KeyError: 'Gxpi2' when following 00-Protocols tutorial

[liuyichao82]

Hi PyGSTi team,
I'm currently a beginner learning PyGSTi and working through the 00-Protocols tutorial, and I encountered a KeyError related to 'Gxpi2' when executing the following line from the example:

# define the quantum processor (or piece of a processor) we'll be doing RB on
qubit_labels = ['Q1']
gate_names = ['Gxpi2', 'Gxmpi2', 'Gypi2', 'Gympi2']
pspec = pygsti.processors.QubitProcessorSpec(len(qubit_labels), gate_names,
                 qubit_labels=qubit_labels)
depths = [0, 10, 20, 30]
circuits_per_depth = 50

# Create compilation rules from native gates to Cliffords
from pygsti.processors import CliffordCompilationRules as CCR
compilations = {'absolute': CCR.create_standard(pspec, 'absolute', ('paulis', '1Qcliffords'), verbosity=0), 'paulieq': CCR.create_standard(pspec, 'paulieq', ('1Qcliffords', 'allcnots'), verbosity=0)}

This results in the following traceback:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[6], line 11
      9 # Create compilation rules from native gates to Cliffords
     10 from pygsti.processors import CliffordCompilationRules as CCR
---> 11 compilations = {'absolute': CCR.create_standard(pspec, 'absolute', ('paulis', '1Qcliffords'), verbosity=0),            
     12                 'paulieq': CCR.create_standard(pspec, 'paulieq', ('1Qcliffords', 'allcnots'), verbosity=0)}
     14 # create an experiment design
     15 exp_design = pygsti.protocols.CliffordRBDesign(pspec, compilations, depths, circuits_per_depth)

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\processors\compilationrules.py:504, in CliffordCompilationRules.create_standard(cls, base_processor_spec, compile_type, what_to_compile, verbosity)
    499             print(
    500                 "- Creating a circuit to implement {} {} on qubit {}...".format(gname, descs[compile_type],
    501                                                                                 q))
    502         # This does a brute-force search to compile the gate, by creating `templates` when necessary, and using
    503         # a template if one has already been constructed.
--> 504         compilation_rules.add_local_compilation_of(_Label(gname, q), verbosity=verbosity)
    505     if verbosity > 0: print("Complete.")
    507 # Manually add in the "obvious" compilations for CNOT gates as templates, so that we use the normal conversions
    508 # based on the Hadamard gate -- if this is possible. If we don't do this, we resort to random compilations,
    509 # which might not give the "expected" compilations (even if the alternatives might be just as good).

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\processors\compilationrules.py:789, in CliffordCompilationRules.add_local_compilation_of(self, oplabel, unitary, srep, max_iterations, force, verbosity)
    751 def add_local_compilation_of(self, oplabel, unitary=None, srep=None, max_iterations=10, force=False, verbosity=1):
    752     """
    753     Adds a new local compilation of `oplabel`.
    754 
   (...)    787     None
    788     """
--> 789     circuit = self._get_local_compilation_of(oplabel, unitary, srep, max_iterations, force, verbosity)
    790     self.add_specific_compilation_rule(oplabel, circuit, unitary)

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\processors\compilationrules.py:744, in CliffordCompilationRules._get_local_compilation_of(self, oplabel, unitary, srep, max_iterations, force, verbosity)
    741 if not force and oplabel in self.specific_compilations:
    742     return self.specific_compilations[oplabel]  # don't re-compute unless we're told to
--> 744 circuit = self._create_local_compilation_of(oplabel,
    745                                             unitary=unitary,
    746                                             srep=srep,
    747                                             max_iterations=max_iterations,
    748                                             verbosity=verbosity)
    749 return circuit

