Advanced usage¶
Note
If you’re running locally, remember set up the QVM and quilc in server mode before trying to use them: Setting up requisite servers for pyQuil.
pyQuil configuration¶
QCSClient
instructs pyQuil on how to connect with the components needed to compile and run
programs (quilc
, qvm
, and QCS). Any APIs that take a configuration object as input
(e.g. get_qc()
) typically do so optionally, so that a default configuration can be loaded
for you if one is not provided. You can override this default configuration by either instantiating your own
QCSClient
object and providing it as input to the function in question,
or by setting the QCS_SETTINGS_FILE_PATH
and/or QCS_SECRETS_FILE_PATH
environment variables to have
pyQuil load its settings and secrets from specific locations. By default, configuration will be loaded from
$HOME/.qcs/settings.toml
and $HOME/.qcs/secrets.toml
.
Additionally, you can override whichever QVM and quilc URLs are loaded from settings.toml
(profiles.<profile>.applications.pyquil.qvm_url
and profiles.<profile>.applications.pyquil.quilc_url
fields)
by setting the QCS_SETTINGS_APPLICATIONS_QVM_URL
and/or QCS_SETTINGS_APPLICATIONS_QUILC_URL
environment variables. If these URLs are missing from settings.toml
and are not set by environment variables,
the following defaults will be used (as they correspond to the default behavior of the QVM and quilc when running
locally):
QVM URL:
http://127.0.0.1:5000
quilc URL:
tcp://127.0.0.1:5555
Multithreading¶
QuantumComputer
objects are safe to share between threads, enabling you to execute and retrieve
results for multiple programs or parameter values at once. Note that Program
and
EncryptedProgram
are not thread-safe, and should be copied (with copy()
) before use in a
concurrent context.
Note
The QVM processes incoming requests in parallel, while a QPU may process them sequentially or in parallel
(depending on the qubits used). If you encounter timeouts while trying to run large numbers of programs against a
QPU, try increasing the execution_timeout
parameter on calls to get_qc()
(specified in
seconds).
Note
We suggest running jobs with a minimum of 2x parallelism, so that the QVM or QPU is fully occupied while your program runs and no time is wasted in between jobs.
Note
Because pyQuil does not currently have an asyncio
API it is recommended to use ThreadPool
s.
Below is an example that demonstrates how to use pyQuil in a multithreading scenario:
from multiprocessing.pool import ThreadPool
from pyquil import get_qc, Program
from pyquil.api import QCSClient
qc = get_qc("Aspen-M-3")
def run(program: Program):
return qc.run(qc.compile(program)).get_register_map().get("ro")
programs = [
Program(
"DECLARE ro BIT",
"RX(pi) 0",
"MEASURE 0 ro",
).wrap_in_numshots_loop(10),
] * 20
with ThreadPool(5) as pool:
results = pool.map(run, programs)
for i, result in enumerate(results):
print(f"Results for program {i}:\n{result}\n")
Alternative QPU endpoints¶
Rigetti QCS supports alternative endpoints for access to a QPU architecture, useful for very particular cases. Generally, this is useful to call “mock” or test endpoints, which simulate the results of execution for the purposes of integration testing without the need for an active reservation or contention with other users. See the QCS API Docs for more information on QPU Endpoints.
To be able to call these endpoints using pyQuil, enter the endpoint_id
of your desired endpoint in one
of the sites where quantum_processor_id
is used:
# Option 1
qc = get_qc("Aspen-M-3", endpoint_id="my_endpoint")
# Option 2
qam = QPU(quantum_processor_id="Aspen-M-3", endpoint_id="my_endpoint")
After doing so, for all intents and purposes - compilation, optimization, etc - your program will behave the same as when using “default” endpoint for a given quantum processor, except that it will be executed by an alternate QCS service, and the results of execution should not be treated as correct or meaningful.
Using libquil for Quilc and QVM¶
Note
This feature is experimental and may not work for all platforms.
libquil provides the functionality of Quilc and QVM in a library that can be used without having to run Quilc and QVM as servers, which can make developing with pyQuil easier.
