The Quil Compiler¶
Expectations for Program Contents¶
The QPUs have much more limited natural gate sets than the standard gate set offered by pyQuil: on
Rigetti QPUs, the gate operators are constrained to lie in
XY; and the gates are required to act on physically available hardware (for single-qubit gates,
this means acting only on live qubits, and for qubit-pair gates, this means acting on neighboring
qubits). However, as a programmer, it is often (though not always) desirable to to be able to write
programs which don’t take these details into account. This generally leads to more portable code if
one isn’t tied to a specific set of gates or QPU architecture. To ameliorate these limitations, the
Rigetti software toolkit contains an optimizing compiler that translates arbitrary Quil to native
Quil and native Quil to executables suitable for Rigetti hardware.
Interacting with the Compiler¶
After installing the SDK, the Quil Compiler,
quilc is available on your
local machine. You can initialize a local
quilc server by typing
quilc -R into your
terminal. You should see the following message.
$ quilc -S +-----------------+ | W E L C O M E | | T O T H E | | R I G E T T I | | Q U I L | | C O M P I L E R | +-----------------+ Copyright (c) 2018 Rigetti Computing. ... - Launching quilc. ... - Spawning server at (tcp://*:5555) .
To get a description of
quilc and its options and examples of command line use, see the quilc README or type
man quilc in your terminal.
QuantumComputer object supplied by the function
pyquil.api.get_qc() comes equipped with a
connection to your local Rigetti Quil compiler. This can be accessed using the instance method
as in the following:
from pyquil.quil import Pragma, Program from pyquil.api import get_qc from pyquil.gates import CNOT, H qc = get_qc("9q-square-qvm") ep = qc.compile(Program(H(0), CNOT(0,1), CNOT(1,2))) print(ep.program)
RZ(pi/2) 0 RX(pi/2) 0 RZ(-pi/2) 1 RX(pi/2) 1 CZ 1 0 RX(-pi/2) 1 RZ(-pi/2) 2 RX(pi/2) 2 CZ 2 1 RZ(-pi/2) 0 RZ(-pi/2) 1 RX(-pi/2) 2 RZ(pi/2) 2
The compiler connection is also available directly via the property
precise class of this object changes based on context (e.g.,
QVMCompiler), but it always conforms to the interface laid out by
compiler.quil_to_native_quil(program, *, protoquil): This method converts a Quil program into native Quil, according to the ISA that the compiler is initialized with. The input parameter is specified as a
Programobject. The optional
protoquilkeyword argument instructs the compiler to restrict both its input and output to protoquil (Quil code that can be executed on a QPU). If the server is started with the
-Poption, or you specify
Programobject will be equipped with a
.metadataproperty that gives extraneous information about the compilation output (e.g., gate depth, as well as many others). This call blocks until Quil compilation finishes.
compiler.native_quil_to_executable(nq_program): This method converts a native Quil program, which is promised to consist only of native gates for a given ISA, into an executable suitable for submission to one of a QVM or a QPU. This call blocks until the executable is generated.
The instance method
qc.compile described above is a combination of these two methods: first the
incoming Quil is nativized, and then that is immediately turned into an executable. Accordingly,
the previous example snippet is identical to the following:
from pyquil.quil import Pragma, Program from pyquil.api import get_qc from pyquil.gates import CNOT, H qc = get_qc("9q-square-qvm") p = Program(H(0), CNOT(0,1), CNOT(1,2)) np = qc.compiler.quil_to_native_quil(p, protoquil=True) print(np.metadata) ep = qc.compiler.native_quil_to_executable(np) print(ep.program)
If your circuit is sufficiently complex the compiler may require more time than is permitted by default. To change this timeout, use the compiler_timeout option on get_qc:
qc = get_qc(..., compiler_timeout=100) # 100 seconds
Legal compiler input¶
The QPU is not able to execute all possible Quil programs. At present, a Quil program qualifies for execution if has the following form:
The program may begin with a
RESETinstruction. (If provided, the QPU will actively reset the state of the quantum device to the ground state before program execution. If omitted, the QPU will wait for a relaxation period to pass before program execution instead.)
This is then followed by a block of native quantum gates. A gate is native if it is of the form
RZ(θ)for any value
RX(k*π/2)for an integer
CZ q0 q1for
q1a pair of qubits participating in a qubit-qubit interaction. Some devices provide the
XY(θ) q0 q1two qubit gate.
