Equivalence Checking of Parameterized Quantum Circuits
Introduction
Before running on a quantum device, a parameterized quantum circuit needs to be compiled into a new circuit consisting of the set of quantum gates supported by the device. Therefore, it is necessary to check the equivalence of the two circuits before and after compilation. In the paper Equivalence Checking of Parameterized Quantum Circuits, a method for checking the equivalence of parameterized quantum circuits based on ZX calculus is proposed. This tutorial attempts to reproduce the method in the MindSpore Quantum architecture.
Paper link: https://doi.org/10.1145/3566097.3567932
[1]:
# import libraries
from mindquantum.core.circuit import Circuit
import numpy as np
from mindquantum.core.gates import H, CNOT, RX, RZ
from mindquantum.core.circuit import dagger
Step 1
Prepare the quantum circuits.
Take the TwoLocal-Circular circuit in the Qiskit circuits library as an example. The TwoLocal circuit is a parameterized circuit composed of alternating rotation layers and entanglement layers. Rotation layer consists of single qubit gates acting on all qubits. Entanglement layer consists of double qubit gates to entangle qubits according to the entanglement strategy.
Construct a TwoLocal-Circular circuit acting on 127-bit qubits, consisting of three sets of rotation layers and entanglement layers alternately, containing a total of 508 parameters. The rotation layer is RX gates acting on each qubit. The entanglement layer is a CNOT gate on the first qubit controlled by the last qubit and CNOT gates on the next qubit controlled by the previous qubit.
[2]:
# ansatz = rotation layer + entanglement layer
def build_ansatz(n_qubits, depth):
circ = Circuit() # initialize a quantum circuit
for i in range(depth):
for j in range(n_qubits):
circ += RX(f'theta{i*n_qubits+j}').on(j) # RX gate on each qubit
# CNOT gate on the first qubit controlled by the last qubit
circ += CNOT.on(0, n_qubits-1)
for j in range(n_qubits-1):
# CNOT gate on the next qubit controlled by the previous qubit
circ += CNOT.on(j+1, j)
for j in range(n_qubits):
circ += RX(f'theta{depth*n_qubits+j}').on(j) # RX gate on each qubit
return circ
[3]:
# example ansatz: 3 qubits and 1 layer
build_ansatz(3, 1).svg()
[3]:
[4]:
# initial circuit: 127 qubits and 3 layer
n_qubits = 127
depth = 3
circ1 = build_ansatz(n_qubits, depth)
circ1.summary()
Circuit Summary ╭───────────────────────┬───────────────────────────────────────────────────────────────────╮ │ Info │ value │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Number of qubit │ 127 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Total number of gate │ 889 │ │ Barrier │ 0 │ │ Noise Channel │ 0 │ │ Measurement │ 0 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Parameter gate │ 508 │ │ 508 ansatz parameters │ theta0, theta1, theta2, theta3, theta4, theta5, theta6, theta7, │ │ │ theta8, theta9... │ ╰───────────────────────┴───────────────────────────────────────────────────────────────────╯
Then do the compilation.
Suppose the set of quantum gates before compilation is: H, CNOT, RZ, RX. The compiled set of quantum gates is: H, CNOT, RZ. The compilation rule is that the H, CNOT, and RZ gates remain unchanged, and the RX gates are compiled into a combination of H, RZ, H.
[5]:
def compile_circuit(circ):
circ_compiled = Circuit()
for gate in circ: # traverse the gates in the initial circuit
# the H, CNOT, and RZ gates remain unchanged
if gate.name == 'H' or gate.name == 'CNOT' or gate.name == 'RZ':
circ_compiled += gate
# the RX gates are compiled into a combination of H*RZ*H
elif gate.name == 'RX':
circ_compiled += H.on(gate.obj_qubits)
circ_compiled += RZ(gate.coeff).on(gate.obj_qubits)
circ_compiled += H.on(gate.obj_qubits)
return circ_compiled
[6]:
# Example of a line generated by compiling a layer of ansatz lines and we can see that all RX gates have changed according to the compilation rules
compile_circuit(build_ansatz(3, 1)).svg()
[6]:
[7]:
# compile initial circuit
circ2 = compile_circuit(circ1)
circ2.summary() # summary compilation circuit
Circuit Summary ╭───────────────────────┬───────────────────────────────────────────────────────────────────╮ │ Info │ value │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Number of qubit │ 127 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Total number of gate │ 1905 │ │ Barrier │ 0 │ │ Noise Channel │ 0 │ │ Measurement │ 0 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Parameter gate │ 508 │ │ 508 ansatz parameters │ theta0, theta1, theta2, theta3, theta4, theta5, theta6, theta7, │ │ │ theta8, theta9... │ ╰───────────────────────┴───────────────────────────────────────────────────────────────────╯
Finally, construct the complete cicuit.
