Note
If you're running locally, remember set up the QVM and quilc in server mode before trying to use them: server
.
Network endpoints for the Rigetti Forest infrastructure and information pertaining to QPU access are stored in a pair of configuration files. These files are located by default at ~/.qcs_config
and ~/.forest_config
. The location can be changed by setting the environment variables QCS_CONFIG
or FOREST_CONFIG
to point to the new location. When running on a QMI, the configuration files are automatically managed so as to point to the correct endpoints.
Authentication credentials for Forest server are read from ~/.qcs/user_auth_token
. You may download user auth credentials at https://qcs.rigetti.com/auth/token. When running on a QMI, authentication credentials are read from ~/.qcs/qmi_auth_token
.
When running locally, configuration files are not necessary. Thus, the average user will not have to do any work to get their configuration files set up.
If for some reason you want to use an atypical configuration, you may need to modify these files.
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 hits for device information and for the 2.0 endpoints,user_id
stores a Forest 2.0 user ID, andexec_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.
The Forest config file on any QMI has these contents, with specific IP addresses filled in:
# .forest_config
[Rigetti Forest]
qpu_endpoint_address = None
qvm_address = http://10.1.165.XX:5000
compiler_server_address = tcp://10.1.165.XX:5555
where
qpu_endpoint_address
is the endpoint where pyQuil will try to communicate with the QPU orchestrating service during QPU-engagement. It may not appear until your QMI engages, and furthermore will have no effect if you are running locally. It's best to leave this alone. If you obtain access to one of our QPUs, we will fill it in for you.qvm_address
is the endpoint where pyQuil will try to communicate with the Rigetti Quantum Virtual Machine. On a QMI, this points to the provided QVM instance. On a local installation, this should be set to the server endpoint for a locally running QVM instance. However, pyQuil will use the default valuehttp://localhost:5000
if this file isn't found, which is the correct endpoint when you run the QVM locally withqvm -S
.compiler_server_address
: This is the endpoint where pyQuil will try to communicate with the compiler server. On a QMI, this points to a provided compiler server instance. On a local installation, this should be set to the server endpoint for a locally runningquilc
instance. However, pyQuil will use the default valuetcp://localhost:5555
if this isn't set, which is the correct endpoint when you runquilc
locally withquilc -S
.
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.
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
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
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 :py
~pyquil.quil.Program
. This will be a program that applies an X gate followed by an H gate on our qubit. - Use the :py
~pyquil.quil.Program.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 :py~pyquil.quil.Program.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]]
Many algorithms require manipulating sums of Pauli combinations, such as 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)Z1X2
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 [ − iβσ]. This is accomplished by constructing a parameterized Quil program that is evaluated when passed values for the coefficients of the angle β.
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)Z1X2
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 [ − iαH] without explicitly filling in a value for α.
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)