qiskit | source SDK for working with quantum computers
kandi X-RAY | qiskit Summary
kandi X-RAY | qiskit Summary
The four elements of Qiskit are the essential parts that give Qiskit its power. and if you want to run your experiments on real systems you need the 5th element.
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Top functions reviewed by kandi - BETA
- Setup extension
- Install api sources
- Return a dictionary of the current version
- Copies a package from a given package
- Extend the html context
- Return the language label
- Retrieve the label of the current version
- Get current translation
- Build the table
- Get the date of a given version
- Extract the version information from git tags
- Build table
- Return the URL for a translation
- Add content prefix
- Gets the URL for a file
- Run a shell command
- Create a temporary directory
qiskit Key Features
qiskit Examples and Code Snippets
Lines of Code : 57License : Permissive (Apache-2.0)
Copy>>> from qiskit.optimization import QuadraticProgram
>>> from qiskit.optimization.algorithms import MinimumEigenOptimizer
>>> from dwave.plugins.qiskit import DWaveMinimumEigensolver
...
>>> # Construct a simple qu ...
def custom_hook_1(qasm: str) -> str:
new_qasm = ...
return new_qasm
def custom_hook_2(qasm: str) -> str:
new_qasm = ...
return new_qasm
job = execute(circuit, backend, shots=10, before_compile=[custom_hook_1, custom_hook_2])
from qiskit import execute
from qiskit_rigetti import RigettiQCSProvider, QuilCircuit
# Get provider and backend
p = RigettiQCSProvider()
backend = p.get_simulator(num_qubits=2, noisy=True) # or p.get_backend(name='Aspen-9')
# Create a Bell state spsa = SPSA(maxiter=300)
ry = TwoLocal(qubitOp.num_qubits, 'ry', 'cz', reps=7, entanglement='linear')
vqe = VQE(qubitOp, ry, spsa, quantum_instance=quantum_instance)
result = vqe.run(quantum_instance)
pip show qiskit-textbook
Name: qiskit-textbook
Version: 0.1.0
Summary: A collection of widgets, tools and games for using along
the Qiskit Textbook. See the textbook and a list of contributors at qiskit.org/textbfrom qiskit.compiler import assemble
my_qobj = assemble(c)
result = simulator.run(my_qobj).result()
a, x_sum = reduce(lambda x,y: (x[0].compose(y[0], y[1]), x[1][0]+y[1][0]), zip(circli, cs), (qcla, 0))
pip install --ignore-installed qiskit-terra qiskit-aer
from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector
from kaleidoscope import qsphere
circ = QuantumCircuit(1)
circ.x(0)
circ.h(0)
circ.x(0)
circ.h(0)
circ.x(0)
state = Statevector.from_instruction(circ)
qsphere# IBMQ Configure
# IBMQ.save_account(os.environ.get('IBM'))
# IBMQ.load_account()
# provider = IBMQ.get_provider('ibm-q')
# qcomp = provider.get_backend('ibmq_16_melbourne')
# Get backend using the Aer provider
bacCommunity Discussions
Trending Discussions on qiskit
QUESTION
While implementing a hybrid quantum LSTM model, the model is overfitting and thus giving low accuracy. I tried setting dropout = 1 in nn.LSTM but no improvement. I have used a single hidden layer. How do I add the dropout layer to reduce overfitting?
Model parameters:
...ANSWER
Answered 2021-Oct-17 at 12:46Pytorch's LSTM layer takes the dropout parameter as the probability of the layer having its nodes zeroed out. When you pass 1, it will zero out the whole layer. I assume you meant to make it a conventional value such as 0.3 or 0.5.
As @ayandas says above, too, it applies dropout to each layer except the last (see the link above), so it won't work for a single-layer LSTM. You can always apply your own dropout using nn.dropout at the output of your LSTM layers if you wish.
QUESTION
Surveying the QML module in Qiskit for a quantum neural network project and wondering if there is support to add noise models and run noisy simulations?
