Skip to main content

Forest backend for Qiskit: run Qiskit code on Rigetti quantum computers or simulators

Project description

Forest backend for Qiskit

Allows running Qiskit code on Rigetti simulators and quantum computers by changing only two lines of your Qiskit code.

More goodies at https://quantastica.com

Install

pip install quantastica-qiskit-forest

Usage

Import ForestBackend into your Qiskit code:

from quantastica.qiskit_forest import ForestBackend

And replace Aer.get_backend with ForestBackend.get_backend.

Example

from qiskit import QuantumRegister, ClassicalRegister
from qiskit import QuantumCircuit, execute, Aer

# Import ForestBackend:
from quantastica.qiskit_forest import ForestBackend

qc = QuantumCircuit()

q = QuantumRegister(2, "q")
c = ClassicalRegister(2, "c")

qc.add_register(q)
qc.add_register(c)

qc.h(q[0])
qc.cx(q[0], q[1])

qc.measure(q[0], c[0])
qc.measure(q[1], c[1])


# Instead:
#backend = Aer.get_backend("qasm_simulator")

# Use:
backend = ForestBackend.get_backend("qasm_simulator")

# OR:
# backend = ForestBackend.get_backend("statevector_simulator")
# backend = ForestBackend.get_backend("Aspen-7-28Q-A")
# backend = ForestBackend.get_backend("Aspen-7-28Q-A", as_qvm=True)
# ...

job = execute(qc, backend=backend)
job_result = job.result()

print(job_result.get_counts(qc))

Prerequisites

Running on your local Rigetti simulator

You need to install Rigetti Forest SDK and make sure that quilc compiler and qvm simulator are running:

Open new terminal and run:

quilc -S

And in one more new terminal run:

qvm -S -c

Running on Rigetti quantum computer

  • You need to get access to Rigetti Quantum Cloud Services (QCS)

  • In your Quantum Machine Image (QMI) install this package and Qiskit

  • Reserve a QPU lattice

  • Run your code via QMI terminal or Jupyter notebook served by your QMI

Details

Syntax

ForestBackend.get_backend(backend_name = None, as_qvm = False)

Arguments

backend_name can be:

  • any valid Rigetti lattice name

OR:

  • qasm_simulator will be sent to QVM as Nq-qvm (where N is number of qubits in the circuit)

  • statevector_simulator will be executed as WavefunctionSimulator.wavefunction()

If backend name is not provided then it will act as qasm_simulator

as_qvm boolean:

  • False (default)

  • True: if backend_name is QPU lattice name, then code will execute on QVM which will mimic QPU

That's it. Enjoy! :)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

quantastica-qiskit-forest-0.9.15.tar.gz (10.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

quantastica_qiskit_forest-0.9.15-py3-none-any.whl (13.1 kB view details)

Uploaded Python 3

File details

Details for the file quantastica-qiskit-forest-0.9.15.tar.gz.

File metadata

  • Download URL: quantastica-qiskit-forest-0.9.15.tar.gz
  • Upload date:
  • Size: 10.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.7.4

File hashes

Hashes for quantastica-qiskit-forest-0.9.15.tar.gz
Algorithm Hash digest
SHA256 970314f0e3e14183bef961e2db9eeaf0dcf93fc2289d5dbe188ac2a5451509ff
MD5 428c5f5281524c9662ef60669f582cfd
BLAKE2b-256 5d4fc1c885faa079e60f57934548d0b9d8c06c274359e67986e76ba1c56e885c

See more details on using hashes here.

File details

Details for the file quantastica_qiskit_forest-0.9.15-py3-none-any.whl.

File metadata

  • Download URL: quantastica_qiskit_forest-0.9.15-py3-none-any.whl
  • Upload date:
  • Size: 13.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.40.2 CPython/3.7.4

File hashes

Hashes for quantastica_qiskit_forest-0.9.15-py3-none-any.whl
Algorithm Hash digest
SHA256 560193cdc6c99efa882ed108120cadbfc98d0abff1dcb6a50029c6c0694d0357
MD5 6f6247858a216fce98ad689c42357ee7
BLAKE2b-256 22ce187175698df9089e76a2fe7f05e793732b269fc24767e0c734cc52175165

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page