ewl 0.7.0

A simple Python library to simulate and execute EWL quantum circuits on IBM Q.

EWL

A simple Python library to simulate and execute EWL quantum circuits on IBM Q with symbolic calculations using SymPy.

Installation

pip install ewl

Examples

• Prisoner's dilemma
  • Two players
  • Three players
  • Payoff function 3D plot
  • Simulation with predefined gate noises

Usage

Initialization

This library uses SymPy to perform symbolic calculations. It's convenient to import it as sp and define frequently used constants for future use.

import sympy as sp

i = sp.I
pi = sp.pi
sqrt2 = sp.sqrt(2)

When using this library in Jupyter Notebook, call init_printing to enable pretty printing.

sp.init_printing()

EWL instance

First, you need to define the initial (preferably entangled) quantum state:

from sympy.physics.quantum.qubit import Qubit

psi = (Qubit('00') + i * Qubit('11')) / sqrt2

Then you need to define the players' strategies. Each strategy must be a unitary matrix as it represents a single-qubit quantum gate.

from ewl.parametrizations import *

alice = sp.Matrix([[1, 0], [0, 1]])

The library comes with a series of built-in parametrizations, including the original one from EWL paper as well as other 2- and 3 degrees of freedom parametrizations (see here).

bob = U_Eisert_Wilkens_Lewenstein(theta=pi / 2, phi=0)

At this point you can also use arbitrary symbols and compound expressions to generalize the analysis.

theta, gamma = sp.symbols('theta gamma', real=True)

charlie = U_Eisert_Wilkens_Lewenstein(theta=theta, phi=gamma / 2)

You also need to define the payoff matrix, possibly with symbols, for arbitrary number of players.

from sympy import Array

payoff_matrix = Array([
    [
        [3, 5],
        [0, 1],
    ],
    [
        [3, 0],
        [5, 1],
    ],
])

Finally, you can make an instance of quantum game in the EWL protocol by providing the initial quantum state, a list of players' strategies and the payoff matrix with corresponding shape. The library supports arbitrary number of players, although it works best for 2-player games.

from ewl import EWL

ewl = EWL(psi, [alice, bob], payoff_matrix)

Calculations

Based on the provided initial quantum state, the library automatically calculates the corresponding matrix of J and J^† gates.

ewl.J
ewl.J_H

Based on the players' strategies, the library also calculates the amplitudes of the result quantum state in the computational basis.

ewl.amplitudes()
ewl.amplitudes(simplify=False)

From the amplitudes one can easily derive the probabilities of possible game results. By default, the expressions are simplified using trigonometric identities. Make sure to enable real=True flag when defining real-valued symbols to allow for further simplification.

ewl.probs()
ewl.probs(simplify=False)

Finally, based on the payoff matrix and previously mentioned probabilities, the library calculates the payoff functions as symbolic expressions (possibly with parameters from the initial state and strategies).

ewl.payoff_function(player=0)  # first player
ewl.payoff_function(player=1, simplify=False)  # second player
ewl.payoff_function(player=None)  # payoff sum

For quantum games parametrized with exactly two symbols, it is possible to plot a three-dimensional graph of the payoff function.

from ewl.plotting import plot_payoff_function

plot_payoff_function(
    ewl, player=0,
    x=alpha, x_min=0, x_max=pi / 2,
    y=beta, y_min=0, y_max=pi / 2)

Parameters

Here's how you can list all symbols used either in the initial quantum state or in the players' strategies:

ewl.params

You can also substitute the symbols with specific values to obtain a non-parametrized instance of quantum game as new EWL instance:

ewl_fixed = ewl.fix(theta=0, gamma=pi / 2)

Qiskit integration

This library also integrates with Qiskit, allowing arbitrary quantum games in the EWL protocol to be executed on IBM Q devices. First, you need to load your credentials:

from qiskit import IBMQ

IBMQ.load_account()

When running locally, make sure to save the access token to disk first using IBMQ.save_account.

In order to access backend-specific features of EWL instance, first you need to convert it to EWL_IBMQ instance. Note that the input quantum game must be non-parametrized (cannot have any symbols).

from ewl.ibmq import EWL_IBMQ

ewl_ibmq = EWL_IBMQ(ewl_fixed)

You can also specify and apply noise model used in quantum simulation.

from qiskit.providers.aer.noise import NoiseModel, pauli_error, QuantumError, ReadoutError

p_error = 0.05
bit_flip = pauli_error([('X', p_error), ('I', 1 - p_error)])
phase_flip = pauli_error([('Z', p_error), ('I', 1 - p_error)])

noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(bit_flip, ['u1', 'u2', 'u3'])
noise_model.add_all_qubit_quantum_error(phase_flip, ['x'], [0])

ewl_ibmq = EWL_IBMQ(ewl_fixed, noise_model)

You can draw the original quantum circuit of quantum game in the EWL protocol.

ewl_ibmq.draw()

It is also possible to draw the quantum circuit transpiled for a specific backend.

ewl_ibmq.draw_transpiled(backend_name='ibmq_quito', optimization_level=3)

Here's how you can execute the quantum game on a specific statevector simulator:

ewl_ibmq.simulate_probs(backend_name='statevector_simulator')

You may also run the quantum circuit on QASM simulator and get histogram data of the experiment.

ewl_ibmq.simulate_counts(backend_name='qasm_simulator')

Finally, you can run the quantum game on a real quantum device:

ewl_ibmq.run(backend_name='ibmq_quito', optimization_level=3)