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QuantumAwesomeness.py
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QuantumAwesomeness.py
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# first, some tools we'll need from this directory
from devices import * # info on supported devices
from devicePrep import *
try:
import mwmatching as mw # perfect matching
except:
pass
# other tools
import random, numpy, math, time, copy, os
from IPython.display import clear_output
import networkx as nx
import matplotlib.pyplot as plt
from itertools import product
import warnings
warnings.filterwarnings('ignore')
path = os.path.dirname(os.path.abspath(__file__))
def importSDK ( device ):
# *This function contains SDK specific code.*
#
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# Process:
# * The SDK associated with this device is imported.
#
# Output:
# * Nothing returned, but global variables required by the SDKs are defined
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
if sdk in ["QISKit","ManualQISKit"]:
global ClassicalRegister, QuantumRegister, QuantumCircuit, execute, register, available_backends, get_backend
from qiskit import ClassicalRegister, QuantumRegister
from qiskit import QuantumCircuit, execute
from qiskit import register, available_backends, get_backend
try:
import Qconfig
qx_config = {
"APItoken": Qconfig.APItoken,
"url": Qconfig.config['url']}
register(qx_config['APItoken'], qx_config['url'])
except:
pass
elif sdk=="ProjectQ":
global projectq, H, Measure, CNOT, C, Z, Rx, Ry
import projectq
from projectq.ops import H, Measure, CNOT, C, Z, Rx, Ry
elif sdk=="Forest":
global Program, api, I, H, CNOT, CZ, RX, RY
from pyquil.quil import Program
import pyquil.api as api
from pyquil.gates import I, H, CNOT, CZ, RX, RY
elif sdk=="Cirq":
global GridQubit, CNOT, CZ, X, Y, Circuit, H, measure, google
from cirq import GridQubit, CNOT, CZ, X, Y, Circuit, H, measure, google
def initializeQuantumProgram ( device, sim ):
# *This function contains SDK specific code.*
#
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *sim* - Boolean denoting whether this is a simulated run
# Process:
# * Initializes everything required by the SDK for the quantum program. The details depend on which SDK is used.
#
# Output:
# * *q* - Register of qubits (used by QISKit, ProjectQ and Circ).
# * *c* - Register of classical bits (used by QISKit).
# * *engine* - Class required to create programs (used by ProjectQ and Forest).
# * *script* - The quantum program (used by QISKit, Forest and Circ).
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
importSDK ( device )
if sdk in ["QISKit","ManualQISKit"]:
engine = None
q = QuantumRegister(num)
c = ClassicalRegister(num)
script = QuantumCircuit(q, c)
elif sdk=="ProjectQ":
engine = projectq.MainEngine()
q = engine.allocate_qureg( num )
c = None
script = None
elif sdk=="Forest":
if sim:
engine = api.QVMConnection(use_queue=True)
else:
engine = api.QPUConnection(device)
script = Program()
q = range(num)
c = range(num)
elif sdk=="Cirq":
q = []
for qubit in range(num):
q.append( GridQubit( pos[qubit][0], pos[qubit][1] ) )
c = None
engine = None
script = Circuit.from_ops()
return q, c, engine, script
def implementGate (device, gate, qubit, script, frac = None ):
# *This function contains SDK specific code.*
#
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *gate* - String that specifies gate type. Should be 'X', 'Y' or 'XX' rotation, or 'finish'.
# * *qubit* - Qubit, list of two qubits or qubit register on which the gate is applied.
# * *script* - Used to store the quantum program in some SDKs
# * *frac=0* - Fraction of pi for which an X rotation is applied. Not required for gate of type 'finish'.
#
# Process:
# * For gates of type 'X', 'Y' and 'XX', the gate $U = \exp(-i \,\times\, gate \,\times\, frac )$ is implemented on the qubit or pair of qubits in *qubit*.