To use libquil
, first follow its installation instructions.
Once libquil
and its dependencies are installed, you will need to run the following command to install a compatible
version of qcs-sdk-python
:
poetry run pip install --config-settings=build-args='--features libquil' qcs-sdk-python --force-reinstall --no-binary qcs-sdk-python
You can then check that libquil
is available to pyQuil by executing the following Python code
from pyquil.diagnostics import get_report
print(get_report())
Towards the end of the output, you will see a libquil
section like below
libquil:
available: true
quilc version: 1.27.0
qvm version: 1.17.2 (077ba23)
If you do not see available: true
then re-try installation. If you continue to have issues, please report them
on github.
If installation was successful, you can now use libquil in pyQuil: the get_qc
function provides two keyword parameters quilc_client
and qvm_client
which can be set to use libquil
:
from pyquil import get_qc
from qcs_sdk.compiler.quilc import QuilcClient
from qcs_sdk.qvm import QVMClient
qc = get_qc("8q-qvm", quilc_client=QuilcClient.new_libquil(), qvm_client=QVMClient.new_libquil())
Please report issues on github.
Using qubit placeholders¶
Note
The functionality provided inline by QubitPlaceholders
is similar to writing a function which returns a
Program
, with qubit indices taken as arguments to the function.
In pyQuil, we typically use integers to identify qubits
from pyquil import Program
from pyquil.gates import CNOT, H
print(Program(H(0), CNOT(0, 1)))
H 0
CNOT 0 1
However, when running on real, near-term QPUs we care about what
particular physical qubits our program will run on. In fact, we may want
to run the same program on an assortment of different qubits. This is
where using QubitPlaceholder
s comes in.
from pyquil.quilatom import QubitPlaceholder
q0 = QubitPlaceholder()
q1 = QubitPlaceholder()
p = Program(H(q0), CNOT(q0, q1))
print(p)
H Placeholder(QubitPlaceholder(0x600002DEB5B0))
CNOT Placeholder(QubitPlaceholder(0x600002DEB5B0)) Placeholder(QubitPlaceholder(0x600002DEABB0))
Addressing qubits¶
If your program uses QubitPlaceholder
s, the placeholders must be resolved before your program can
be run. If you try to run a program with unresolved placeholders, you will get an error:
print(p.out())
RuntimeError: Qubit q4402789176 has not been assigned an index
Instead, you must explicitly map the placeholders to physical qubits. By
default, the function address_qubits()
will address qubits from 0 to
N, skipping indices that are already used in the program.
from pyquil.quil import address_qubits
print(address_qubits(p))
H 0
CNOT 0 1
The real power comes into play when you provide an explicit mapping:
print(address_qubits(p, qubit_mapping={
q0: 14,
q1: 19,
}))
H 14
CNOT 14 19
As an alternative to a mapping, you can consider using resolve_placeholders_with_custom_resolvers()
.
This method accepts any function that takes a placeholder as an argument, and returns a fixed value for that placeholder (or
None
, if you want it to remain unresolved).
q0 = QubitPlaceholder()
q1 = QubitPlaceholder()
p = Program(H(q0), CNOT(q0, q1))
qc = get_qc("2q-qvm")
def qubit_resolver(placeholder: QubitPlaceholder) -> Optional[int]:
if placeholder == q0:
return 0
if placeholder == q1:
return None
p.resolve_placeholders_with_custom_resolvers(qubit_resolver=qubit_resolver)
print(p)
H 0
CNOT 0 Placeholder(...)
Requesting a register of qubit placeholders¶
Usually, your algorithm will use an assortment of qubits. You can use
the convenience function register()
to request a
register of qubits to build your program.
qbyte = QubitPlaceholder.register(8)
p_evens = Program(H(q) for q in qbyte)
print(address_qubits(p_evens, {q: i*2 for i, q in enumerate(qbyte)}))
H 0
H 2
H 4
H 6
H 8
H 10
H 12
H 14
Classical control flow¶
Here are a couple quick examples that show how much richer a Quil program can be with classical control flow.
