QAOA

This package is a flexible python implementation of the Quantum Approximate Optimization Algorithm /Quantum Alternating Operator ansatz (QAOA) aimed at researchers to readily test the performance of a new ansatz, a new classical optimizers, etc. By default it uses qiskit as a backend.

Install with pip install qaoa or pip install -e ..

Background

Given a cost function $$c: \lbrace 0, 1\rbrace^n \rightarrow \mathbb{R}$$ one defines a problem Hamiltonian $H_P$ through the action on computational basis states via

$$ H_P |x\rangle = c(x) |x\rangle,$$

which means that ground states minimize the cost function $c$. Given a parametrized ansatz $ | \gamma, \beta \rangle$, a classical optimizer is used to minimize the energy

$$ \langle \gamma, \beta | H_P | \gamma, \beta \rangle.$$

QAOA of depth $p$ consist of the following ansatz:

$$ |\gamma, \beta \rangle = \prod_{l=1}^p \left( U_M(\beta_l) U_P(\gamma_l)\right) | s\rangle, $$

where

• $U_P$ is a family of phase-separating operators,
• $U_M$ is a family of mixing operators, and
• $|s\rangle$ is a "simple" initial state.

In plain vanilla QAOA these have the form $U_M(\beta_l)=e^{-i\beta_l X^{\otimes n}}$, $U_P(\gamma_l)=e^{-i\gamma_l H_P}$, and the uniform superposition $| s \rangle = |+\rangle^{\otimes n}$ as initial state.

Create a custom ansatz

In order to create a custom QAOA ansatz, one needs to specify a problem, a mixer, and an initial state. These base classes have an abstract method def create_circuit:which needs to be implemented. The problem base class additionally has an abstract method def cost:.

This library already contains several standard implementations.

• The following problem cases are already available:
  • MaxCut
  • QUBO
  • Exact cover
  • Portfolio
• The following mixer cases are already available:
  • X-mixer
  • XY-mixer
  • Grover-mixer
• The following initial state cases are already available:
  • Plus
  • Statevector
  • Dicke

It is very easy to extend this list by providing an implementation of a circuit/cost of the base classes mentioned above. Feel free to fork the repo and create a pull request :-)

To make an ansatz for the MaxCut problem, the X-mixer and the initial state $|+\rangle^{\otimes n}$ one can create an instance like this:

qaoa = QAOA(
	initialstate=initialstates.Plus(),
	problem=problems.MaxCut(G="some networkx instance"),
	mixer=mixers.X()
)

Run optimization at depth $p$

For depth $p=1$ the expectation value can be sampled on an $n\times m$ Cartesian grid over the domain $[0,\gamma_\text{max}]\times[0,\beta_\text{max}]$ with:

qaoa.sample_cost_landscape()

Sampling high-dimensional target functions quickly becomes intractable for depth $p>1$. We therefore iteratively increase the depth. At each depth a local optimization algorithm, e.g. COBYLA, is used to find a local minimum. As initial guess the following is used:

• At depth $p=1$ initial parameters $(\gamma, \beta)$ are given by the lowest value of the sampled cost landscape.
• At depth $p>1$ initial parameters $(\gamma, \beta)$ are based on an interpolation-based heuristic of the optimal values at the previous depth.

Running this iterative local optimization to depth $p$ can be done by the following call:

qaoa.optimize(depth=p)

The function will call sample_cost_landscape if not already done, before iteratively increasing the depth.

Further parameters

QAOA supports the following keywords:

qaoa = QAOA( ...,
	backend= ,
	noisemodel= ,
	optimizer= ,
	precision= ,
	shots= ,
	cvar=
)

• backend: the backend to be used, defaults to Aer.get_backend("qasm_simulator")
• noisemodel: the noise model to be used, default to None,
• optimizer: a list of the optimizer to be used from qiskit-algorithms together with options, defaults to [COBYLA, {}],
• precision: sampel until a certain precision of the expectation value is reached based on $\text{error}=\frac{\text{variance}}{\sqrt{\text{shots}}}$, defaults to None,
• shots: number of shots to be used, defaults to 1024,
• cvar: the value for conditional value at risk (CVAR), defaults to 1, which are the standard moments.

Extract results

Once qaoa.optimize(depth=p) is run, one can extract, the expectation value, variance, and parametres for each depth $1\leq i \leq p$ by respectively calling:

qaoa.get_Exp(depth=i)
qaoa.get_Var(depth=i)
qaoa.get_gamma(depth=i)
qaoa.get_beta(depth=i)

Additionally, for each depth every time the loss function is called, the angles, expectation value, variance, maximum cost, minimum cost, and number of shots are stored in

qaoa.optimization_results[i]

Example usage

See examples here.