This is then followed by a block of
To instruct the compiler to produce Quil code that can be executed on a QPU, you can use the
protoquil keyword in a call to
compiler.quil_to_native_quil(program, protoquil=True) or
If your compiler server is started with the protoquil option
-P (as is the case for your
JupyterLab notebook’s compiler) then specifying
protoquil=False will override the server
and forcefully disable protoquil. Specifying
protoquil=None defers to the server’s choice.
When your compiler is started with the
-P option, the
will return both the compiled program and a dictionary of statistics for the compiled program. This
dictionary contains the keys
final_rewiring: see section below on rewirings.
gate_depth: the longest subsequence of compiled instructions where adjacent instructions share resources.
gate_depthbut restricted to multi-qubit gates.
gate_volume: total number of gates in the compiled program.
program_duration: program duration with parallel executation of gates (using hard-coded values of individual gate durations).
qpu_runtime_estimation: estimated runtime on a Rigetti QPU (in milliseconds). This is extrapolated from a single shot of a 16Q program with final measurements on all 16 qubits. If you are running a parametric program then you should estimate the total runtime as
size of parameter space * estimated runtime of single shot. This should be treated only as an approximation.
program_fidelity: the estimated fidelity of the compiled program.
topological_swaps: the number of topological swaps incurred during compilation of the program.
For example, to inspect the
qpu_runtime_estimation you might do the following:
from pyquil import get_qc, Program # If you have a reserved QPU, use it here qc = get_qc("Aspen-X") # Otherwise use a QVM # qc = get_qc("8q-qvm") # Likely you will have a more complex program: p = Program("RX(pi) 0") native_p = qc.compiler.quil_to_native_quil(p) # The program will now have only native gates print(native_p) # And also a dictionary, with the above keys print(native_p.native_quil_metadata["qpu_runtime_estimation"])
Region-specific compiler features through PRAGMA¶
The Quil compiler can also be communicated with through
PRAGMA commands embedded in the Quil
The interface to the Quil compiler from pyQuil is under construction, and some of the
PRAGMA directives will soon be replaced by finer-grained method calls.
The compiler can be circumvented in user-specified regions. The start of such a region is denoted by
PRAGMA PRESERVE_BLOCK, and the end is denoted by
PRAGMA END_PRESERVE_BLOCK. The Quil
compiler promises not to modify any instructions contained in such a region.
If a preserved block is not legal QPU input, then it is not guaranteed to execute or it may produced unexpected results.
The following is an example of a program that prepares a Bell state on qubits 0 and 1, then performs
a time delay to invite noisy system interaction before measuring the qubits. The time delay region
is marked by
PRAGMA PRESERVE_BLOCK and
PRAGMA END_PRESERVE_BLOCK; without these delimiters,
the compiler will remove the identity gates that serve to provide the time delay. However, the
regions outside of the
PRAGMA region will still be compiled, converting the Bell state preparation
to the native gate set.
DECLARE ro BIT # prepare a Bell state H 0 CNOT 0 1 # wait a while PRAGMA PRESERVE_BLOCK I 0 I 1 I 0 I 1 # ... I 0 I 1 PRAGMA END_PRESERVE_BLOCK # and read out the results MEASURE 0 ro MEASURE 1 ro
The compiler can sometimes arrange gate sequences more cleverly if the user gives it hints about
sequences of gates that commute. A region containing commuting sequences is bookended by
PRAGMA COMMUTING_BLOCKS and
PRAGMA END_COMMUTING_BLOCKS; within such a region, a given
commuting sequence is bookended by
PRAGMA BLOCK and
Lying to the compiler about what blocks can commute can cause incorrect results.
The following snippet demonstrates this hinting syntax in a context typical of VQE-type algorithms: after a first stage of performing some state preparation on individual qubits, there is a second stage of “mixing operations” that both re-use qubit resources and mutually commute, followed by a final rotation and measurement. The following program is naturally laid out on a ring with vertices (read either clockwise or counterclockwise) as 0, 1, 2, 3. After scheduling the first round of preparation gates, the compiler will use the hinting to schedule the first and third blocks (which utilize qubit pairs 0-1 and 2-3) before the second and fourth blocks (which utilize qubit pairs 1-2 and 0-3), resulting in a reduction in circuit depth by one half. Without hinting, the compiler will instead execute the blocks in their written order.