Warning
Dynamic control flow can have unexpected effects on readout data. See Accessing raw execution data for more information.
While loops¶
In this first example, we create a while loop by following these steps:
Declare a register called
flag_register
to use as a boolean test for looping.Initialize this register to
1
, so our while loop will execute. This is often called the loop preamble or loop initialization.Write the body of the loop in its own
Program
. This will be a program that applies an \(X\) gate followed by an \(H\) gate on our qubit.Use the
while_do()
method to add control flow.Call
resolve_label_placeholders()
to resolve the label placeholders inserted bywhile_do
.
from pyquil import Program
from pyquil.gates import *
# Initialize the Program and declare a 1 bit memory space for our boolean flag
outer_loop = Program()
flag_register = outer_loop.declare('flag_register', 'BIT')
# Set the initial flag value to 1
outer_loop += MOVE(flag_register, 1)
# Define the body of the loop with a new Program
inner_loop = Program()
inner_loop += Program(X(0), H(0))
inner_loop += MEASURE(0, flag_register)
# Run inner_loop in a loop until flag_register is 0
outer_loop.while_do(flag_register, inner_loop)
outer_loop.resolve_label_placeholders()
print(outer_loop)
DECLARE flag_register BIT[1]
MOVE flag_register[0] 1
LABEL @START_0
JUMP-UNLESS @END_0 flag_register[0]
X 0
H 0
MEASURE 0 flag_register[0]
JUMP @START_0
LABEL @END_0
Notice that the outer_loop
program applied a Quil instruction directly to a
classical register. There are several classical commands that can be used in this fashion:
NOT
which flips a classical bitAND
which operates on two classical bitsIOR
which operates on two classical bitsMOVE
which moves the value of a classical bit at one classical address into anotherEXCHANGE
which swaps the value of two classical bits
Note
The approach documented here can be used to construct a “numshots” loop in pure Quil. See the
with_loop()
method and Build a fixed-count loop with Quil for more
information.
If, then¶
In this next example, we show how to do conditional branching in the
form of the traditional if
construct as in many programming
languages. Much like the last example, we construct programs for each
branch of the if
, and put it all together by using the if_then()
method.
# Declare our memory spaces
branching_prog = Program()
ro = branching_prog.declare('ro', 'BIT')
test_register = branching_prog.declare('test_register', 'BIT')
# Construct each branch of our if-statement. We can have empty branches
# simply by having empty programs.
then_branch = Program(X(0))
else_branch = Program()
# Construct our program so that the result in test_register is equally likely to be a 0 or 1
branching_prog += H(1)
branching_prog += MEASURE(1, test_register)
# Add the conditional branching
branching_prog.if_then(test_register, then_branch, else_branch)
# Measure qubit 0 into our readout register
branching_prog += MEASURE(0, ro)
branching_prog.resolve_label_placeholders()
print(branching_prog)
DECLARE ro BIT[1]
DECLARE test_register BIT[1]
H 1
MEASURE 1 test_register[0]
JUMP-WHEN @THEN_0 test_register[0]
JUMP @END_0
LABEL @THEN_0
X 0
LABEL @END_0
MEASURE 0 ro[0]
We can run this program a few times to see what we get in the readout register ro
.
from pyquil import get_qc
qc = get_qc("2q-qvm")
branching_prog.wrap_in_numshots_loop(10)
result = qc.run(branching_prog)
print(result.get_register_map()['test_register'])
[[1]
[1]
[1]
[0]
[1]
[0]
[0]
[1]
[1]
[0]]
Sentinel based loop¶
Now that we understand how to create loops and conditionals, we can put them together to create a sentinel controlled loop. That is, we’ll repeat the body of a program until a certain condition is met. In this example, we’ll use the classic bell state program to demonstrate the concept. However, this technique can be applied to any program with a probabilistic outcome that we want to repeat until we get a desired result.