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\processors\compilationrules.py:691, in CliffordCompilationRules._create_local_compilation_of(self, oplabel, unitary, srep, max_iterations, verbosity)
    688     if is_local_compilation_feasible(available_gatenames):
    689         available_gatelabels = [to_template_label(gl) for gn in available_gatenames
    690                                 for gl in self.processor_spec.available_gatelabels(gn, oplabel.sslbls)]
--> 691         template_to_use = self.add_clifford_compilation_template(
    692             oplabel.name, oplabel.num_qubits, unitary, srep,
    693             available_gatelabels, available_srep_dict,
    694             verbosity=verbosity, max_iterations=max_iterations)
    696 #If a template has been found, use it.
    697 if template_to_use is not None:

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\processors\compilationrules.py:884, in CliffordCompilationRules.add_clifford_compilation_template(self, gate_name, nqubits, unitary, srep, available_gatelabels, available_sreps, verbosity, max_iterations)
    882 # Find the symplectic action of all possible circuits of length 1 on the qubits
    883 for layer in all_layers:
--> 884     obtained_sreps[layer] = _symp.symplectic_rep_of_clifford_layer(layer, nqubits, srep_dict=available_sreps)
    886 # find the 1Q identity gate name
    887 I_name = self._find_std_gate(self.processor_spec, 'I')

File c:\ProgramData\miniconda3\envs\qutip-mkl\Lib\site-packages\pygsti\tools\symplectic.py:1191, in symplectic_rep_of_clifford_layer(layer, n, q_labels, srep_dict, add_internal_sreps)
   1188     layer = _Label(layer)
   1190 for sub_lbl in layer.components:
-> 1191     matrix, phase = srep_dict[sub_lbl.name]
   1192     nforgate = sub_lbl.num_qubits
   1193     sub_lbl_qubits = sub_lbl.qubits if (sub_lbl.qubits is not None) else q_labels

KeyError: 'Gxpi2'

I'm using PyGSTi version: 0.9.13, and I tried using Python versions 3.12 and 3.13 but it didn't work. The following are all the packages installed in the entire conda environment:

