IBM

VQE - Variational Quantum Eigensolver using Qiskit

Description

The Variational Quantum Eigensolver (VQE) solves combinatorial optimization problems by approximating the solution:

For a given problem represented as a cost Hamiltonian we apply a classical/quantum hybrid algorithm to find the solution. The VQE solves the problem by iteratively applying a linear transformation (variational form) on the cost Hamiltonian and optimizing the parameters of the transformation using a classical optimizer.

For further information see [VQE Qiskit] (https://qiskit.org/documentation/stubs/qiskit.algorithms.VQE.html)

Parameters

use_qpu: bool

If True, the algorithm will run on the QPU. Default: False

backend_name: str

Defines backend simulator for the algorithm. To see, which backends are available, please check your ibm account. It usually starts with 'ibm_', e.g., 'ibm_osaka'. 'least_busy' is also allowed. In this case, the least busy solver will be used. Default: 'basic_simulator'

hub: str

The IBM Quantum hub. Default: 'ibm-q'

group: str

The IBM Quantum group. Default: 'open'

project: str

The IBM Quantum project. Default: 'main'

min_num_vars: int

Default: 1

Available

Parameters

{
  "use_qpu": false,
  "backend_name": "basic_simulator",
  "hub": "ibm-q",
  "group": "open",
  "project": "main"
}

QPU Provider

ibm

QAOA - Quantum Approximate Optimization Algorithm using Qiskit

Description

The Quantum Approximate Optimization Algorithm (QAOA) solves combinatorial optimization problems by approximating the solution:

For a given problem represented as a cost Hamiltonian we formulate two unitary operators. The QAOA solves the problem by iteratively applying the two unitary operators on the cost Hamiltonian for a number of steps p. The angles for the unitary operators are iteratively updated by measuring the state after applying these (like in VQE).

For further information see QAOA Qiskit.

Parameters

use_qpu: bool

If True, the algorithm will run on the QPU. Default: False

backend_name: str

Defines backend simulator for the algorithm. To see, which backends are available, please check your ibm account. It usually starts with 'ibm_', e.g., 'ibm_osaka'. 'least_busy' is also allowed. In this case, the least busy solver will be used. Default: 'basic_simulator'

hub: str

The IBM Quantum hub. Default: 'ibm-q'

group: str

The IBM Quantum group. Default: 'open'

project: str

The IBM Quantum project. Default: 'main'

min_num_vars: int

Default: 1

Available

Parameters

{
  "use_qpu": false,
  "backend_name": "basic_simulator",
  "hub": "ibm-q",
  "group": "open",
  "project": "main"
}

QPU Provider

ibm

RQAOA - Recursive Quantum Approximate Optimization Algorithm using Qiskit

Description

The Quantum Approximate Optimization Algorithm (QAOA) solves combinatorial optimization problems by approximating the solution:

For a given problem represented as a cost Hamiltonian we formulate two unitary operators. The QAOA solves the problem by iteratively applying the two unitary operators on the cost Hamiltonian for a number of steps p. The angles for the unitary operators are iteratively updated by measuring the state after applying these (like in VQE).

The problem is solved in a recursive fashion.

For further information see QAOA Qiskit.

Parameters

use_qpu: bool

If True, the algorithm will run on the QPU. Default: False

backend_name: str

Defines backend simulator for the algorithm. To see, which backends are available, please check your ibm account. It usually starts with 'ibm_', e.g., 'ibm_osaka'. 'least_busy' is also allowed. In this case, the least busy solver will be used. Default: 'basic_simulator'

hub: str

The IBM Quantum hub. Default: 'ibm-q'

group: str

The IBM Quantum group. Default: 'open'

project: str

The IBM Quantum project. Default: 'main'

min_num_vars: int

Default: 1

Available

Parameters

{
  "use_qpu": False,
  "backend_name": "basic_simulator",
  "hub": "ibm-q",
  "group": "open",
  "project": "main"
}

QPU Provider

ibm