To start, let’s import everything we’ll need:
# Import some types we'll use
from typing import Optional, Tuple
# We'll use numpy to help us validate our results
import numpy as np
# We'll need to create a program and define an executor
from pyquil import Program, get_qc
# We'll use these gates in our program
from pyquil.gates import CNOT, H, X
# We'll also need the help of a few control flow instructions
from pyquil.quilbase import Halt, Qubit, MemoryReference, JumpTarget, Jump
from pyquil.quilatom import Label
Building our program¶
Adding control flow to a program introduces complexity, especially as we add more branches to the program. To manage this complexity we’ll use some of the methods we learned about in the previous sections as well as by breaking down the program into its constituent parts.
The program body¶
First, let’s define the body of our program. This is the part of the program that we’ll repeat until we get the result we desire. In this case, we’ll create a bell state between two qubits and measure them:
def body(qubits: Tuple[Qubit, Qubit], measures: MemoryReference) -> Program:
"""Constructs a bell state between the given qubit and measures them into the given memory reference."""
program = Program(H(qubits[0]), CNOT(*qubits))
program.measure_all(*zip(qubits, measures))
return program
Resetting state¶
For this program, we’ll say our desired result is that both qubits measure to 0. After an unsuccessful attempt where they measure to 1, we’ll want to reset the state of the qubits before trying again. To do this, we’ll create a program that applies an \(X\) gate to the qubits if either of them measured to 1:
def reset_bell_state(qubits: Tuple[Qubit, Qubit], measures: MemoryReference) -> Program:
"""Resets the state of the qubits if either of them measured to 1."""
program = Program()
program.if_then(measures[0], Program(X(qubits[0])))
program.if_then(measures[1], Program(X(qubits[1])))
return program
Enforcing a sentinel condition¶
Next, we’ll construct the part of our program that enforces the sentinel condition. In this case, we’ll end the program
if the given memory reference is 0, otherwise we’ll want to reset the state of our qubits and jump back to the
beginning of the program. We’ll construct the branch that ends the program using Quil’s Halt
instruction, and we’ll
accept the alternative branch as an argument to our function and pass it in the next step:
def enforce_sentinel(mem_ref: MemoryReference, else_program: Program) -> Program:
"""Ends the program if mem_ref is 0, otherwise executes else_program."""
program = Program()
# We use the `if_then` method here to help us construct our branch. As described above,
# `if_then` takes a memory reference and two programs. It constructs a branch that
# runs the first program if `mem_ref` is 1, otherwise it runs the second program.
# Since we want to end the program if `mem_ref` is 0, we pass in our HALTing
# program as the second program and the alternative branch as the first.
program.if_then(mem_ref, else_program, Program(Halt()))
return program
Putting it all together¶
With each component of our program ready, we just need to compose all the pieces:
def sentinel_program(qubits: Tuple[Qubit, Qubit]) -> Program:
# Create a label to reference the start of the program
start_label = Label("start-loop")
# Use the label to create a jump target at the beginning of the program
program = Program(JumpTarget(start_label))
# Declare a register to measure the qubits into
measures = program.declare("measures", "BIT", 2)
# Add the loop body to our program
program += body(qubits, measures)
# If the sentinel condition isn't met, then we:
reset = Program(
# Reset the state of our qubits
reset_bell_state(qubits, measures),
# Jump back to the start of the program
Jump(start_label)
)
# Finally, if both Qubits measured to 0 (our sentinel), then we want to end the program
# Otherwise, we try again.
program += enforce_sentinel(measures[0], reset)
# We used pyQuil to construct some of the branches for us, those methods use label placeholders
# to avoid conflicts with existing labels in the program, so we resolve those placeholders here.
program.resolve_label_placeholders()
return program
Testing our program¶
Now that we have our program, let’s test it out. We’ll use the sentinel_program
function to construct the program
and run it against a QVM for 1000 shots. We’ll use numpy to assert that the measures register contains only 0s. Over
1000 trials, this result would be improbable if our program didn’t work as intended.