# Name                    Version                   Build  Channel
_openmp_mutex             4.5                       2_gnu    conda-forge
asttokens                 3.0.0              pyhd8ed1ab_1    conda-forge
brotli                    1.1.0                h2466b09_2    conda-forge
brotli-bin                1.1.0                h2466b09_2    conda-forge
bzip2                     1.0.8                h2466b09_7    conda-forge
ca-certificates           2025.4.26            h4c7d964_0    conda-forge
colorama                  0.4.6              pyhd8ed1ab_1    conda-forge
comm                      0.2.2              pyhd8ed1ab_1    conda-forge
contourpy                 1.3.2           py312hd5eb7cc_0    conda-forge
cpython                   3.12.11         py312hd8ed1ab_0    conda-forge
cycler                    0.12.1             pyhd8ed1ab_1    conda-forge
cython                    3.1.1           py312h890cc4b_1    conda-forge
debugpy                   1.8.14          py312h275cf98_0    conda-forge
decorator                 5.2.1              pyhd8ed1ab_0    conda-forge
exceptiongroup            1.3.0              pyhd8ed1ab_0    conda-forge
executing                 2.2.0              pyhd8ed1ab_0    conda-forge
fonttools                 4.58.1          py312h31fea79_0    conda-forge
freetype                  2.13.3               h57928b3_1    conda-forge
importlib-metadata        8.7.0              pyhe01879c_1    conda-forge
intel-openmp              2024.2.1          h57928b3_1083    conda-forge
ipykernel                 6.29.5             pyh4bbf305_0    conda-forge
ipython                   9.3.0              pyh6be1c34_0    conda-forge
ipython_pygments_lexers   1.1.1              pyhd8ed1ab_0    conda-forge
jedi                      0.19.2             pyhd8ed1ab_1    conda-forge
jinja2                    3.1.6              pyhd8ed1ab_0    conda-forge
jupyter_client            8.6.3              pyhd8ed1ab_1    conda-forge
jupyter_core              5.8.1              pyh5737063_0    conda-forge
kiwisolver                1.4.8           py312hc790b64_0    conda-forge
krb5                      1.21.3               hdf4eb48_0    conda-forge
lcms2                     2.17                 hbcf6048_0    conda-forge
lerc                      4.0.0                h6470a55_1    conda-forge
libblas                   3.9.0           31_h641d27c_mkl    conda-forge
libbrotlicommon           1.1.0                h2466b09_2    conda-forge
libbrotlidec              1.1.0                h2466b09_2    conda-forge
libbrotlienc              1.1.0                h2466b09_2    conda-forge
libcblas                  3.9.0           31_h5e41251_mkl    conda-forge
libdeflate                1.24                 h76ddb4d_0    conda-forge
libexpat                  2.7.0                he0c23c2_0    conda-forge
libffi                    3.4.6                h537db12_1    conda-forge
libfreetype               2.13.3               h57928b3_1    conda-forge
libfreetype6              2.13.3               h0b5ce68_1    conda-forge
libgcc                    15.1.0               h1383e82_2    conda-forge
libgomp                   15.1.0               h1383e82_2    conda-forge
libhwloc                  2.11.2          default_ha69328c_1001    conda-forge
libiconv                  1.18                 h135ad9c_1    conda-forge
libjpeg-turbo             3.1.0                h2466b09_0    conda-forge
liblapack                 3.9.0           31_h1aa476e_mkl    conda-forge
liblzma                   5.8.1                h2466b09_1    conda-forge
liblzma-devel             5.8.1                h2466b09_1    conda-forge
libpng                    1.6.47               h7a4582a_0    conda-forge
libsodium                 1.0.20               hc70643c_0    conda-forge
libsqlite                 3.50.0               h67fdade_0    conda-forge
libtiff                   4.7.0                h05922d8_5    conda-forge
libwebp-base              1.5.0                h3b0e114_0    conda-forge
libwinpthread             12.0.0.r4.gg4f2fc60ca      h57928b3_9    conda-forge
libxcb                    1.17.0               h0e4246c_0    conda-forge
libxml2                   2.13.8               h442d1da_0    conda-forge
libzlib                   1.3.1                h2466b09_2    conda-forge
markupsafe                3.0.2           py312h31fea79_1    conda-forge
matplotlib-base           3.10.3          py312h90004f6_0    conda-forge
matplotlib-inline         0.1.7              pyhd8ed1ab_1    conda-forge
mkl                       2024.2.2            h66d3029_15    conda-forge
munkres                   1.1.4              pyh9f0ad1d_0    conda-forge
narwhals                  1.41.0             pyhe01879c_0    conda-forge
nest-asyncio              1.6.0              pyhd8ed1ab_1    conda-forge
networkx                  3.5                pyhe01879c_0    conda-forge
numpy                     2.2.6           py312h3150e54_0    conda-forge
openjpeg                  2.5.3                h4d64b90_0    conda-forge
openssl                   3.5.0                ha4e3fda_1    conda-forge
packaging                 25.0               pyh29332c3_1    conda-forge
pandas                    2.2.3           py312h72972c8_3    conda-forge
parso                     0.8.4              pyhd8ed1ab_1    conda-forge
pickleshare               0.7.5           pyhd8ed1ab_1004    conda-forge
pillow                    11.2.1          py312h078707f_0    conda-forge
pip                       25.1.1             pyh8b19718_0    conda-forge
platformdirs              4.3.8              pyhe01879c_0    conda-forge
plotly                    6.1.2              pyhd8ed1ab_0    conda-forge
prompt-toolkit            3.0.51             pyha770c72_0    conda-forge
psutil                    7.0.0           py312h4389bb4_0    conda-forge
pthread-stubs             0.4               h0e40799_1002    conda-forge
pure_eval                 0.2.3              pyhd8ed1ab_1    conda-forge
pygments                  2.19.1             pyhd8ed1ab_0    conda-forge
pygsti                    0.9.13          np20py312h994d5b2_4    conda-forge
pyparsing                 3.2.3              pyhd8ed1ab_1    conda-forge
python                    3.12.0          h2628c8c_0_cpython    conda-forge
python-dateutil           2.9.0.post0        pyhff2d567_1    conda-forge
python-tzdata             2025.2             pyhd8ed1ab_0    conda-forge
python_abi                3.12                    7_cp312    conda-forge
pytz                      2025.2             pyhd8ed1ab_0    conda-forge
pywin32                   307             py312h275cf98_3    conda-forge
pyzmq                     26.4.0          py312hd7027bb_0    conda-forge
qhull                     2020.2               hc790b64_5    conda-forge
scipy                     1.15.2          py312h451d5c4_0    conda-forge
setuptools                80.9.0             pyhff2d567_0    conda-forge
six                       1.17.0             pyhd8ed1ab_0    conda-forge
stack_data                0.6.3              pyhd8ed1ab_1    conda-forge
tbb                       2021.13.0            h62715c5_1    conda-forge
tk                        8.6.13               h2c6b04d_2    conda-forge
tornado                   6.5.1           py312h4389bb4_0    conda-forge
traitlets                 5.14.3             pyhd8ed1ab_1    conda-forge
typing_extensions         4.14.0             pyhe01879c_0    conda-forge
tzdata                    2025b                h78e105d_0    conda-forge
ucrt                      10.0.22621.0         h57928b3_1    conda-forge
unicodedata2              16.0.0          py312h4389bb4_0    conda-forge
vc                        14.3                h2b53caa_26    conda-forge
vc14_runtime              14.42.34438         hfd919c2_26    conda-forge
wcwidth                   0.2.13             pyhd8ed1ab_1    conda-forge
wheel                     0.45.1             pyhd8ed1ab_1    conda-forge
xorg-libxau               1.0.12               h0e40799_0    conda-forge
xorg-libxdmcp             1.1.5                h0e40799_0    conda-forge
xz                        5.8.1                h208afaa_1    conda-forge
xz-tools                  5.8.1                h2466b09_1    conda-forge
zeromq                    4.3.5                ha9f60a1_7    conda-forge
zipp                      3.22.0             pyhd8ed1ab_0    conda-forge
zstd                      1.5.7                hbeecb71_2    conda-forge