# * *gate='Finish'* implements the measurement command on the qubit register required for ProjectQ to not complain.
#
# Output:
# * None are returned, but modifications are made to the objects that contain the quantum program.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
if sdk in ["QISKit","ManualQISKit"]:
if gate=='X':
script.u3(frac * math.pi, -math.pi/2, math.pi/2, qubit )
elif gate=='Y': # a Y axis rotation
script.u3(frac * math.pi, 0,0, qubit )
elif gate=='XX':
if entangleType=='CX':
script.cx( qubit[0], qubit[1] )
script.u3(frac * math.pi, -math.pi/2, math.pi/2, qubit[0] )
script.cx( qubit[0], qubit[1] )
elif entangleType=='CZ':
script.h( qubit[1] )
script.cz( qubit[0], qubit[1] )
script.u3(frac * math.pi, -math.pi/2, math.pi/2, qubit[0] )
script.cz( qubit[0], qubit[1] )
script.h( qubit[1] )
else:
print("Support for this is yet to be added")
elif sdk=="ProjectQ":
if gate=='X':
Rx( frac * math.pi ) | qubit
elif gate=='Y': # a Y axis rotation
Ry( frac * math.pi ) | qubit
elif gate=='XX':
if entangleType=='CX':
CNOT | ( qubit[0], qubit[1] )
Rx( frac * math.pi ) | qubit[0]
CNOT | ( qubit[0], qubit[1] )
elif entangleType=='CZ':
H | qubit[1]
C(Z) | ( qubit[0], qubit[1] )
Rx( frac * math.pi ) | qubit[0]
C(Z) | ( qubit[0], qubit[1] )
H | qubit[1]
else:
print("Support for this is yet to be added")
elif gate=='finish':
Measure | qubit
elif sdk=="Forest":
if gate=='X':
if qubit in pos.keys(): # only if qubit is active
script.inst( RX ( frac * math.pi, qubit ) )
elif gate=='Y': # a Y axis rotation
if qubit in pos.keys(): # only if qubit is active
script.inst( RY ( frac * math.pi, qubit ) )
elif gate=='XX':
if entangleType=='CX':
script.inst( CNOT( qubit[0], qubit[1] ) )
script.inst( RX ( frac * math.pi, qubit[0] ) )
script.inst( CNOT( qubit[0], qubit[1] ) )
elif entangleType=='CZ':
script.inst( H ( qubit[1] ) )
script.inst( CZ( qubit[0], qubit[1] ) )
script.inst( RX ( frac * math.pi, qubit[0] ) )
script.inst( CZ( qubit[0], qubit[1] ) )
script.inst( H ( qubit[1] ) )
elif entangleType=='none':
script.inst( RX ( frac * math.pi, qubit[0] ) )
script.inst( RX ( frac * math.pi, qubit[1] ) )
else:
print("Support for this is yet to be added")
elif sdk=="Cirq":
if gate=='X':
if qubit in pos.keys(): # only if qubit is active
script.append( X(qubit)**frac )
elif gate=='Y': # a Y axis rotation
if qubit in pos.keys(): # only if qubit is active
script.append( Y(qubit)**frac )
elif gate=='XX':
if entangleType=='CX':
script.append( CNOT(qubit[0],qubit[1]) )
script.append( X(qubit[0])**frac )
script.append( CNOT(qubit[0],qubit[1]) )
elif entangleType=='CZ':
script.append( H(qubit[1]) )
script.append( CZ(qubit[0],qubit[1]) )
script.append( X(qubit[0])**frac )
script.append( CZ(qubit[0],qubit[1]) )
script.append( H(qubit[1]) )
else:
print("Support for this is yet to be added")
def resultsLoad ( fileType, move, shots, sim, device ) :
# Input:
# * *fileType* - String describing type of file to load.
# * *move* - String describing the way moves were chosen for the results to be loaded.
# * *shots* - Number of shots used in the results to be loaded.
# * *sim* - Boolean denoting whether a simulator was used for the results to be loaded.
# * *device* - String specifying the device on which the game is played.
#
# Process:
# * A filename is created using the details given in the input. This file, which will contain and array of arrays, is then loaded, evalulated and stored as an which will contain and array of arrays.
# If the file doesn't exist, the process will fail and throw and exception. This is a fatal error, so no exception handling is used.
#
# Output:
# * *samples* - Array of arrays of whatever it was the file contained.
filename = 'move='+move+'_shots=' + str(shots) + '_sim=' + str(sim) + '.txt'
saveFile = open(path+'/results/' + device + '/'+fileType+'_'+filename)
sampleStrings = saveFile.readlines()
saveFile.close()
samples = []
for sampleString in sampleStrings:
samples.append( eval( sampleString ) )
return samples
def getResults ( device, sim, shots, q, c, engine, script ):
# *This function contains SDK specific code.*
#
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *q* - Register of qubits (used in some SDKs).