qubits = (Qubit(0), Qubit(1))
qc = get_qc("2q-qvm")
program = sentinel_program(qubits)
program.wrap_in_numshots_loop(1000)
print(program.out())
results = qc.run(program)
measures = results.get_register_map()["measures"]
assert np.all(measures == 0)
DECLARE measures BIT[2]
LABEL @start-loop
H 0
CNOT 0 1
MEASURE 0 measures[0]
MEASURE 1 measures[1]
JUMP-WHEN @THEN_0 measures[0]
HALT
JUMP @END_0
LABEL @THEN_0
JUMP-WHEN @THEN_1 measures[0]
JUMP @END_1
LABEL @THEN_1
X 0
LABEL @END_1
JUMP-WHEN @THEN_2 measures[1]
JUMP @END_2
LABEL @THEN_2
X 1
LABEL @END_2
JUMP @start-loop
LABEL @END_0
Pauli Operator Algebra¶
Many algorithms require manipulating sums of Pauli combinations, such as
\(\sigma = \frac{1}{2}I - \frac{3}{4}X_0Y_1Z_3 + (5-2i)Z_1X_2,\) where
\(G_n\) indicates the gate \(G\) acting on qubit \(n\). We
can represent such sums by constructing PauliTerm
and PauliSum
.
The above sum can be constructed as follows:
from pyquil.paulis import ID, sX, sY, sZ
# Pauli term takes an operator "X", "Y", "Z", or "I"; a qubit to act on, and
# an optional coefficient.
a = 0.5 * ID()
b = -0.75 * sX(0) * sY(1) * sZ(3)
c = (5-2j) * sZ(1) * sX(2)
# Construct a sum of Pauli terms.
sigma = a + b + c
print(f"sigma = {sigma}")
sigma = (0.5+0j)*I + (-0.75+0j)*X0*Y1*Z3 + (5-2j)*Z1*X2
Right now, the primary thing one can do with Pauli terms and sums is to construct the exponential of the Pauli term, i.e., \(\exp[-i\beta\sigma]\). This is accomplished by constructing a parameterized Quil program that is evaluated when passed values for the coefficients of the angle \(\beta\).
Related to exponentiating Pauli sums, we provide utility functions for finding the commuting subgroups of a Pauli sum and approximating the exponential with the Suzuki-Trotter approximation through fourth order.
When arithmetic is done with Pauli sums, simplification is automatically done.
The following shows an instructive example of all three.
from pyquil.paulis import exponential_map
sigma_cubed = sigma * sigma * sigma
print(f"Simplified: {sigma_cubed}\n")
# Produce Quil code to compute exp[iX]
H = -1.0 * sX(0)
print(f"Quil to compute exp[iX] on qubit 0:\n"
f"{exponential_map(H)(1.0)}")
Simplified: (32.46875-30j)*I + (-16.734375+15j)*X0*Y1*Z3 + (71.5625-144.625j)*Z1*X2
Quil to compute exp[iX] on qubit 0:
H 0
RZ(-2) 0
H 0
exponential_map
returns a function allowing you to fill in a multiplicative
constant later. This commonly occurs in variational algorithms. The function
exponential_map
is used to compute \(\exp[-i \alpha H]\) without explicitly filling in a
value for \(\alpha\).
expH = exponential_map(H)
print(f"0:\n{expH(0.0)}\n")
print(f"1:\n{expH(1.0)}\n")
print(f"2:\n{expH(2.0)}")
0:
H 0
RZ(0) 0
H 0
1:
H 0
RZ(-2) 0
H 0
2:
H 0
RZ(-4) 0
H 0
To take it one step further, you can use Parametric compilation with exponential_map
. For instance:
ham = sZ(0) * sZ(1)
prog = Program()
theta = prog.declare('theta', 'REAL')
prog += exponential_map(ham)(theta)