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

## 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


It is also necessary to define two unitary strategies that represent the classical strategies:

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

D = sp.Matrix([
[0, i],
[i, 0],
])


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

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).

from ewl.parametrizations import *

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.

payoff_matrix = sp.Array([
[
[3, 0],
[5, 1],
],
[
[3, 5],
[0, 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=psi, C=C, D=D, players=[alice, bob], payoff_matrix=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


You can also obtain a tuple containing the payoffs for each respective player.

ewl.payoffs()
ewl.payoffs(simplify=False)


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)


It is also possible to substitute specific players' strategies for further analysis of a certain case of the game.

ewl_CD = ewl.play(C, D)


### Mixed strategies

The library also supports quantum games with mixed strategies. You can define a mixed strategy by passing a list of probabilities and corresponding pure strategies:

from ewl.mixed import MixedStrategy

alice = MixedStrategy([(p1, U1), (p2, U2)])


Note: The library will check if the probabilities sum up to 1. To disable this check, pass check_sum=False.

Then you can create an instance of EWL quantum game with mixed strategies:

from ewl.mixed import MixedEWL

mixed_ewl = MixedEWL(psi=psi, C=C, D=D, players=[alice, bob], payoff_matrix=payoff_matrix)


The major difference is that you cannot call amplitudes method due to the fact that the state of quantum game with mixed strategies cannot be expressed as a vector in a general case. Instead, you can calculate the density matrix.

mixed_ewl.density_matrix()


Other properties and methods such as J, J_H, params, fix, probs and payoff_function behave similarly as for the regular EWL instance with pure strategies.

### Example games

The library comes with a series of built-in example games, in particular a few variants of Quantum Prisoner's Dilemma with different parametrizations from various articles (see here).

### 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



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

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()

ewl_ibmq = EWL_IBMQ(ewl_fixed, noise_model=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)


## Citation

@software{PythonEWL2022,
author = {Tomasz Zawadzki and Piotr Kotara},
title = {A Python tool for symbolic analysis of quantum games in EWL protocol with IBM Q integration},
howpublished = {\url{https://github.com/tomekzaw/ewl}},
}


## Project details

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