DECLARE ro BIT # Stage one H 0 H 1 H 2 H 3 # Stage two PRAGMA COMMUTING_BLOCKS PRAGMA BLOCK CNOT 0 1 RZ(0.4) 1 CNOT 0 1 PRAGMA END_BLOCK PRAGMA BLOCK CNOT 1 2 RZ(0.6) 2 CNOT 1 2 PRAGMA END_BLOCK PRAGMA BLOCK CNOT 2 3 RZ(0.8) 3 CNOT 2 3 PRAGMA END_BLOCK PRAGMA BLOCK CNOT 0 3 RZ(0.9) 3 CNOT 0 3 PRAGMA END_BLOCK PRAGMA END_COMMUTING_BLOCKS # Stage three H 0 H 1 H 2 H 3 MEASURE 0 ro MEASURE 1 ro MEASURE 2 ro MEASURE 3 ro
When a Quil program contains multi-qubit instructions that do not name qubit-qubit links present on a
target device, the compiler will rearrange the qubits so that execution becomes possible. In order to
help the user understand what rearrangement may have been done, the compiler emits comments at various
points in the raw Quil code (which is not currently visible from a pyQuil
# Entering rewiring and
# Exiting rewiring. From the perspective of the user, both
comments serve the same purpose:
# Entering rewiring: #(n0 n1 ... nk) indicates that the logical
j in the program has been assigned to lie on the physical qubit labeled
the device. This is strictly for human-readability: these comments are discarded and have no effect.
When the compiler needs to move an instruction’s qubits closer it will insert
SWAP gates which
can be costly. If, however, the swaps are inserted at the very beginning of the program, the
compiler can treat them as virtual swaps which do not appear in the resulting program but instead
affect the initial rewiring of the program.
For example, consider running a
CZ on non-neighboring qubits on a linear device:
import networkx as nx from pyquil import Program, get_qc from pyquil.api._quantum_computer import _get_qvm_with_topology from pyquil.gates import CZ graph = nx.from_edgelist([(0, 1), (1, 2)]) qc = _get_qvm_with_topology(name="line", topology=graph) p = Program(CZ(0, 2)) print(qc.compile(p).program) CZ 2 1
We see that the resulting program has only a single
CZ even though the original program would
usually require the insertion of a
SWAP gate. The compiler instead opted to just relabel (or
rewire) the qubits, thus not inflating the number of gates in the result.
For larger and more complex programs (with more entanglement) it may not always be possible to avoid
inserting swaps. For example, the following program requires a
SWAP that increases its gate depth:
import networkx as nx from pyquil import Program, get_qc from pyquil.api._quantum_computer import _get_qvm_with_topology from pyquil.gates import H, CZ graph = nx.from_edgelist([(0, 1), (1, 2)]) qc = _get_qvm_with_topology(name="line", topology=graph) p = Program(CZ(0, 1), H(0), CZ(1, 2), CZ(0, 2)) print(qc.compile(p).program) CZ 2 1 RX(-pi/2) 2 RX(-pi/2) 2 CZ 2 1 CZ 1 0 RZ(pi/2) 1 RX(-pi/2) 1 RX(-pi/2) 1 RX(-pi/2) 2 RX(pi/2) 2 XY(pi) 2 1 RX(-pi/2) 1 CZ 1 0 RZ(pi/2) 1 RX(pi/2) 2 RX(pi/2) 2
SWAP gates generally cost three
CZ gates or three
XY gates. However, if your device
XY gates available, then the compiler can produce a
SWAP gate that
uses only two two-qubit gates (one
CZ and one
In addition, you have some control over how the compiler constructs its
rewiring, which is controlled by
PRAGMA INITIAL_REWIRING. The syntax is as follows.
# <type> can be NAIVE, RANDOM, PARTIAL, or GREEDY # # The double quotes are required. PRAGMA INITIAL_REWIRING "<type>"
Including this before any non-pragmas will allow the compiler to alter its rewiring behavior.
The default initial rewiring strategy¶
Each initial rewiring strategy is described in more detail after the discussion about defaults.
INITIAL_REWIRING pragma is provided the compiler will choose one of two options
depending on the program:
NAIVE: The qubits used in all instructions in the program satisfy the topological constraints of the device.
For example, if your program consists of two-qubit instructions where the qubits in each instruction are nearest neighbors on the device, the compiler will employ the native strategy:
from pyquil import Program, get_qc from pyquil.gates import CZ qc = get_qc("Aspen-X", as_qvm=True) p = Program(CZ(3, 4)) print(qc.compile(p).program) CZ 3 4
In the above example, CZ 3 4 touches qubits that are already nearest neighbors (and support a CZ instruction) and so the compiler employs the naive strategy (and thus does not rewire those qubits to use better ones).
