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 Server Mode for PyQuil.
PyQuil Configuration Files¶
Network endpoints for the Rigetti Forest infrastructure and information pertaining to QPU access are
stored in configuration files located by default at ~/.qcs_config
and ~/.forest_config
.
The file paths can be customized by setting the environment variables QCS_CONFIG
or
FOREST_CONFIG
, respectively.
These files are present on your QMI, and the default configuration will work for most users.
Authentication credentials for Forest server are read from ~/.qcs/user_auth_token
(note: no file extension). You can download this token from https://qcs.rigetti.com/auth/token.
The default QCS config file on any QMI looks similar to the following:
# .qcs_config
[Rigetti Forest]
url = https://forest-server.qcs.rigetti.com
user_id = 4fd12391-11eb-52ec-35c2-262765ae4c4f
[QPU]
exec_on_engage = bash exec_on_engage.sh
where
url
is the endpoint that pyQuil uses for device information
exec_on_engage
specifies the shell command that the QMI will launch when the QMI becomes QPU-engaged. It would have no effect if you are running locally, but is important if you are running on the QMI. By default, it runs theexec_on_engage.sh
shell script. It’s best to leave the configuration as is, and edit that script. More documentation aboutexec_on_engage.sh
can be found in the QCS docs here.
Note
PyQuil itself reads these values out using the helper class pyquil._config.PyquilConfig
. PyQuil users should not
ever need to touch this class directly.
If you’d like to change your pyQuil configuration, you can do so in your runtime environment or by editing these configuration files. All configuration options derive their values from one of three places, in decreasing order of precedence:
The runtime environment, set using export in Unix-based platforms, and setx in Windows.
QCS configuration files noted above:
.qcs_config
and.forest_config
, or their user-configured file paths. The two are not interchangeable; each config value will be looked up in exactly one of these files.The default values specified in
pyquil.api._config
. These should not be changed by any Pyquil user, as doing so may lead to difficult-to-debug behavior.
These are all the configuration options available to you, and where they can be set:
Setting |
Environment |
Configuration File |
---|---|---|
Forest Server URL Source of device information |
|
Key: |
Dispatch URL Provides QPU engagement authorization |
|
Key: |
User Authentication Token Path File path to the authentication token obtained from qcs. |
|
Key: |
QCS URL QCS website |
|
Key: |
QPU URL Send binaries to the QPU |
|
Key: |
QVM URL Simulator |
|
Key: |
QPU Compiler URL Send native Quil and receive a binary |
|
Key: |
Local Compiler URL Send Quil and receive native Quil |
|
Key: |
The configuration options omitted from this table but present in pyquil.api._config
are
deprecated and should not be used.
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 {q4402789176}
CNOT {q4402789176} {q4402789120}
If you try to use this program directly, it will not work
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.
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(prog, qubit_mapping={
q0: 14,
q1: 19,
}))
H 14
CNOT 14 19
Register¶
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¶
Note
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:
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.
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)
print(outer_loop)
DECLARE flag_register BIT[1]
MOVE flag_register 1
LABEL @START1
JUMP-UNLESS @END2 flag_register
X 0
H 0
MEASURE 0 flag_register
JUMP @START1
LABEL @END2
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
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)
print(branching_prog)
DECLARE test_register BIT[1]
DECLARE ro BIT[1]
H 1
MEASURE 1 test_register
JUMP-WHEN @THEN1 test_register
JUMP @END2
LABEL @THEN1
X 0
LABEL @END2
MEASURE 0 ro
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)
qc.run(branching_prog)
[[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"
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) 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) 0
H 0
2:
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)