Any help or clarification would be greatly appreciated!

[coreyostrove]

Hi @liuyichao82, thank you for your report, and apologies for the delayed reply.

I'm unfortunately having trouble reproducing the error you're encountering on my system using python 3.13 with pyGSTi 0.9.13. Before we get too far in debugging could I have you try to run the tutorial notebook using the slightly newer pyGSTi 0.9.13.1? Since it looked like you were running this on a fairly fresh installation I was actually a bit confused why conda installed 0.9.13 instead, but upon closer inspection it looks like a pypi configuration issue on our end causing this that we'll need to fix. In the meantime, you can install 0.9.13.1 manually by cloning the git repository and installing pygsti from there. One cloned, you can navigate to the cloned repo and checkout this version using git checkout v0.9.13.1. Then just activate your conda environment and install pygsti using pip install -e .[complete]. With any luck that might fix your issue, but if not please report back and we can help try to find underlying cause.

[coreyostrove]

Hi @liuyichao82, thank you for your report, and apologies for the delayed reply.

I'm unfortunately having trouble reproducing the error you're encountering on my system using python 3.13 with pyGSTi 0.9.13. Before we get too far in debugging could I have you try to run the tutorial notebook using the slightly newer pyGSTi 0.9.13.1? Since it looked like you were running this on a fairly fresh installation I was actually a bit confused why conda installed 0.9.13 instead, but upon closer inspection it looks like a pypi configuration issue on our end causing this that we'll need to fix. In the meantime, you can install 0.9.13.1 manually by cloning the git repository and installing pygsti from there. One cloned, you can navigate to the cloned repo and checkout this version using git checkout v0.9.13.1. Then just activate your conda environment and install pygsti using pip install -e .[complete]. With any luck that might fix your issue, but if not please report back and we can help try to find underlying cause.