According to the reversibility of the quantum circuit, if the two circuits are equivalent, then applying one circuit and the reversion of the other circuit, the final quantum state is equivalent to the state before the application. Thus, the complete quantum circuit consists of the compiled circuit and the reversion of the initial circuit.
[8]:
# complete circuit
circ1_inv = dagger(circ1) # dagger() reverse the circuit
# complete circuit = reversion of initial circuit + circuit after compilation
circ_all = circ1_inv + circ2
circ_all.summary() # summary complete circuit
Circuit Summary ╭───────────────────────┬───────────────────────────────────────────────────────────────────╮ │ Info │ value │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Number of qubit │ 127 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Total number of gate │ 2794 │ │ Barrier │ 0 │ │ Noise Channel │ 0 │ │ Measurement │ 0 │ ├───────────────────────┼───────────────────────────────────────────────────────────────────┤ │ Parameter gate │ 1016 │ │ 508 ansatz parameters │ theta507, theta506, theta505, theta504, theta503, theta502, │ │ │ theta501, theta500, theta499, theta498... │ ╰───────────────────────┴───────────────────────────────────────────────────────────────────╯
Setp 2
Draw the complete circuit into ZX diagram.
The equivalence checking of parameterized quantum circuits is based on the ZX calculus. Then the quantum circuits need to be converted into ZX diagrams.
The quantum gate is the vertex in the ZX diagram, divided into 3 colors. The H gate is represented as a yellow vertex, the RX gate is a red vertex with parameters, and the RZ gate is a green vertex with parameters. The target qubit of the CNOT gate is a red vertex, and the control qubit is a green vertex, which two are neighbors. The vertices of two adjacent quantum gates on the same qubit are neighbors to each other.
Start by defining the vertex class and graph class.
[9]:
class Vertex:
def __init__(self, name, color, qubit, neighbor, phase=0.0):
self.name = name # the number of the vertex
self.color = color # the color of the vertex
self.phase = phase # the parameter of the vertex
self.qubit = qubit # the qubit of the vertex
self.neighbor = neighbor # the neighbor of the vertex
[10]:
# graph class
class Graph:
def __init__(self):
self.vertices = {}
# count: the total number of vertices
# which only increases but does not decrease
# count is also used to name new vertices
self.count = 0
# add an edge from the start to the end
def add_edge(self, from_vertex, to_vertex):
self.vertices[from_vertex].neighbor.append(to_vertex)
# add vertex
def add_vertex(self, color, qubit, neighbor, phase=0.0):
name = self.count
self.count += 1
# add edges from the current vertex to its neighbors
self.vertices[name] = Vertex(name, color, qubit, neighbor, phase)
for v in neighbor: # add edges from its neighbors to it
self.add_edge(v, name)
def print(self):
print("==================graph message==================")
for v in self.vertices.values():
print(v.name, '\t', v.neighbor, '\t', v.color, '\t', v.phase)
print('\n')
# clear the loops produced during adding or deleting
# there is no loop in ZX-diagram(loop which made of a single edge)
def clear(self):
for v in self.vertices.values():
while v.name in v.neighbor:
# remove the vertex from its own neighbors
self.vertices[v.name].neighbor.remove(v.name)
# delete vertex
def delete_vertex(self, name):
for v in self.vertices.values():
while name in v.neighbor:
# delete edges whose end is the current vertex
self.vertices[v.name].neighbor.remove(name)
# delete edges whose start is the current vertex
self.vertices.pop(name)
# if two circuits are equivalent
def equiv(self):
# if equivalent, after simplification, there is no vertex
if not self.vertices:
print("Equivalent!")
else:
print("Not sure!")
Then draw the quantum circuit into a ZX diagram.
Traverse all the quantum gates in the circuit and plot them as vertices in the ZX diagram. If there is no gate on the current qubit, the current quantum gate has no neighbors. If there has at least one quantum gate on the current qubit, the current quantum gate and the last quantum gate on the qubit are neighbors. The CNOT gate adds a neighbor relationship between the control qubit and the target qubit.
