Advanced Usage


If you’re running locally, remember set up the QVM and quilc in server mode before trying to use them: Setting Up Server Mode for pyQuil.

pyQuil Configuration

QCSClientConfiguration instructs pyQuil on how to connect with the components needed to compile and run programs (quilc, QVMs, 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 QCSClientConfiguration 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_PYQUIL_QVM_URL and/or QCS_SETTINGS_APPLICATIONS_PYQUIL_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:

  • quilc URL: tcp://


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.


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).


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.

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 QCSClientConfiguration

configuration = QCSClientConfiguration.load()
qc = get_qc("Aspen-X", client_configuration=configuration)

def run(program: Program):

programs = [
        "DECLARE ro BIT",
        "RX(pi) 0",
        "MEASURE 0 ro",
] * 20

with ThreadPool(5) as pool:
    results =, 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-9", endpoint_id="my_endpoint")

# Option 2
qam = QPU("Aspen-9", 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 Qubit Placeholders


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 QubitPlaceholders comes in.

from pyquil.quilatom import QubitPlaceholder
q0 = QubitPlaceholder()
q1 = QubitPlaceholder()
p = Program(H(q0), CNOT(q0, q1))
H {q4402789176}
CNOT {q4402789176} {q4402789120}

If you try to use this program directly, it will not work

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.

from pyquil.quil import address_qubits
H 0
CNOT 0 1

The real power comes into play when you provide an explicit mapping:

print(address_qubits(prog, qubit_mapping={
    q0: 14,
    q1: 19,
H 14
CNOT 14 19


Usually, your algorithm will use an assortment of qubits. You can use the convenience function QubitPlaceholder.register() to request a list 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


Classical control flow is not yet supported on the QPU.

Here are a couple quick examples that show how much richer a Quil program can be with classical control flow. In this first example, we create a while loop by following these steps:

  1. Declare a register called flag_register to use as a boolean test for looping.

  2. Initialize this register to 1, so our while loop will execute. This is often called the loop preamble or loop initialization.

  3. 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.

  4. Use the while_do() method to add control flow.

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)

DECLARE flag_register BIT[1]
MOVE flag_register 1
JUMP-UNLESS @END2 flag_register
X 0
H 0
MEASURE 0 flag_register

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 bit

  • AND which operates on two classical bits

  • IOR which operates on two classical bits

  • MOVE which moves the value of a classical bit at one classical address into another

  • EXCHANGE which swaps the value of two classical bits

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()
test_register = branching_prog.declare('test_register', 'BIT')
ro = branching_prog.declare('ro', '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)

DECLARE test_register BIT[1]
H 1
MEASURE 1 test_register
JUMP-WHEN @THEN1 test_register
X 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")
[[1], [1], [1], [0], [1], [0], [0], [1], [1], [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"
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) 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)
H 0
RZ(0) 0
H 0

H 0
RZ(-2.0) 0
H 0

H 0
RZ(-4.0) 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)