Quick update: We should have those configuration issues resolved, and what is now v0.9.13.2 on pypi should be directly pip installable so that you can avoid cloning the repo and building from source locally.

[liuyichao82]

Hello, thank you very much for your reply. First of all, regarding the installation version issue, I found that it seems to be due to conda. I still installed 0.9.13 using conda install pygsti, but installed 0.9.13.2 using pip install pygsti.
Secondly, regarding the KeyError: 'Gxpi2' issue, unfortunately, this problem still exists after I updated 0.9.13.2.This is all the packages installed in my current conda environment. This environment is specially set up to run pygsti, so it is a fairly fresh environment

anyio                     4.9.0              pyh29332c3_0    conda-forge
argon2-cffi               25.1.0             pyhd8ed1ab_0    conda-forge
argon2-cffi-bindings      21.2.0          py312h4389bb4_5    conda-forge
arrow                     1.3.0              pyhd8ed1ab_1    conda-forge
asttokens                 3.0.0              pyhd8ed1ab_1    conda-forge
async-lru                 2.0.5              pyh29332c3_0    conda-forge
attrs                     25.3.0             pyh71513ae_0    conda-forge
babel                     2.17.0             pyhd8ed1ab_0    conda-forge
beautifulsoup4            4.13.4             pyha770c72_0    conda-forge
bleach                    6.2.0              pyh29332c3_4    conda-forge
bleach-with-css           6.2.0                h82add2a_4    conda-forge
brotli-python             1.1.0           py312h275cf98_2    conda-forge
bzip2                     1.0.8                h2466b09_7    conda-forge
ca-certificates           2025.6.15            h4c7d964_0    conda-forge
cached-property           1.5.2                hd8ed1ab_1    conda-forge
cached_property           1.5.2              pyha770c72_1    conda-forge
certifi                   2025.6.15          pyhd8ed1ab_0    conda-forge
cffi                      1.17.1          py312h4389bb4_0    conda-forge
charset-normalizer        3.4.2              pyhd8ed1ab_0    conda-forge
clarabel                  0.11.1          py312h11ebf2c_0    conda-forge
colorama                  0.4.6              pyhd8ed1ab_1    conda-forge
comm                      0.2.2              pyhd8ed1ab_1    conda-forge
cpython                   3.12.11         py312hd8ed1ab_0    conda-forge
cvxopt                    1.3.2           py312h625275e_4    conda-forge
cvxpy                     1.6.6           py312h2e8e312_0    conda-forge
cvxpy-base                1.6.6           py312h72972c8_0    conda-forge
cython                    3.1.1           py312h890cc4b_1    conda-forge
debugpy                   1.8.14          py312h275cf98_0    conda-forge
decorator                 5.2.1              pyhd8ed1ab_0    conda-forge
defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
dsdp                      5.8               h6e01ec9_1203    conda-forge
exceptiongroup            1.3.0              pyhd8ed1ab_0    conda-forge
executing                 2.2.0              pyhd8ed1ab_0    conda-forge
fftw                      3.3.10          nompi_h89e6982_110    conda-forge
fqdn                      1.5.1              pyhd8ed1ab_1    conda-forge
glpk                      5.0                  h8ffe710_0    conda-forge
gsl                       2.7                  hdfb1a43_0    conda-forge
h11                       0.16.0             pyhd8ed1ab_0    conda-forge
h2                        4.2.0              pyhd8ed1ab_0    conda-forge
hpack                     4.1.0              pyhd8ed1ab_0    conda-forge
httpcore                  1.0.9              pyh29332c3_0    conda-forge
httpx                     0.28.1             pyhd8ed1ab_0    conda-forge
hyperframe                6.1.0              pyhd8ed1ab_0    conda-forge
idna                      3.10               pyhd8ed1ab_1    conda-forge
importlib-metadata        8.7.0              pyhe01879c_1    conda-forge
importlib_resources       6.5.2              pyhd8ed1ab_0    conda-forge
intel-openmp              2024.2.1          h57928b3_1083    conda-forge
ipykernel                 6.29.5             pyh4bbf305_0    conda-forge
ipython                   9.3.0              pyh6be1c34_0    conda-forge
ipython_pygments_lexers   1.1.1              pyhd8ed1ab_0    conda-forge
isoduration               20.11.0            pyhd8ed1ab_1    conda-forge
jedi                      0.19.2             pyhd8ed1ab_1    conda-forge
jinja2                    3.1.6              pyhd8ed1ab_0    conda-forge
