A fast library for analyzing with quantum stabilizer circuits.
Project description
Stim
Stim is a fast simulator for quantum stabilizer circuits.
API references are available on the stim github wiki: https://github.com/quantumlib/stim/wiki
Stim can be installed into a python 3 environment using pip:
pip install stim
Once stim is installed, you can import stim and use it.
There are three supported use cases:
- Interactive simulation with
stim.TableauSimulator. - High speed sampling with samplers compiled from
stim.Circuit. - Independent exploration using
stim.Tableauandstim.PauliString.
Interactive Simulation
Use stim.TableauSimulator to simulate operations one by one while inspecting the results:
import stim
s = stim.TableauSimulator()
# Create a GHZ state.
s.h(0)
s.cnot(0, 1)
s.cnot(0, 2)
# Look at the simulator state re-inverted to be forwards:
t = s.current_inverse_tableau()
print(t**-1)
# prints:
# +-xz-xz-xz-
# | ++ ++ ++
# | ZX _Z _Z
# | _X XZ __
# | _X __ XZ
# Measure the GHZ state.
print(s.measure_many(0, 1, 2))
# prints one of:
# [True, True, True]
# or:
# [False, False, False]
High Speed Sampling
By creating a stim.Circuit and compiling it into a sampler, samples can be generated very quickly:
import stim
# Create a circuit that measures a large GHZ state.
c = stim.Circuit()
c.append_operation("H", [0])
for k in range(1, 30):
c.append_operation("CNOT", [0, k])
c.append_operation("M", range(30))
# Compile the circuit into a high performance sampler.
sampler = c.compile_sampler()
# Collect a batch of samples.
# Note: the ideal batch size, in terms of speed per sample, is roughly 1024.
# Smaller batches are slower because they are not sufficiently vectorized.
# Bigger batches are slower because they use more memory.
batch = sampler.sample(1024)
print(type(batch)) # numpy.ndarray
print(batch.dtype) # numpy.uint8
print(batch.shape) # (1024, 30)
print(batch)
# Prints something like:
# [[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# ...
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]]
This also works on circuits that include noise:
import stim
import numpy as np
c = stim.Circuit("""
X_ERROR(0.1) 0
Y_ERROR(0.2) 1
Z_ERROR(0.3) 2
DEPOLARIZE1(0.4) 3
DEPOLARIZE2(0.5) 4 5
M 0 1 2 3 4 5
""")
batch = c.compile_sampler().sample(2**20)
print(np.mean(batch, axis=0).round(3))
# Prints something like:
# [0.1 0.2 0. 0.267 0.267 0.266]
You can also sample annotated detection events using stim.Circuit.compile_detector_sampler.
For a list of gates that can appear in a stim.Circuit, see the latest readme on github.
Independent Exploration
Stim provides data types stim.PauliString and stim.Tableau, which support a variety of fast operations.
import stim
xx = stim.PauliString("XX")
yy = stim.PauliString("YY")
assert xx * yy == -stim.PauliString("ZZ")
s = stim.Tableau.from_named_gate("S")
print(repr(s))
# prints:
# stim.Tableau.from_conjugated_generators(
# xs=[
# stim.PauliString("+Y"),
# ],
# zs=[
# stim.PauliString("+Z"),
# ],
# )
s_dag = stim.Tableau.from_named_gate("S_DAG")
assert s**-1 == s_dag
assert s**1000000003 == s_dag
cnot = stim.Tableau.from_named_gate("CNOT")
cz = stim.Tableau.from_named_gate("CZ")
h = stim.Tableau.from_named_gate("H")
t = stim.Tableau(5)
t.append(cnot, [1, 4])
t.append(h, [4])
t.append(cz, [1, 4])
t.prepend(h, [4])
assert t == stim.Tableau(5)
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