# * *c* - Register of classical bits (used in some SDKs).
# * *engine* - Class required to create programs (used in some SDKs).
# * *script* - The quantum program (used in some SDKs).
#
# Process:
# * This function sends the quantum program to the desired backend to be run, and obtains results.
#
# Output:
# * *resultsRaw* - A dictionary whose keys are the bit strings obtained as results, and the values are the fraction of shots for which they occurred.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
if sdk=="QISKit":
# pick the right backend
if sim:
backend = get_backend('local_qasm_simulator')
else:
backend = get_backend(device)
# add measurement for all qubits
for n in range(num):
script.measure( q[n], c[n] )
# execute job
noResults = True
while noResults:
try: # try to run, and wait if it fails
if not sim:
print('Status of device:',backend.status)
job = execute(script, backend, shots=shots, skip_translation=True)
resultsVeryRaw = job.result().get_counts()
noResults = False
except Exception as e:
print(e)
print("Job failed. We'll wait and try again")
time.sleep(600)
# invert order of the bit string and turn into probs
resultsRaw = {}
for string in resultsVeryRaw.keys():
invertedString = string[::-1]
resultsRaw[ invertedString ] = resultsVeryRaw[string]/shots
elif sdk=="ManualQISKit":
# add measurement for all qubits
for n in range(num):
script.measure( q[n], c[n] )
qasm = engine.get_qasm("script")
input("\nHere is a QASM representation of the circuit you need to run\n"+qasm)
input_data = input("Whatever you enter into the box below will go into the results file.\nObviously, it is best if you put the results. But you can also put a job ID that can be replaced with the results later.\n")
try: # if the input successfully evaluates, we treat it as data
resultsRaw = eval(input_data)
except: # otherwise we treat it as a job ID
resultsRaw = input_data
elif sdk=="ProjectQ":
engine.flush()
# list of bit strings
strings = [''.join(x) for x in product('01', repeat=num)]
# get prob for each bit string to make resultsRaw
resultsRaw = {}
for string in strings:
resultsRaw[ string ] = engine.backend.get_probability( string, q )
elif sdk=="Forest":
# get list of active (and therefore plotted) qubits
qubits_active = list(pos.keys())
# execute job
noResults = True
while noResults:
try: # try to run, and wait for 5 mins if it fails
resultsVeryRaw = engine.run_and_measure(script, qubits_active, trials=shots)
noResults = False
except Exception as e:
#print(e)
print("\nJob failed. We'll wait and try again.\n")
time.sleep(300)
# convert them the correct form
resultsRaw = {}
for sample in resultsVeryRaw:
bitString = ""
disabled_so_far = 0
for qubit in range(num):
if qubit in qubits_active:
bitString += str(sample[qubit-disabled_so_far])
else:
bitString += "0" # fake result for dead qubit
disabled_so_far += 1
if bitString not in resultsRaw.keys():
resultsRaw[bitString] = 0
resultsRaw[bitString] += 1/shots
elif sdk=="Cirq":
# add measurement for all qubits
for qubit in range(num):
script.append( measure(q[qubit],key=qubit) )
if sim:
backend = google.XmonSimulator()
elif device=='Foxtail':
backend = google.Foxtail
elif device=='Bristlecone':
backend = google.Bristlecone
resultsExtremelyRaw = backend.run(script, repetitions=shots)
resultsVeryRaw = []
for qubit in range(num):
resultsVeryRaw.append( resultsExtremelyRaw.measurements[qubit][:, 0] )
resultsRaw = {}
for shot in range(shots):
bitString = ""
for qubit in range(num):
if resultsVeryRaw[qubit][shot]:
bitString += '1'
else:
bitString += '0'
if bitString not in resultsRaw.keys():
resultsRaw[bitString] = 0
resultsRaw[bitString] += 1/shots
return resultsRaw
def processResults ( resultsRaw, num, pairs, sim, shots ):
# Input:
# * *resultsRaw* - String specifying the device on which the game is played. Details about the device will be obtained using getLayout.
# * *num* - The number of qubits in the device.
# * *pairs* - A dictionary of pairs of qubits for which an entagling gate is possible. The key is a string which serves as the name of the pair. The value is a two element list with the qubit numbers of the two qubits in the pair. For controlled-NOTs, the control qubit is listed first.
# * *sim* - Boolean denoting whether a simulator was used.
# * *shots* - Number of shots used for statistics.
#
# Process:
# * This function sends the quantum program to the desired backend to be run, and obtains results.
#
# Output:
# * *oneProb* - A list with an entry for each qubit. Each entry is the fraction of samples for which the measurement of that qubit returns *1*.