json5                     0.12.0             pyhd8ed1ab_0    conda-forge
jsonpointer               3.0.0           py312h2e8e312_1    conda-forge
jsonschema                4.24.0             pyhd8ed1ab_0    conda-forge
jsonschema-specifications 2025.4.1           pyh29332c3_0    conda-forge
jsonschema-with-format-nongpl 4.24.0               hd8ed1ab_0    conda-forge
jupyter-lsp               2.2.5              pyhe01879c_2    conda-forge
jupyter_client            8.6.3              pyhd8ed1ab_1    conda-forge
jupyter_core              5.8.1              pyh5737063_0    conda-forge
jupyter_events            0.12.0             pyh29332c3_0    conda-forge
jupyter_server            2.16.0             pyhe01879c_0    conda-forge
jupyter_server_terminals  0.5.3              pyhd8ed1ab_1    conda-forge
jupyterlab                4.4.3              pyhd8ed1ab_0    conda-forge
jupyterlab_pygments       0.3.0              pyhd8ed1ab_2    conda-forge
jupyterlab_server         2.27.3             pyhd8ed1ab_1    conda-forge
krb5                      1.21.3               hdf4eb48_0    conda-forge
libblas                   3.9.0           31_h641d27c_mkl    conda-forge
libcblas                  3.9.0           31_h5e41251_mkl    conda-forge
libexpat                  2.7.0                he0c23c2_0    conda-forge
libffi                    3.4.6                h537db12_1    conda-forge
libhwloc                  2.11.2          default_ha69328c_1001    conda-forge
libiconv                  1.18                 h135ad9c_1    conda-forge
liblapack                 3.9.0           31_h1aa476e_mkl    conda-forge
liblzma                   5.8.1                h2466b09_1    conda-forge
liblzma-devel             5.8.1                h2466b09_1    conda-forge
libosqp                   0.6.3                he0c23c2_1    conda-forge
libqdldl                  0.1.7                h63175ca_0    conda-forge
libsodium                 1.0.20               hc70643c_0    conda-forge
libsqlite                 3.50.0               h67fdade_0    conda-forge
libwinpthread             12.0.0.r4.gg4f2fc60ca      h57928b3_9    conda-forge
libxml2                   2.13.8               h442d1da_0    conda-forge
libzlib                   1.3.1                h2466b09_2    conda-forge
markupsafe                3.0.2           py312h31fea79_1    conda-forge
matplotlib-inline         0.1.7              pyhd8ed1ab_1    conda-forge
mistune                   3.1.3              pyh29332c3_0    conda-forge
mkl                       2024.2.2            h66d3029_15    conda-forge
narwhals                  1.43.1                   pypi_0    pypi
nbclient                  0.10.2             pyhd8ed1ab_0    conda-forge
nbconvert-core            7.16.6             pyh29332c3_0    conda-forge
nbformat                  5.10.4             pyhd8ed1ab_1    conda-forge
nest-asyncio              1.6.0              pyhd8ed1ab_1    conda-forge
networkx                  3.5                      pypi_0    pypi
notebook                  7.4.3              pyhd8ed1ab_0    conda-forge
notebook-shim             0.2.4              pyhd8ed1ab_1    conda-forge
numpy                     2.2.6           py312h3150e54_0    conda-forge
openssl                   3.5.0                ha4e3fda_1    conda-forge
osqp                      0.6.7.post3     py312h72972c8_1    conda-forge
overrides                 7.7.0              pyhd8ed1ab_1    conda-forge
packaging                 25.0               pyh29332c3_1    conda-forge
pandas                    2.3.0                    pypi_0    pypi
pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge
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pip                       25.1.1             pyh8b19718_0    conda-forge
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plotly                    6.1.2                    pypi_0    pypi
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psutil                    7.0.0           py312h4389bb4_0    conda-forge
pure_eval                 0.2.3              pyhd8ed1ab_1    conda-forge
pycparser                 2.22               pyh29332c3_1    conda-forge
pygments                  2.19.1             pyhd8ed1ab_0    conda-forge
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pysocks                   1.7.1              pyh09c184e_7    conda-forge
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tk                        8.6.13               h2c6b04d_2    conda-forge
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[coreyostrove]

