isqdeployer 0.0.50

Python lib for using isq

isqdeployer

What is isqdeployer

isqdeployer serves as an Object-Oriented Interface for isq, offering a pure Python alternative that eliminates the need for isq syntax. It also simplifies the installation process, automatically configuring everything required for isqc. Notably, isqdeployer maintains the crucial advantage of optimizing gates at the quantum circuit level, benefiting quantum algorithms.

Installation

Execute the following command without any additional steps.

pip install isqdeployer

Usuage

A simple example

In the following example, we define a quantum circuit with 2 qubits. It implements an H gate on the second qubit and concludes with a measurement of all qubits.

from isqdeployer import Circuit 

cir = Circuit(2) # define a circuit
cir.H(1)         # set gate
cir.setMeasurement([0,1]) # set measurement
cir.runJob()     # run           

The output is: [{'00': 0.4999999999999999, '01': 0.4999999999999999}].

A complex example: Rabi oscillation

Consider a single spin (or qubit) initially in the state $|0\rangle$. When subjected to a wave pulse directly along the X-axis, described by an interacting Hamiltonian $H=X$ (Pauli $X$), the qubit state will oscillate between $|0\rangle$ and $|1\rangle$. The following code simulates this experiment.

from isqdeployer import Circuit 
from isqdeployer.circuitDeployer import PauliHamDeployer 
from isqdeployer.utils.pauliHamiltonian import PauliHamiltonian
import numpy as np 
import matplotlib.pyplot as plt 

ham = PauliHamiltonian(nq=1) # define a Hamiltonian
ham.setOneTerm(xi=1.0,p=[1]) # Hamiltonian = Pauli X, (0,1,2,3) represents (I,X,Y,Z)

cir = Circuit(
    nq=1, # only 1 qubit
    workDir="/home/user/Desktop/test", # see how it works by inspecting internal process 
    isInputArg=True, # use additional parameter (t) to increase the speed of the calculation
    ) # circuit, init state is |0>

t = cir.getInputArg(FI='F', id=0) # set the circuit to contains a parameter t

dep = PauliHamDeployer(circuit=cir) # use a deployer to implement gates
dep.expHt(
    h=ham,
    t=t,
    N=15, # Suzuki decomposition
    ) 
cir.setMeasurement([0])

Tlist = np.arange(0,10,0.1)# the time range we study
results = cir.runJob(paramList=[{'F':[t]} for t in Tlist ]) # batch submit all jobs

#---- plot 
prob0 = [res.get('0',0) for res in results]
plt.plot(Tlist,prob0)
plt.xlabel("Time (t)")
plt.ylabel('Probability $ \langle 0|0\\rangle$')
plt.show()

The plot of the results is shown in the figure below:

Inspect isq code

In the example above, we have set workDir="/home/user/Desktop/test". One can observe the intermediate resources in that directory. The original isq file is named resource.isq.

Backend

Default simulator

In the above examples, the circuit uses a default simulator as a backend. To illustrate this explicitly, please refer to the following example:

from isqdeployer.backend import NumpySimulator
from isqdeployer.circuit import Circuit

backend = NumpySimulator() 
cir = Circuit(nq=1,backend=backend) 
cir.H(0) 
cir.setMeasurement([0])
cir.runJob() 

High efficient simulator

If one has a Fortran compiler (such as gfortran) installed on their PC, they can use:

from isqdeployer.backend import fSimulator
backend = fSimulator(nCore=4)

where nCore can be the number of CPU cores. This parameter (nCore) can be utilized in most of our simulators.

Real quantum hardware

Currently, we support the connection to the quantum computer offered by QuantumCteK.

from isqdeployer.backend import GuodunBackend
backend = GuodunBackend(login_key="...",)  

Other backends

Users can define their own backend (hardware or simulator) by inheriting the basic class:

from isqdeployer.backend.abcBackend import Backend

class YourBackend(Backend):
    pass 

With this capability, one can connect any backend to isqdeployer.

More information

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