# * *sameProb* - A dictionary with pair names as keys, and probability that the two qubits each pair give the same results as values.
# * *results* - If results are not from a simulator, this is just resultsRaw. If they are, it is assumed that the simulated effectively gave results with no statistical noise, so a sampling process is used to simulate the effect of the required number of shots.
oneProb = [0]*num
sameProb = {p: 0 for p in pairs}
if type(resultsRaw) is dict: # try to process only if it is a dict (and so not if a job id)
strings = list(resultsRaw.keys())
if sim==True:
# sample from this prob dist shots times to get results
results = {}
for string in strings:
results[string] = 0
for shot in range(shots):
j = numpy.random.choice( len(strings), p=list(resultsRaw.values()) )
results[strings[j]] += 1/shots
else:
results = resultsRaw
# determine the fraction of results that came out as 1 (instead of 0) for each qubit
for bitString in strings:
for v in range(num):
if (bitString[v]=="1"):
oneProb[v] += results[bitString]
for bitString in strings:
for p in pairs:
if bitString[pairs[p][0]]==bitString[pairs[p][1]]:
sameProb[p] += results[bitString]
else:
results = resultsRaw
return oneProb, sameProb, results
def printM ( string, move ):
# If *move=M*, this is just *print()*. Otherwise it does nothing.
if move=="M":
print(string)
def entangle( device, move, shots, sim, gates, conjugates ):
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *move* - String describing the way moves were chosen when creating the circuit.
# * *shots* - Number of shots to be taken.
# * *sim* - Boolean denoting whether a simulator will be used.
# * *gates* - Entangling gates applied so far. Each round of the game corresponds to two 'slices'. *gates* is a list with a dictionary for each slice. The dictionary has pairs of qubits as keys and fractions of pi defining a corresponding entangling gate as values.
# * *conjugates* - List of single qubit gates to conjugate entangling gates of previous rounds. Each is specified by a two element list. First is a string specifying the rotation axis ('X' or 'Y'), and the second specifies the fraction of pi for the rotation.
#
# Process:
# * Quantum circuit is created and run given the details (device, gates, etc) provided by the input. The results are then processed to give the final output.
#
# Output:
# * *oneProb*, *sameProb* and *results* - See processResults() for explanation.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
q, c, engine, script = initializeQuantumProgram(device,sim)
# apply all gates
# gates has two entries for each round, except for the current round which has only one
rounds = int( (len(gates)+1)/2 )
# loop over past rounds and apply the required gates
for r in range(rounds-1):
# do the first part of conjugation (the inverse)
for n in range(num):
implementGate ( device, conjugates[r][n][0], q[n], script, frac=-conjugates[r][n][1] )
# get the sets of gates that create and (attempt to) remove the puzzle for this round
gates_create = gates[2*r]
gates_remove = gates[2*r+1]
# determine which pairs are for both, and which are unique
pairs_both = list( set(gates_create.keys()) & set(gates_remove.keys()) )
pairs_create = list( set(gates_create.keys()) - set(gates_remove.keys()) )
pairs_remove = list( set(gates_remove.keys()) - set(gates_create.keys()) )
# then do the exp[ i XX * frac ] gates accordingly
for p in pairs_both:
#print([r,p,gates_create[p]+gates_remove[p]] )
implementGate ( device, "XX", [ q[pairs[p][0]], q[pairs[p][1]] ], script, frac=(gates_create[p]+gates_remove[p]) )
for p in pairs_create:
#print([r,p,gates_create[p]] )
implementGate ( device, "XX", [ q[pairs[p][0]], q[pairs[p][1]] ], script, frac=(gates_create[p]) )
for p in pairs_remove:
#print([r,p,gates_remove[p]] )
implementGate ( device, "XX", [ q[pairs[p][0]], q[pairs[p][1]] ], script, frac=(gates_remove[p]) )
# do the second part of conjugation
for n in range(num):
implementGate ( device, conjugates[r][n][0], q[n], script, frac=conjugates[r][n][1] )
# then the same for the current round (only needs the exp[ i XX * (frac - frac_inverse) ] )
r = rounds-1
for p in gates[2*r].keys():
implementGate ( device, "XX", [ q[ pairs[p][0] ], q[ pairs[p][1] ] ], script, frac=gates[2*r][p] )
resultsRaw = getResults( device, sim, shots, q, c, engine, script )
oneProb, sameProb, results = processResults ( resultsRaw, num, pairs, sim, shots )
implementGate ( device, "finish", q, script )
return oneProb, sameProb, results
def calculateEntanglement( oneProb ):
# Input:
# * *oneProb* - Float representing the fraction of samples for which the measurement of a qubit returns *1*.
#
# Process:
# * Calculates the number that will be shown to the player, based on oneProb. This should be zero when oneProb is zero, 1 when oneProb is 0.5, and monotonic.
