Circuits

Conceptual overview

There are two primary representations of quantum programs in Cirq, each of which are represented by a class: Circuit and Schedule. Conceptually a Circuit object is very closely related to the abstract quantum circuit model, while a Schedule object is like the abstract quantum circuit model but includes detailed timing information.

Conceptually: a Circuit is a collection of Moments. A Moment is a collection of Operations that all act during the same abstract time slice. An Operation is a some effect that operates on a specific subset of Qubits, the most common type of Operation is a GateOperation.

Circuits and Moments Let’s unpack this.

At the base of this construction is the notion of a qubit. In Cirq, qubits are represented by subclasses of the QubitId base class. Different subclasses of QubitId can be used for different purposes. For example the qubits that Google’s Xmon devices use are often arranged on the vertices of a square grid. For this the class GridQubit subclasses QubitId. For example, we can create a 3 by 3 grid of qubits using

qubits = [cirq.GridQubit(x, y) for x in range(3) for y in range(3)]

print(qubits[0])
# prints "(0, 0)"

The next level up conceptually is the notion of a Gate. A Gate represents a physical process that occurs on a Qubit. The important property of a Gate is that it can be applied on to one or more qubits. This can be done via the Gate.on method itself or via () and doing this turns the Gate into a GateOperation.

# This is an Pauli X gate. It is an object instance.
x_gate = cirq.X
# Applying it to the qubit at location (0, 0) (defined above)
# turns it into an operation.
x_op = x_gate(qubits[0])

print(x_op)
# prints "X((0, 0))"

A Moment is quite simply a collection of operations, each of which operates on a different set of qubits, and which conceptually represents these operations as occurring during this abstract time slice. The Moment structure itself is not required to be related to the actual scheduling of the operations on a quantum computer, or via a simulator, though it can be. For example, here is a Moment in which Pauli X and a CZ gate operate on three qubits:

cz = cirq.CZ(qubits[0], qubits[1])
x = cirq.X(qubits[2])
moment = cirq.Moment([x, cz])

print(moment)
# prints "X((0, 2)) and CZ((0, 0), (0, 1))"

Note that is not the only way to construct moments, nor even the typical method, but illustrates that a Moment is just a collection of operations on disjoint sets of qubits.

Finally at the top level a Circuit is an ordered series of Moments. The first Moment in this series is, conceptually, contains the first Operations that will be applied. Here, for example, is a simple circuit made up of two moments

cz01 = cirq.CZ(qubits[0], qubits[1])
x2 = cirq.X(qubits[2])
cz12 = cirq.CZ(qubits[1], qubits[2])
moment0 = cirq.Moment([cz01, x2])
moment1 = cirq.Moment([cz12])
circuit = cirq.Circuit((moment0, moment1))

print(circuit)
# prints the text diagram for the circuit:
# (0, 0): ───@───────
#            │
# (0, 1): ───@───@───
#                │
# (0, 2): ───X───@───

Again, note that this is only one way to construct a Circuit but illustrates the concept that a Circuit is an iterable of Moments.

Constructing circuits

Constructing Circuits as a series of Moments with each Moment being hand-crafted is tedious. Instead we provide a variety of different manners to create a Circuit.

One of the most useful ways to construct a Circuit is by appending onto the Circuit with the Circuit.append method.

from cirq.ops import CZ, H
q0, q1, q2 = [cirq.GridQubit(i, 0) for i in range(3)]
circuit = cirq.Circuit()
circuit.append([CZ(q0, q1), H(q2)])

print(circuit)
# prints
# (0, 0): ───@───
#            │
# (1, 0): ───@───
#
# (2, 0): ───H───

This appended an entire new moment to the qubit, which we can continue to do,

circuit.append([H(q0), CZ(q1, q2)])

print(circuit)
# prints
# (0, 0): ───@───H───
#            │
# (1, 0): ───@───@───
#                │
# (2, 0): ───H───@───

In these two examples, we have appending full moments, what happens when we append all of these at once?

circuit = cirq.Circuit()
circuit.append([CZ(q0, q1), H(q2), H(q0), CZ(q1, q2)])

print(circuit)
# prints
# (0, 0): ───@───H───
#            │
# (1, 0): ───@───@───
#                │
# (2, 0): ───H───@───

We see that here we have again created two Moments. How did Circuit know how to do this? Circuit's Circuit.append method (and its cousin Circuit.insert) both take an argument called the InsertStrategy. By default the InsertStrategy is InsertStrategy.NEW_THEN_INLINE.

InsertStrategies

InsertStrategy defines how Operations are placed in a Circuit when requested to be inserted at a given location. Here a location is identified by the index of the Moment (in the Circuit) where the insertion is requested to be placed at (in the case of Circuit.append this means inserting at the Moment at an index one greater than the maximum moment index in the Circuit). There are four such strategies: InsertStrategy.EARLIEST, InsertStrategy.NEW, InsertStrategy.INLINE and InsertStrategy.NEW_THEN_INLINE.

InsertStrategy.EARLIEST is defined as

InsertStrategy.EARLIEST: Scans backward from the insert location until a moment with operations touching qubits affected by the operation to insert is found. The operation is added into the moment just after that location.

For example, if we first create an Operation in a single moment, and then use InsertStrategy.EARLIEST the Operation can slide back to this first Moment if there is space:

from cirq.circuits import InsertStrategy
circuit = cirq.Circuit()
circuit.append([CZ(q0, q1)])
circuit.append([H(q0), H(q2)], strategy=InsertStrategy.EARLIEST)

print(circuit)
# prints
# (0, 0): ───@───H───
#            │
# (1, 0): ───@───────
#
# (2, 0): ───H───────

After creating the first moment with a CZ gate, the second append uses the InsertStrategy.EARLIEST strategy. The H on q0 cannot slide back, while the H on q2 can and so ends up in the first Moment.