[11]:
def draw_graph(circ):
g = Graph()
# last_name saves the last vertex on each qubit
last_name = [-1] * circ.n_qubits
for gate in circ: # traverse all the quantum gates
if gate.name == 'H': # H gate = yellow vertex
# there are vertices on the current qubit
if last_name[gate.obj_qubits[0]] != -1:
g.add_vertex('yellow', gate.obj_qubits[0],
[last_name[gate.obj_qubits[0]]])
else: # there is no vertex on the current qubit
g.add_vertex('yellow', gate.obj_qubits[0], [])
# update the last vertex on the current qubit to the current vertex
last_name[gate.obj_qubits[0]] = g.count-1
if gate.name == 'RX': # RX gate = red vertex
if last_name[gate.obj_qubits[0]] != -1:
g.add_vertex('red', gate.obj_qubits[0],
[last_name[gate.obj_qubits[0]]], gate.coeff)
else:
g.add_vertex('red', gate.obj_qubits[0], [], gate.coeff)
last_name[gate.obj_qubits[0]] = g.count-1
if gate.name == 'RZ': # RZ gate = green vertex
if last_name[gate.obj_qubits[0]] != -1:
g.add_vertex('green', gate.obj_qubits[0],
[last_name[gate.obj_qubits[0]]], gate.coeff)
else:
g.add_vertex('green', gate.obj_qubits[0], [], gate.coeff)
last_name[gate.obj_qubits[0]] = g.count-1
if gate.name == 'CNOT':
# control qubit = green vertex
if last_name[gate.obj_qubits[1]] != -1:
g.add_vertex('green', gate.obj_qubits[1],
[last_name[gate.obj_qubits[1]]])
else:
g.add_vertex('green', gate.obj_qubits[1], [])
last_name[gate.obj_qubits[1]] = g.count-1
# target qubit = red vertex
if last_name[gate.obj_qubits[0]] != -1:
g.add_vertex('red', gate.obj_qubits[0],
[last_name[gate.obj_qubits[0]], g.count-1])
else:
g.add_vertex('red', gate.obj_qubits[0], [g.count-1])
last_name[gate.obj_qubits[0]] = g.count-1
return g
Finally, draw the complete quantum circuit into a ZX diagram.
[12]:
g = draw_graph(circ_all)
Step 3
Simplify the ZX diagram
The ZX calculus consists of ZX diagrams and reduction rules, according to which the vertices and neighbor relations in the ZX diagram are simplified.
Here lists some of the rules, and will not be repeated one by one.
rule 1: red or green vertices with parameter 0 that are not adjacent to vertices on other qubits can be deleted.
[13]:
def rule_1(g: Graph):
# during the ZX calculus, the vertices will increase or decrease
# use list() to get all the initial vertices
for v1 in list(g.vertices.keys()):
# whether the current vertex has been deleted during simplification
if v1 not in g.vertices.keys():
continue # deleted, pass
v1 = g.vertices[v1]
# parameter = 0
if v1.phase == 0 or list(v1.phase.values()) == [0.0]*len(list(v1.phase.values())):
# whether the current vertex is related to vertices on other qubits
# and if so, it cannot be deleted
flag = True
for v2 in v1.neighbor:
v2 = g.vertices[v2]
# related to vertices on other qubits
if v2.qubit != v1.qubit:
flag = False
break
if flag: # not related to vertices on other qubits
for v2 in v1.neighbor:
v2 = g.vertices[v2]
# connect the previous vertex to the next vertex
v2.neighbor.extend(v1.neighbor)
g.clear() # remove rings that may arise
g.delete_vertex(v1.name) # delete the current vertex
rule 2: two adjacent, red or green vertices of the same color can be merged.
[14]:
def rule_2(g: Graph):
for v1 in list(g.vertices.keys()):
if v1 not in g.vertices.keys():
continue
v1 = g.vertices[v1]
if v1.color == 'red' or v1.color == 'green': # red or green
for v2 in v1.neighbor: # adjacent
v2 = g.vertices[v2]
if v2.color == v1.color: # same color
v2.phase = v2.phase + v1.phase # add the parameters
# merge these two vertices
v2.neighbor.extend(v1.neighbor)
g.clear()
for v3 in v1.neighbor: # update the neighbors
v3 = g.vertices[v3]
v3.neighbor.append(v2.name)
g.clear()
# delete the vertex that has been merged
g.delete_vertex(v1.name)
rule 3: green vertices whose neighbors are yellow vertices can become red vertices and remove adjacent yellow vertices.