# This is done by first calculating the frac that would result in such a oneProb (oneProb=0 ==> frac=0, oneProb=0.5 ==> frac=1/2, oneProb=1 ==> frac=1), and then doubling it. Since the result can exceed 1 in general (though only due to spurious effects that result in oneprob>0.5), it is capped by 1 before being returned.
# This quantity is called the 'entanglement' because early versions used the mixedness
# E = 1-2*abs( 0.5-oneProb )
# which is a measure of how entangled a qubit is with the rest of the universe. The frac based measure is now used instead such that the resulting values have a more uniform spread.
#
# Output:
# * *E* - As described above.
E = ( 2 * calculateFrac( oneProb ) )
return min( E, 1)
def calculateFrac ( oneProb ):
# Input:
# * *oneProb* - Float representing the fraction of samples for which the measurement of a qubit returns *1*.
#
# Process:
# * Calculates the fraction of pi for an X rotation which would result in the given value of oneProb.
# Prob(1) = sin(frac*pi/2)^2
# therefore frac = asin(sqrt(oneProb)) *2 /pi
#
# Output:
# * *frac* - As described above.
oneProb = max(0,oneProb)
oneProb = min(1,oneProb)
frac = math.asin(math.sqrt( oneProb )) * 2 / math.pi
return frac
def calculateFuzz ( oneProb, pairs, matchingPairs ):
# Input:
# * *oneProb* - A list with an entry for each qubit. Each entry is the fraction of samples for which the measurement of that qubit returns *1*.
# * *pairs* - A dictionary of pairs of qubits for which an entagling gate is possible. The key is a string which serves as the name of the pair. The value is a two element list with the qubit numbers of the two qubits in the pair. For controlled-NOTs, the control qubit is listed first.
# * *matchingPairs* - The pairing of qubits in the current round.
#
# Process:
# * The two qubits of the same pair should have the same oneProb value. If they don't, it is because of fuzz.
# The fuzz is therefore quantified by the average difference between these values.
#
# Output:
# * *fuzzAv* - As described above.
fuzzAv = 0
for p in matchingPairs:
fuzzAv += abs( oneProb[pairs[p][0]] - oneProb[pairs[p][1]] )/len(matchingPairs)
return fuzzAv
def calculateEntropy ( probs ):
# Input:
# * *probs* - Array of probabilities. They are assumed to sum to 1, but this is not checked.
#
# Process:
# * The Shannon entropy, H, of the probability distribution is calculated
#
# Output:
# * *H* - As described above.
H = 0
for prob in probs:
if prob>0:
H -= prob * math.log(prob,2)
return H
def calculateExpect ( probs ):
# Input:
# * *probs* - Array of probabilities.
#
# Process:
# * For each probability, the expectation value is calculated. The probabilities are takem to be the probability of a value -1 value, with 1-p representing the probability of +1.
#
# Output:
# * *expect* - Corresponding array of expectation values.
expect = []
for p in probs:
expect.append( 1-2*p)
return expect
def calculateMutual ( oneProb, sameProb, pairs ):
# Input:
# * *oneProb* - A list with an entry for each qubit. Each entry is the fraction of samples for which the measurement of that qubit returns *1*.
# * *sameProb* - A dictionary with pair names as keys, and probability that the two qubits each pair give the same results as values.
# * *pairs* - A dictionary of pairs of qubits for which an entagling gate is possible. The key is a string which serves as the name of the pair. The value is a two element list with the qubit numbers of the two qubits in the pair. For controlled-NOTs, the control qubit is listed first.
#
# Process:
# * For each pair, the (classical) mutual information for the measurement results of the two qubits is calculated. This is done using oneProbs and sameProbs, which is a bit of a pain. But this information is sufficient to calculate the probability for the results '00', '01', '10' and '11' for the two qubits of each pair, which the probability distrubution required to calculate the mutual information.
