer=SPSA(),

                            ansatz=RealAmplitudes(2),

                            initial_states=input_states)

result = ssvqe_instance.compute_eigenvalues(operator)

```

This will find the lowest eigenvalues of the `ZZ` operator and the resulting optimized parameters will encode the corresponding eigenstates.