The Quantum Computer¶

The Quantum Virtual Machine (QVM)¶

The Rigetti Quantum Virtual Machine is an implementation of the Quantum Abstract Machine from A Practical Quantum Instruction Set Architecture. [1] It is implemented in ANSI Common LISP and executes programs specified in the Quantum Instruction Language (Quil).

Quil is an opinionated quantum instruction language: its basic belief is that in the near term quantum computers will operate as coprocessors, working in concert with traditional CPUs. This means that Quil is designed to execute on a Quantum Abstract Machine that has a shared classical/quantum architecture at its core.

The QVM is a wavefunction simulation of unitary evolution with classical control flow and shared quantum classical memory.

Using the QVM¶

After downloading the SDK, the QVM is available on your local machine. You can initialize a local QVM server by typing qvm -S into your terminal. You should see the following message.

\$ qvm -S
******************************
* Welcome to the Rigetti QVM *
******************************

(Configured with 2048 MiB of workspace and 8 workers.)

[2018-11-06 18:18:18] Starting server on port 5000.


For a detailed description of how to use the qvm from the command line, see The QVM manual page.

Once the QVM is serving requests, we can run the following pyQuil program to get a QuantumComputer object which will use the QVM.

from pyquil import get_qc, Program
from pyquil.gates import *
qc = get_qc('9q-square-qvm')


One executes quantum programs on the QVM using the .run(...) method, intended to closely mirror how one will execute programs on a real QPU. We also offer a Wavefunction Simulator (formerly a part of the QVM object), which allows users to contruct and inspect wavefunctions of quantum programs. Learn more about the Wavefunction Simulator here. For information on constructing quantum programs, please refer back to Programs and Gates.

The .run(...) method¶

The .run(...) method takes in a compiled program. You are responsible for compiling your program before running it. Remember to also start up a quilc compiler server, too, with quilc -S.

p = Program()
ro = p.declare('ro', 'BIT', 1)
p += X(0)
p += MEASURE(0, ro[0])
p += MEASURE(1, ro[1])
p.wrap_in_numshots_loop(5)
executable = qc.compile(p)
results = qc.run(executable)
print(results)


The results returned are a list of lists of integers. In the above case, that’s

[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0]]


Let’s unpack this. The outer list is an enumeration over the trials; the argument given to wrap_in_numshots_loop will match the length of results.

The inner list, on the other hand, is an enumeration over the results stored in the memory region named ro, which we use as our readout register. We see that the result of this program is that the memory region ro[0] now stores the state of qubit 0, which should be 1 after an $$X$$-gate. See Declaring Memory and Measurement for more details about declaring and accessing classical memory regions.

Simulating the QPU using the QVM¶

The QVM is a powerful tool for testing quantum programs before executing them on the QPU.

qc = get_qc("QuantumComputerName")
qc = get_qc("QuantumComputerName-qvm")


By simply providing -qvm in the device name, all programs executed on this QVM will, have the same topology as the named QPU. To learn how to add noise models to your virtual QuantumComputer instance, check out Noise and Quantum Computation.

The Quantum Processing Unit¶

Coming soon: Detailed information about how to use get_qc() to target a QPU.