#
# Output:
# * *I* - Dictionary with pair names as keys and corresponding values of the mutual information as values.
I = {}
for p in sameProb.keys():
p0 = oneProb[pairs[p][0]]
p1 = oneProb[pairs[p][1]]
expect = calculateExpect( [ p0, p1, 1-sameProb[p] ] )
prob = [0]*4
prob[0] = ( 1 + expect[0] + expect[1] + expect[2] )/4
prob[1] = ( 1 - expect[0] + expect[1] - expect[2] )/4
prob[2] = ( 1 + expect[0] - expect[1] - expect[2] )/4
prob[3] = ( 1 - expect[0] - expect[1] + expect[2] )/4
I[p] = calculateEntropy( [ 1-p0, p0 ] ) + calculateEntropy( [ 1-p1, p1 ] ) - calculateEntropy( prob )
if I[p]>1e-3:
I[p] = I[p] / min( calculateEntropy( [ 1-p0, p0 ] ) , calculateEntropy( [ 1-p1, p1 ] ) )
return I
def printPuzzle ( device, oneProb, move, ascii=False ):
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *oneProb* - A list with an entry for each qubit. Each entry is the fraction of samples for which the measurement of that qubit returns 1.
# * *move* - String describing the way moves are chosen.
# * *ascii* - Boolean to convey whether the image should be purely ascii.
#
# Process:
# * The contents of *oneProb* contains some basic clues about the circuit that has been performed. It is the player's job to use those clues to guess the circuit. This means we have to print *oneProb* to screen. In order to make the game a pleasant experience and help build intuition about the device, this is done visually. The networkx package is used to visualize the layout of the qubits, and the oneProb information is conveyed using colour. This is done only when a player is manually playing, and so when move='M'.
# * An altenative visualization has been partially implemented. This would use only ascii.
#
# Output:
# * None returned, but the above described image is printed to screen.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
if move=="M":
if ascii:
def change_char (string, char, pos):
return string[:pos] + char + string[(pos+len(char)):]
lines = 5
length = 10
def get_x (X):
return int( length * X )
def get_y (Y):
return H-1 - int( lines * Y )
W = length*(area[0]-1)+1
H = lines*(area[1]-1)+1
plot = [ "░"*(W+4) for _ in range(H) ]
for pair in pairs:
x = get_x( (pos[pairs[pair][0]][0]+pos[pairs[pair][1]][0])/2 )
y = get_y( (pos[pairs[pair][0]][1]+pos[pairs[pair][1]][1])/2 )
plot[y] = change_char(plot[y],pair,x)
xx = [0]*2
yy = [0]*2
for j in range(2):
xx[j] = get_x( pos[pairs[pair][j]][0] )
yy[j] = get_y( pos[pairs[pair][j]][1] )
for p in range(length):
x = int( xx[0] + (p/length)*(xx[1]-xx[0]) )
y = int( yy[0] + (p/length)*(yy[1]-yy[0]) )
plot[y] = change_char(plot[y]," ",x)
for qubit in pos:
x = get_x( pos[qubit][0] )
y = get_y( pos[qubit][1] )
num = "("+str(int(100*oneProb[qubit])) +")"
plot[y] = change_char(plot[y],num,x)
print("░"*(W+8))
for line in plot:
print("░░░░"+line)
print("░"*(W+8))
else:
# create a graph with qubits as vertices and possible entangling gates as edges
G=nx.Graph()
for p in pairs:
if p[0:4]!='fake':
G.add_edge(pairs[p][0],pairs[p][1])
for p in pairs:
if p[0:4]!='fake':
G.add_edge(pairs[p][0],p)
G.add_edge(pairs[p][1],p)
pos[p] = [(pos[pairs[p][0]][dim] + pos[pairs[p][1]][dim])/2 for dim in range(2)]
# colour and label the edges with the oneProb data
colors = []
sizes = []
labels = {}
for node in G:
if type(node)!=str:
if (oneProb[node]>1): # if oneProb is out of bounds (due to this node having already been selected) make it grey
colors.append( (0.5,0.5,0.5) )
else: # otherwise it is on the spectrum between red and blue
E = calculateEntanglement( oneProb[node] )
colors.append( (1-E,0,E) )
sizes.append( 3000 )
if oneProb[node]>1:
labels[node] = ""
elif oneProb[node]==0.5:
labels[node] = "99"
else:
labels[node] = "%.0f" % ( 100 * ( E ) )
else:
colors.append( "black" )
sizes.append( 1000 )
labels[node] = node
# show it
if area[0]>2*area[1]:
ratio = 0.65
else:
ratio = 1
plt.figure(2,figsize=(2*area[0],2*ratio*area[1]))
nx.draw(G, pos, node_color = colors, node_size = sizes, labels = labels, with_labels = True,
font_color ='w', font_size = 22.5)
plt.show()
def calculateFracDifference (frac1, frac2):
# Input:
# * *frac1*, *frac2* - Two values of frac
#
# Process:
# * Determine the minimum difference between the two, accounting for the fact that frac=0 and frac=2 are equivalent. Note that his means that the