Going through this tutorial and wondering how one would define a noise model in the argument of qnn.forward when doing a batched forward pass.
ANSWER
Answered 2021-Aug-09 at 11:40You can create a noisy simulator like in the snippet below and then use it when you create a QuantumInstance:
QUESTION
I tried to implement Deutsch algorithm using qiskit. The following is a code.
...ANSWER
Answered 2021-Aug-01 at 05:36Deutsch algorithm applies the X gate to the qubit you use for phase kickback trick, to prepare it in the |-⟩ state before applying the oracle. Your implementation applies it to the "data" qubit instead, so that the combined effect of the algorithm (after H gates cancel out) is just preparing the data qubit in |1⟩ state.
QUESTION
I'm using the qiskit textbook, and it creates a QuantumCircuit and then draws the circuit, and it looks like this:
I see the same result when running the textbook as a jupyter notebook in IBM's quantum lab.
However, when I download the textbook as a jupyter notebook and run it myself locally, it looks like this:
I don't like this very much, and I think I am missing something simple. The code that is running is exactly the same. I am using MacOS 11.4 (Big Sur). The following code is sufficient to show a difference when I run it online vs. locally:
...ANSWER
Answered 2021-Jun-05 at 17:40Because Qiskit has multiple drawers. Those are:
textmpllatexlatex_source.
The drawer you see in the IBM Quantum Lab is the one based on Matplotlib. You can get the same output by qc.draw('mpl').
To set a default, you can change (or create if does not exist) the file ~/.qiskit/settings.conf) with the entry circuit_drawer = mpl.
QUESTION
I had to try the example of qiskit’s Traveling Salesman Problem with 3 nodes and executing it at IBM backend called simulator_statevector.Can execute and get the result normally.
But when trying to solve the TSP problem with more than 3 nodes,I changed n = 3 to n = 4.
...ANSWER
Answered 2021-Jun-05 at 12:02I found the answer, my method is to increase the Ansat number of reps from 5 to 7.
from solving TSP 4 node problem
QUESTION
I had installed the qiskit-textbook distribution of Qiskit using this command : pip install git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src
The tutorial then said to change the contents of the configuration file located at ~/.qiskit/settings.conf but I can't find the folder at ~/. I can't find the iPython configuration file either at ~/.ipython/profile_default/ipython_kernel_config.py for some reason.
How do I fix this?
...ANSWER
Answered 2021-May-27 at 14:53QUESTION
I've installed Qiskit-textbook by pip install git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src. But I don't know where is it downloaded
ANSWER
Answered 2021-May-11 at 12:41That command installs the Qiskit Textbook package, which is a Python package containing some of the problems and widgets used in the textbook. You can see the location of an installed package using pip show :
QUESTION
I am working on a program for Qiskit, but I am getting a strange error (one that I have not gotten in the past) when I try to simulate a circuit. Here is a minimal example producing the error:
...ANSWER
Answered 2021-May-03 at 12:24I believe you need to assemble the transpiled circuit in a qobj before running it :
QUESTION
When I try to train the model I get the following error:
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not str
The code I'm using is:
...ANSWER
Answered 2021-Apr-16 at 07:27The problem is that data is a dictionary and when you unpack it the way you did (X_train, Y_train = data) you unpack the keys while you are interested in the values.
refer to this simple example:
QUESTION
I want to use the BasisTranslator in Qiskit to process my circuit to base gates.
One of the constructor parameters for BasisTranslator is EquivalenceLibrary. I attempted creating a parameter-less EquivalenceLibrary() and pass it to a BasisTranslator, but the code fails, saying it cannot translate gates like MCT or CCX into the default basis.
ANSWER
Answered 2021-Mar-31 at 10:46The easiest way is to leverage the session equivalence library that is included with qiskit (at qiskit.circuit.equivalence_library.SessionEquivalenceLibrary) which is a prebuilt equivalence library which includes all the standard library gates. You can use it with something like:
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