| Name | stim JSON |
| Version |
1.14.0
JSON |
| download |
| home_page | https://github.com/quantumlib/stim |
| Summary | A fast library for analyzing with quantum stabilizer circuits. |
| upload_time | 2024-09-24 08:40:22 |
| maintainer | None |
| docs_url | None |
| author | Craig Gidney |
| requires_python | >=3.6.0 |
| license | Apache 2 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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:
```bash
pip install stim
```
Once stim is installed, you can `import stim` and use it.
There are three supported use cases:
1. Interactive simulation with `stim.TableauSimulator`.
2. High speed sampling with samplers compiled from `stim.Circuit`.
3. Independent exploration using `stim.Tableau` and `stim.PauliString`.
## Interactive Simulation
Use `stim.TableauSimulator` to simulate operations one by one while inspecting the results:
```python
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:
```python
import stim
# Create a circuit that measures a large GHZ state.
c = stim.Circuit()
c.append("H", [0])
for k in range(1, 30):
c.append("CNOT", [0, k])
c.append("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:
```python
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](https://github.com/quantumlib/Stim#readme).
## Independent Exploration
Stim provides data types `stim.PauliString` and `stim.Tableau`, which support a variety of fast operations.
```python
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)
```
Raw data
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"description": "# Stim\n\nStim is a fast simulator for quantum stabilizer circuits.\n\nAPI references are available on the stim github wiki: https://github.com/quantumlib/stim/wiki\n\nStim can be installed into a python 3 environment using pip:\n\n```bash\npip install stim\n```\n\nOnce stim is installed, you can `import stim` and use it.\nThere are three supported use cases:\n\n1. Interactive simulation with `stim.TableauSimulator`.\n2. High speed sampling with samplers compiled from `stim.Circuit`.\n3. Independent exploration using `stim.Tableau` and `stim.PauliString`.\n\n## Interactive Simulation\n\nUse `stim.TableauSimulator` to simulate operations one by one while inspecting the results:\n\n```python\nimport stim\n\ns = stim.TableauSimulator()\n\n# Create a GHZ state.\ns.h(0)\ns.cnot(0, 1)\ns.cnot(0, 2)\n\n# Look at the simulator state re-inverted to be forwards:\nt = s.current_inverse_tableau()\nprint(t**-1)\n# prints:\n# +-xz-xz-xz-\n# | ++ ++ ++\n# | ZX _Z _Z\n# | _X XZ __\n# | _X __ XZ\n\n# Measure the GHZ state.\nprint(s.measure_many(0, 1, 2))\n# prints one of:\n# [True, True, True]\n# or:\n# [False, False, False]\n```\n\n## High Speed Sampling\n\nBy creating a `stim.Circuit` and compiling it into a sampler, samples can be generated very quickly:\n\n```python\nimport stim\n\n# Create a circuit that measures a large GHZ state.\nc = stim.Circuit()\nc.append(\"H\", [0])\nfor k in range(1, 30):\n c.append(\"CNOT\", [0, k])\nc.append(\"M\", range(30))\n\n# Compile the circuit into a high performance sampler.\nsampler = c.compile_sampler()\n\n# Collect a batch of samples.\n# Note: the ideal batch size, in terms of speed per sample, is roughly 1024.\n# Smaller batches are slower because they are not sufficiently vectorized.\n# Bigger batches are slower because they use more memory.\nbatch = sampler.sample(1024)\nprint(type(batch)) # numpy.ndarray\nprint(batch.dtype) # numpy.uint8\nprint(batch.shape) # (1024, 30)\nprint(batch)\n# Prints something like:\n# [[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]\n# [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]\n# [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]\n# ...\n# [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]\n# [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]\n# [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]\n# [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]]\n```\n\nThis also works on circuits that include noise:\n\n```python\nimport stim\nimport numpy as np\n\nc = stim.Circuit(\"\"\"\n X_ERROR(0.1) 0\n Y_ERROR(0.2) 1\n Z_ERROR(0.3) 2\n DEPOLARIZE1(0.4) 3\n DEPOLARIZE2(0.5) 4 5\n M 0 1 2 3 4 5\n\"\"\")\nbatch = c.compile_sampler().sample(2**20)\nprint(np.mean(batch, axis=0).round(3))\n# Prints something like:\n# [0.1 0.2 0. 0.267 0.267 0.266]\n```\n\nYou can also sample annotated detection events using `stim.Circuit.compile_detector_sampler`.\n\nFor a list of gates that can appear in a `stim.Circuit`, see the [latest readme on github](https://github.com/quantumlib/Stim#readme).\n\n## Independent Exploration\n\nStim provides data types `stim.PauliString` and `stim.Tableau`, which support a variety of fast operations.\n\n```python\nimport stim\n\nxx = stim.PauliString(\"XX\")\nyy = stim.PauliString(\"YY\")\nassert xx * yy == -stim.PauliString(\"ZZ\")\n\ns = stim.Tableau.from_named_gate(\"S\")\nprint(repr(s))\n# prints:\n# stim.Tableau.from_conjugated_generators(\n# xs=[\n# stim.PauliString(\"+Y\"),\n# ],\n# zs=[\n# stim.PauliString(\"+Z\"),\n# ],\n# )\n\ns_dag = stim.Tableau.from_named_gate(\"S_DAG\")\nassert s**-1 == s_dag\nassert s**1000000003 == s_dag\n\ncnot = stim.Tableau.from_named_gate(\"CNOT\")\ncz = stim.Tableau.from_named_gate(\"CZ\")\nh = stim.Tableau.from_named_gate(\"H\")\nt = stim.Tableau(5)\nt.append(cnot, [1, 4])\nt.append(h, [4])\nt.append(cz, [1, 4])\nt.prepend(h, [4])\nassert t == stim.Tableau(5)\n```\n",
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