#
# Output:
# * *matchingPairs* - A list of the names of a random set of disjoint pairs included in the matching.
delta = max(frac1,frac2) - min(frac1,frac2)
delta = min( delta, 1-delta )
return delta
def getDisjointPairs ( pairs, oneProb, weight ):
# Input:
# * *pairs* - A dictionary with names of pairs as keys and lists of the two qubits of each pair as values
# * *oneProb* - A list with an entry for each qubit. Each entry is the fraction of samples for which the measurement of that qubit returns 1.
# * *weight* - dictionary with pair names as keys and a weight assigned to each pair as the corresponding values.
#
# Process:
# * A minimum weight perfect matching of the qubits is performed, using the possible pairing and weights provided. If weights are not given, but oneProbs are, the weights are calculated from the oneProbs. If oneProbs aren't given either, the weights are chosen randomly to generate a random pairing.
#
# Output:
# * *matchingPairs* - A list of the names of a random set of disjoint pairs included in the matching.
if not weight:
for p in pairs.keys():
if oneProb:
weight[p] = -calculateFracDifference( calculateFrac( oneProb[ pairs[p][0] ] ) , calculateFrac( oneProb[ pairs[p][1] ] ) )
else:
weight[p] = random.randint(0,100)
edges = []
for p in pairs.keys():
edges.append( ( pairs[p][0], pairs[p][1], weight[p] ) )
# match[j] = k means that edge j and k are matched
match = mw.maxWeightMatching(edges, maxcardinality=True)
# get a list of the pair names for each pair in the matching (not including fakes)
matchingPairs = []
for v in range(len(match)):
for p in pairs.keys():
if pairs[p]==[v,match[v]] and p[0:4]!='fake' :
matchingPairs.append(p)
return matchingPairs
def runGame ( device, move, shots, sim, maxScore=None, dataNeeded=True, cleanup=False, game=None, ascii=False):
# Input:
# * *device* - String specifying the device on which the game is played.
# Details about the device will be obtained using getLayout.
# * *move* - String describing the way moves are chosen.
# * *shots* - Number of shots to be used for statistics.
# * *sim* - Boolean for whether the simulator is to be used.
# * *maxScore* - maximum number of rounds to run the game for
# * *dataNeeded* - Boolean determining whether game need to obtain new data, or will run on old data
# * *cleanup* - Boolean determining whether error mitigation post-processing is used
# * *game* - Integer identifiying a specific game to play from a file (can only be True if dataNeeded=True)
# * *ascii* - Boolean to convey whether the image presented to the player should be purely ascii.
#
# Process:
# * Runs the game! Done either by loading up saved data, or running a new instance.
#
# Output:
# * *gates* - Entangling gates applied so far. Each round of the game corresponds to two 'slices'. *gates* is a list with a dictionary for each slice. The dictionary has pairs of qubits as keys and fractions of pi defining a corresponding entangling gate as values.
# * *conjugates* - List of single qubit gates to conjugate entangling gates of previous rounds. Each is specified by a two element list. First is a string specifying the rotation axis ('X' or 'Y'), and the second specifies the fraction of pi for the rotation.
# * *oneProbs*: Array of oneProb arrays (see processResults() for explanation of these), with an element for each round of the game.
# * *sameProbs*: Array of sameProb arrays (see processResults() for explanation of these), with an element for each round of the game.
