Jaspify#

jaspify(func=None, terminal_sampling=False)[source]#

This simulator is the established Qrisp simulator linked to the Jasp infrastructure. Among a variety of simulation tricks, the simulator can leverage state sparsity, allowing simulations with up to hundreds of qubits!

To be called as a decorator of a Jasp-traceable function.

Note

If you are developing a hybrid algorithm like QAOA or VQE that relies heavily on sampling, please activate the terminal_sampling feature.

Parameters:

funccallable: The function to simulate.
terminal_samplingbool, optional: Whether to leverage the terminal sampling strategy. Significantly fast for all sampling tasks but can yield incorrect results in some situations. Check out Terminal Sampling form more details. The default is False.

Returns:

callable: A function performing the simulation.

Examples

We simulate a function creating a simple GHZ state:

from qrisp import *
from qrisp.jasp import *

@jaspify
def main():

    qf = QuantumFloat(5)

    h(qf[0])

    for i in range(1, 5):
        cx(qf[0], qf[i])

    return measure(qf)

print(main())            
# Yields either 0 or 31

To highlight the speed of the terminal sampling feature, we Sampling from a uniform superposition

def state_prep():
    qf = QuantumFloat(5)
    h(qf)
    return qf

@jaspify
def without_terminal_sampling():
    sampling_func = sample(state_prep, shots = 10000)
    return sampling_func()

@jaspify(terminal_sampling = True)
def with_terminal_sampling():
    sampling_func = sample(state_prep, shots = 10000)
    return sampling_func()

Benchmark the time difference:

import time

t0 = time.time()
res = without_terminal_sampling()
print(time.time() - t0)
# Yields
# 43.78982925

t0 = time.time()
res = with_terminal_sampling()
print(time.time() - t0)
# Yields
# 0.550775527