Contrast this with the InsertStrategy.NEW InsertStrategy:

InsertStrategy.NEW: Every operation that is inserted is created in a new moment.
circuit = cirq.Circuit()
circuit.append([H(q0), H(q1), H(q2)], strategy=InsertStrategy.NEW)

print(circuit)
# prints
# (0, 0): ───H───────────
#
# (1, 0): ───────H───────
#
# (2, 0): ───────────H───

Here every operator processed by the append ends up in a new moment. InsertStrategy.NEW is most useful when you are inserting a single operation and don’t want it to interfere with other Moments.

Another strategy is InsertStrategy.INLINE:

InsertStrategy.INLINE: Attempts to add the operation to insert into the moment just before the desired insert location. But, if there’s already an existing operation affecting any of the qubits touched by the operation to insert, a new moment is created instead.
circuit = cirq.Circuit()
circuit.append([CZ(q1, q2)])
circuit.append([CZ(q1, q2)])
circuit.append([H(q0), H(q1), H(q2)], strategy=InsertStrategy.INLINE)

print(circuit)
# prints
# (0, 0): ───────H───────
#
# (1, 0): ───@───@───H───
#            │   │
# (2, 0): ───@───@───H───

After two initial CZ between the second and third qubit, we try to insert 3 H Operations. We see that the H on the first qubit is inserted into the previous Moment, but the H on the second and third qubits cannot be inserted into the previous Moment, so a new Moment is created.

Finally we turn to the default strategy:

InsertStrategy.NEW_THEN_INLINE: Creates a new moment at the desired insert location for the first operation, but then switches to inserting operations according to InsertStrategy.INLINE.
circuit = cirq.Circuit()
circuit.append([H(q0)])
circuit.append([CZ(q1,q2), H(q0)], strategy=InsertStrategy.NEW_THEN_INLINE)

print(circuit)
# prints
# (0, 0): ───H───H───
#
# (1, 0): ───────@───
#                │
# (2, 0): ───────@───

The first append creates a single moment with a H on the first qubit. Then the append with the InsertStrategy.NEW_THEN_INLINE strategy begins by inserting the CZ in a new Moment (the InsertStrategy.NEW in InsertStrategy.NEW_THEN_INLINE). Subsequent appending is done InsertStrategy.INLINE so the next H on the first qubit is appending in the just created Moment.

Here is a helpful diagram for the different InsertStrategies.

TODO(dabacon): diagram.

Patterns for Arguments to Append and Insert

Above we have used a series of Circuit.append calls with a list of different Operations we are adding to the circuit. But the argument where we have supplied a list can also take more than just list values.

Example:

def my_layer():
    yield CZ(q0, q1)
    yield [H(q) for q in (q0, q1, q2)]
    yield [CZ(q1, q2)]
    yield [H(q0), [CZ(q1, q2)]]

circuit = cirq.Circuit()
circuit.append(my_layer())

for x in my_layer():
    print(x)
# prints
# CZ((0, 0), (1, 0))
# [cirq.H.on(cirq.GridQubit(0, 0)), cirq.H.on(cirq.GridQubit(1, 0)), cirq.H.on(cirq.GridQubit(2, 0))]
# [cirq.CZ.on(cirq.GridQubit(1, 0), cirq.GridQubit(2, 0))]
# [cirq.H.on(cirq.GridQubit(0, 0)), [cirq.CZ.on(cirq.GridQubit(1, 0), cirq.GridQubit(2, 0))]]

print(circuit)
# prints
# (0, 0): ───@───H───H───────
#            │
# (1, 0): ───@───H───@───@───
#                    │   │
# (2, 0): ───────H───@───@───

Recall that in Python functions that have a yield are generators. Generators are functions that act as iterators. Above we see that we can iterate over my_layer(). We see that when we do this each of the yields produces what was yielded, and here these are Operations, lists of Operations or lists of Operations mixed with lists of Operations. But when we pass this iterator to the append method, something magical happens. Circuit is able to flatten all of these an pass them as one giant list to Circuit.append (this also works for Circuit.insert).

The above idea uses a concept we call an OP_TREE. An OP_TREE is not a class, but a contract. The basic idea is that, if the input can be iteratively flattened into a list of operations, then the input is an OP_TREE.

A very nice pattern emerges from this structure: define generators for sub-circuits, which can vary by size or Operation parameters.

Another useful method is to construct a Circuit fully formed from an OP_TREE via the static method Circuit.from_ops (which takes an insertion strategy as a parameter):

circuit = cirq.Circuit.from_ops(H(q0), H(q1))
print(circuit)
# prints
# (0, 0): ───H───
#
# (1, 0): ───H───

Slicing and Iterating over Circuits

Circuits can be iterated over and sliced. When they are iterated over each item in the iteration is a moment:

circuit = cirq.Circuit.from_ops(H(q0), CZ(q0, q1))
for moment in circuit:
    print(moment)
# prints
# H((0, 0))
# CZ((0, 0), (1, 0))

Slicing a Circuit on the other hand, produces a new Circuit with only the moments corresponding to the slice:

circuit = cirq.Circuit.from_ops(H(q0), CZ(q0, q1), H(q1), CZ(q0, q1))
print(circuit[1:3])
# prints
# (0, 0): ───@───────
#            │
# (1, 0): ───@───H───

Especially useful is dropping the last moment (which are often just measurements): circuit[:-1], or reversing a circuit: circuit[::-1].