# * *resultsDicts*: Array of results arrays (see processResults() for explanation of these), with an element for each round of the game.
num, area, entangleType, pairs, pos, example, sdk, runs = getLayout(device)
gates = []
conjugates = []
oneProbs = []
sameProbs = []
resultsDicts = []
# if we are running off data, load up oneProbs for a move='C' run and see what the right answers are
if dataNeeded==False:
oneProbSamples = resultsLoad ( 'oneProbs', 'C', shots, sim, device )
sameProbSamples = resultsLoad ( 'sameProbs', 'C', shots, sim, device )
gateSamples = resultsLoad ( 'gates', 'C', shots, sim, device )
if maxScore is None: # if a maxScore is not given, use the value from the first sample
maxScore = len( oneProbSamples[ 0 ] )
if cleanup:
cleaner = getCleaningProfile ( device, move, shots, sim, num, maxScore, gritty=True )
samples = len(oneProbSamples) # find out how many samples there are
# choose a game randomly if a specific one was not requested
if game is None:
game = random.randint( 0, samples-1 )
# get the data for this game
oneProbs = oneProbSamples[ game ]
sameProbs = sameProbSamples[ game ]
originalOneProbs = copy.deepcopy( oneProbs )
gates = gateSamples[ game ]
gameOn = True
restart = False
score = 0
while gameOn:
score += 1
# Step 1: get a new puzzle
if dataNeeded:
# if running anew, we generate a new set of gates
# gates applied are of the form
# CNOT | (j,k)
# Rx(frac*pi) | j
# CNOT | (j,k)
# and so are specified by a pair p=[j,k] and a random fraction frac
# first we generate a random set of edges
matchingPairs = getDisjointPairs( pairs, [], {} )
# then we add gates these to the list of gates
appliedGates = {}
for p in matchingPairs:
frac = ( 0.1+0.9*random.random() ) / 2 # this will correspond to a e^(i theta \sigma_x) rotation with pi/20 \leq frac * pi/2 \leq pi/4
appliedGates[p] = frac
gates.append(appliedGates)
# all gates so far are then run
oneProb, sameProb, results = entangle( device, move, shots, sim, gates, conjugates)
else:
oneProb = oneProbs[score-1]
sameProb = sameProbs[score-1]
matchingPairs = list(gates[ 2*(score-1) ].keys())
I = calculateMutual ( oneProb, sameProb, pairs )
correlatedPairs = getDisjointPairs( pairs, [], I )
rawOneProb = copy.deepcopy( oneProb )
if cleanup:
oneProb = CleanData(cleaner[score-1],rawOneProb,sameProb,pairs)
results = []
# Step 2: Get player to guess pairs
displayedOneProb = copy.copy( oneProb )
guessedPairs = []
# if choices are all correct, we just give the player the right answer
if (move=="C"):
guessedPairs = matchingPairs
# if choices are random, we generate a set of random pairs
if (move=="R"):
guessedPairs = getDisjointPairs( pairs, [], {} )
# if choices are via MWPM, we do this
if (move=="B"):
guessedPairs = getDisjointPairs( pairs, oneProb, {} )
# if choices are manual, let's get choosing
if (move=="M"):
# get the player choosing until the choosing is done
unpaired = num
restart = False
while (unpaired>1):
clear_output()
print("")
print("Round "+str(score))
if cleanup:
printM("\nRaw puzzle",move)
printPuzzle( device, rawOneProb, move, ascii=ascii)
printM("\nCleaned puzzle", move)
printPuzzle( device, displayedOneProb, move, ascii=ascii)
pairGuess = input("\nChoose a pair (or type 'done' to skip to the next round, or 'restart' for a new game)\n")
if num<=26 : # if there are few enough qubits, we don't need to be case sensitive
pairGuess = str.upper(pairGuess)
if (pairGuess in pairs.keys()) and (pairGuess not in guessedPairs) :
guessedPairs.append(pairGuess)
# set them both to grey on screen (set the corresponding oneProb value to >1)
for j in [0,1]:
displayedOneProb[ pairs[pairGuess][j] ] = 2
printM("\n\n\n", move)
# check if all active (and therefore displayed) vertices have been covered
unpaired = 0
for n in pos.keys():
unpaired += ( displayedOneProb[n] <= 1 )
elif (str.upper(pairGuess)=="DONE") : # player has decided to stop pairing
unpaired = 0
elif (str.upper(pairGuess)=="RESTART") : # player has decided to stop the game
unpaired = 0
restart = True
else:
printM("That isn't a valid pair. Try again.\n(Note that input can be case sensitive)", move)
# store the oneProb and sameProb
oneProbs.append( oneProb )
sameProbs.append( sameProb )
# store the raw data (if it is not too big
if len(str(results)) < 10000:
resultsDicts.append( results )
# see whether the game over condition is satisfied
gameOn = (score<maxScore) and restart==False