[15]:
def rule_3(g: Graph):
for v1 in list(g.vertices.keys()):
if v1 not in g.vertices.keys():
continue
v1 = g.vertices[v1]
if v1.color == 'green':
flag = True # if all neighbors yellow
for v2 in v1.neighbor:
v2 = g.vertices[v2]
if v2.color != 'yellow': # not all neighbors yellow
flag = False
break
if flag: # all neighbors yellow
v1.color = 'red' # turn into red
v1_neighbor = list(v1.neighbor)
for v2 in v1_neighbor: # delete these yellow vertices
v2 = g.vertices[v2]
v1.neighbor.extend(v2.neighbor)
g.clear()
for v3 in v2.neighbor:
v3 = g.vertices[v3]
v3.neighbor.append(v1.name)
g.clear()
g.delete_vertex(v2.name)
rule 4: two edges between adjacent red and green vertices can be deleted.
[16]:
def rule_4(g: Graph):
for v1 in list(g.vertices.keys()):
if v1 not in g.vertices.keys():
continue
v1 = g.vertices[v1]
if v1.color == 'green':
for v2 in v1.neighbor:
v2 = g.vertices[v2]
# adjacent red and green vertices with two edges
if v2.color == 'red' and v2.neighbor.count(v1.name) == 2:
# delete these two edges
while v2.name in g.vertices[v1.name].neighbor:
v1.neighbor.remove(v2.name)
while v1.name in g.vertices[v2.name].neighbor:
v2.neighbor.remove(v1.name)
Then, use the above rules to simplify the ZX diagram. If no vertex is deleted in a round, the simplification is considered to be complete.
[17]:
def simplify(g: Graph):
temp = [] # whether vertices have been deleted in the current round
# if no vertex is removed in the current round
# the simplification is considered complete
while temp != list(g.vertices.keys()):
temp = list(g.vertices.keys())
rule_3(g)
rule_2(g)
rule_4(g)
rule_1(g)
The complete circuit is large in scale, and a single-layer circuit acting on three qubits can be constructed for testing.
[18]:
test_circ1 = build_ansatz(3, 1)
test_circ1_inv = dagger(test_circ1)
test_circ2 = compile_circuit(test_circ1)
test_circ_all = test_circ1_inv + test_circ2
test_circ_all.svg()
[18]:
[19]:
# draw the testing circuit into a ZX diagram
test_g = draw_graph(test_circ_all)
test_g.print()
==================graph message==================
0 [4] red -theta5
1 [3] red -theta4
2 [5] red -theta3
3 [1, 4, 6] green 0.0
4 [0, 3, 7] red 0.0
5 [2, 6, 8] green 0.0
6 [3, 5, 10] red 0.0
7 [4, 8, 9] green 0.0
8 [5, 7, 11] red 0.0
9 [7, 18] red -theta2
10 [6, 15] red -theta1
11 [8, 12] red -theta0
12 [11, 13] yellow 0.0
13 [12, 14] green theta0
14 [13, 22] yellow 0.0
15 [10, 16] yellow 0.0
16 [15, 17] green theta1
17 [16, 24] yellow 0.0
18 [9, 19] yellow 0.0
19 [18, 20] green theta2
20 [19, 21] yellow 0.0
21 [20, 22, 26] green 0.0
22 [14, 21, 23] red 0.0
23 [22, 24, 27] green 0.0
24 [17, 23, 25] red 0.0
25 [24, 26, 30] green 0.0
26 [21, 25, 33] red 0.0
27 [23, 28] yellow 0.0
28 [27, 29] green theta3
29 [28] yellow 0.0
30 [25, 31] yellow 0.0
31 [30, 32] green theta4
32 [31] yellow 0.0
33 [26, 34] yellow 0.0
34 [33, 35] green theta5
35 [34] yellow 0.0
[20]:
# simplify the testing circuit
print("before simplification:")
test_g.equiv()
simplify(test_g)
print("after simplification:")
test_g.equiv()
before simplification:
Not sure!
after simplification:
Equivalent!
After passing the simplification function test, we can try to simplify the ZX diagram of the complete circuit. The result shows that the two circuits before and after compilation are equivalent.
[21]:
# simplify the complete circuit
print("before simplification:")
g.equiv()
simplify(g)
print("after simplification:")
g.equiv()
before simplification:
Not sure!
after simplification:
Equivalent!
Step 4
If ZX Calculus not sure then verify by instantiating the parameter.