If however, the program uses qubits that must be rewired, then the compiler defaults to the partial strategy:
from pyquil import Program, get_qc from pyquil.gates import CZ qc = get_qc("Aspen-X", as_qvm=True) p = Program(CZ(3, 4)) print(qc.compile(p).program) RZ(-pi/2) 0 RX(pi/2) 0 RZ(-pi/2) 0 RZ(pi/2) 1 XY(pi) 1 0 RZ(pi/2) 1 RX(pi/2) 1 RZ(-pi/2) 1 XY(pi) 1 0 RZ(-pi/2) 0 RX(-pi/2) 0
In this mode, the compiler chooses the
naive mapping between logical qubits and physical qubits,
where logical qubit
i is assigned to physical qubit
i. With this initial rewiring, the
compiler will generally not move an instruction’s qubits around even if it results in a poor
execution fidelity. For example assume that
Aspen-X has a low-fidelity
CZ 0 1, then
compiling this program with naive rewiring will not move the
CZ to a better qubit pair:
from pyquil import Program, get_qc from pyquil.gates import CZ qc = get_qc("Aspen-X", as_qvm=True) p = Program('PRAGMA INITIAL_REWIRING "NAIVE"', CZ(0, 1)) print(qc.compile(p).program) PRAGMA INITIAL_REWIRING "NAIVE" CZ 0 1
If, however, your program includes an instruction that does not use neighboring qubits the compiler will be required to insert swaps (virtual or real, see swaps) that might affect the logical-physical qubit mapping. For example,
from pyquil import Program, get_qc from pyquil.gates import CZ qc = get_qc("Aspen-X", as_qvm=True) p = Program('PRAGMA INITIAL_REWIRING "NAIVE"', CZ(0, 2)) print(qc.compile(p).program) PRAGMA INITIAL_REWIRING "NAIVE" CZ 6 5
In the above program
CZ 0 2 is not a native instruction (meaning it cannot be directly executed
on the target device) and so the compiler must insert a swap (virtual, in this case) into the
program. When rewiring must occur in this mode it is not guaranteed that the resulting program
will have optimal fidelity.
In this mode, the compiler begins with an empty mapping from logical qubits to physical qubits. During the progression of compilation this mapping will be filled-in, and thus at any point the mapping is said to be partial. Generally this gives the compiler the opportunity to assign a logical-to-physical qubit mapping that optimizes the fidelity of the resulting program by incorporating fidelity information about any qubit in the device ISA.
For example, if the instruction
CZ 0 1 has poor fidelity, under the partial rewiring strategy
the compiler can find an alternative that improves the program fidelity:
from pyquil import Program, get_qc from pyquil.gates import CZ qc = get_qc("Aspen-X", as_qvm=True) p = Program('PRAGMA INITIAL_REWIRING "PARTIAL"', CZ(0, 1)) print(qc.compile(p).program) PRAGMA INITIAL_REWIRING "PARTIAL" CZ 20 27
Here the compiler sees that the instruction
CZ 20 27 will produce a program with better fidelity
and so opts to reassign qubits in the original program.
In this mode, the compiler chooses an initial mapping between logical and physical qubits based upon
a greedy optimization of the distances between qubits used in the program and those available on
the device. When compared to the
PARTIAL strategy it is generally more efficient because it uses
a simple heuristic; however, it will also produce a program with worse overall fidelity. If
compilation feels too slow and you’re willing to trade fidelity for compilation speed, then you may
see success with this strategy.
Which strategy should I use?¶
Generally, as quantum software engineers, we want to maximize the execution fidelity of our programs. In other cases, however, for example in QCVV, we want to have more control about where instructions are placed.
Recommended initial rewiring strategy
Maximize program execution fidelity
Preserve, where possible, the qubits used in the input program
Faster qubit allocation at expense of fidelity
Note that each of these have drawbacks described in the sections above.
Common Error Messages¶
The compiler itself is subject to some limitations, and some of the more commonly observed errors follow:
! ! ! Error: Matrices do not lie in the same projective class.The compiler attempted to decompose an operator as native Quil instructions, and the resulting instructions do not match the original operator. This can happen when the original operator is not a unitary matrix, and could indicate an invalid
DEFGATEblock. In some rare circumstances, it can also happen due to floating point precision issues. In the latter case, the issue is resolved simply by recompiling the program. If you issue cannot be solved, please contact email@example.com or post an issue to the github project page.