qudit

High performance simulations for qudit systems. To make qudit machine learning, qudit error correction, and qudit circuit simulation easier. Qudit is made fully around numpy and pytorch to make it easy to mix and match tools without worrying about type errors.

pip install qudit

Quickstart

In most cases it should not matter if you mix and match numpy with qudit since most abstractions are built on top of numpy arrays. The following is two examples to do the same thing, one using the Circuit class and the other manually using the matrices.

Using the Circuit class:

from qudit import Circuit
import numpy as np

C = Circuit(2, dim=2)  # 2 qBits with d=2
G = C.gates[2]

C.gate(G.H, dits=[0])
C.gate(G.CX, dits=[0, 1])

ket0 = np.zeros(2**2)
ket0[0] = 1.0  # |00>

print(C(ket0))  # [1. 0. 0. 1.]/rt2