The ZX calculus cannot directly give the result of the equivalent circuits. In this case, we need to instantiate the parameters in the circuits to determine whether the two circuits after instantiation are equivalent.
[22]:
# counterexample
neq_circ1 = Circuit()
neq_circ1 += H.on(1)
neq_circ1 += RX(f'theta{0}').on(2)
neq_circ1 += CNOT.on(0, 1)
neq_circ1 += RZ(f'theta{1}').on(0)
neq_circ1 += CNOT.on(2, 1)
neq_circ1 += CNOT.on(0, 1)
neq_circ1 += RX(f'theta{2}').on(2)
neq_circ1.svg()
[22]:
[23]:
neq_circ2 = Circuit()
neq_circ2 += H.on(1)
neq_circ2 += RX(f'theta{0}').on(2)
neq_circ2 += CNOT.on(0, 1)
neq_circ2 += RZ(f'theta{1}').on(0)
neq_circ2 += CNOT.on(2, 1)
neq_circ2 += CNOT.on(0, 1)
neq_circ2 += RX({f'theta{0}': 1, f'theta{1}': 1, f'theta{2}': 1}).on(2)
neq_circ2.svg()
[23]:
[24]:
neq_circ1_inv = dagger(neq_circ1)
neq_circ_all = neq_circ1_inv + neq_circ2 # construct the complete circuit of the counterexample
neq_circ_all.svg()
[24]:
[25]:
# draw the counterexample into ZX diagram and simplify it
neq_g = draw_graph(neq_circ_all)
print("before simplification:")
neq_g.equiv()
simplify(neq_g)
print("after simplification:")
neq_g.equiv()
before simplification:
Not sure!
after simplification:
Not sure!
In this case, after simplification, there are still undeleted vertices in the ZX diagram. So the ZX calculus cannot determine its equivalence and needs to be verified by instantiating parameters.
The instantiation has two steps:
First, directly compare whether the matrices of the two circuits are equivalent after instantiation according to the map function, and stop if not.
Second, if the instantiation according to the map function does not get the result, randomly instantiate the parameters, and then directly compare whether the matrices of the two circuits after instantiation are equivalent. If unequivalent, the two circuits are unequivalent, otherwise equivalent.
[26]:
# instantiation according to the map function
def map_para(n, r):
para = {}
for i in range(n):
para[f'theta{i}'] = (2*np.pi/((i+1)*r)-np.pi)
return para
# randomly instantiate the parameters
def random_para(n):
para = {}
for i in range(n):
para[f'theta{i}'] = (np.random.uniform(np.pi, -np.pi))
return para
[27]:
# verify by instantiating parameters
def verify_by_para(circ1, circ2, r):
# there are n parameters in the circuit
n = len(list(set(circ1.params_name+circ2.params_name)))
flag = True # whether the previous r-1 round has a result
for i in range(r-1): # instantiation according to the map function
para = map_para(n, i+1)
# whether the matrices of the two circuits are equivalent
if np.array_equal(circ1.matrix(para), circ2.matrix(para)):
continue
else:
print('Not equivalent!')
flag = False # get a result
break
if flag: # randomly instantiate the parameters
para = random_para(n)
if np.array_equal(circ1.matrix(para), circ2.matrix(para)):
print('Equivalent!')
else:
print('Not equivalent!')
Verify the equivalence of the two counterexample circuits by instantiation.
[28]:
verify_by_para(neq_circ1, neq_circ2, 5)
Not equivalent!
Final step: merge the above process into a complete function
[29]:
def ZXcalculus(circ1, circ2):
circ1_inv = dagger(circ1) # reverse the initial circuit
circ = circ1_inv + circ2 # construct the complete circuit
g = draw_graph(circ) # draw the complete circuit into ZX diagram
print("before simplification:")
g.equiv()
simplify(g) # simplify by the rules
print("after simplification:")
if not g.vertices: # get a result by ZX calculus
g.equiv()
else: # need to verify by instantiation
g.equiv()
print("verify by instantiation:")
verify_by_para(circ1, circ2, 5)
[30]:
from mindquantum.utils.show_info import InfoTable
InfoTable('mindquantum', 'scipy', 'numpy')
[30]:
| Software | Version |
|---|---|
| mindquantum | 0.9.11 |
| scipy | 1.9.3 |
| numpy | 1.23.5 |
| System | Info |
| Python | 3.8.17 |
| OS | Linux x86_64 |
| Memory | 16.62 GB |
| CPU Max Thread | 16 |
| Date | Tue Jan 2 17:34:07 2024 |