Thank you for following-up, @liuyichao82. Could you confirm that you're running the tutorial notebook exactly as it appears here: https://github.com/sandialabs/pyGSTi/blob/master/jupyter_notebooks/Tutorials/00-Protocols.ipynb? If possible, could you upload a copy of .ipynb file for the tutorial notebook that you've run where you've encountered the error? That is, with the traceback present in the output of the corresponding cell? It may be easier to tell what is going on in situ.

In the meantime I'll try reproducing your conda environment more exactly and see if I can spot some incompatibility.

[liuyichao82]

Sorry for the late reply, I may know what the problem is. In my environment, there is a very strange bug from scipy.linalg.expm. Please see the following code:

import numpy as np
import scipy
import scipy.linalg as spl

print(f"SciPy version: {scipy.__version__}")
print(f"NumPy version: {np.__version__}")

# Define Pauli-X matrix

sigmax = np.array([[0, 1], [1, 0]], dtype=complex)
print(f"sigmax:\n{sigmax}")

# Calculate π/2 * sigmax
exp_arg = np.pi / 2 * sigmax
print(f"\nπ/2 * sigmax:\n{exp_arg}")

# Calculate matrix exponential expm(-1j * exp_arg / 2)
matrix_exp_arg = -1j * exp_arg / 2
print(f"\n-1j * exp_arg / 2:\n{matrix_exp_arg}")

# Key calculation: expm function
result = spl.expm(matrix_exp_arg)
print(f"\nexpm(matrix_exp_arg):\n{result}")
print(f"result dtype: {result.dtype}")

# Analyze results
print(f"\nResult analysis:")
print(f"[0,0] element: {result[0,0]}")
print(f"[0,1] element: {result[0,1]}")
print(f"[1,0] element: {result[1,0]}")
print(f"[1,1] element: {result[1,1]}")

# Calculate rotation angle
theta = 2 * np.arccos(np.real(result[0,0]))
print(f"Actual rotation angle: {np.degrees(theta):.6f} degrees")

# Compare with theoretical value
expected = np.array([[1, -1j], [-1j, 1]]) / np.sqrt(2)
print(f"\nTheoretical value:\n{expected}")
print(f"Difference: {np.linalg.norm(result - expected):.2e}")

In the error environment, the result of running is

SciPy version: 1.15.2
NumPy version: 2.2.6
sigmax:
[[0.+0.j 1.+0.j]
 [1.+0.j 0.+0.j]]

π/2 * sigmax:
[[0.        +0.j 1.57079633+0.j]
 [1.57079633+0.j 0.        +0.j]]

-1j * exp_arg / 2:
[[0.-0.j         0.-0.78539816j]
 [0.-0.78539816j 0.-0.j        ]]

expm(matrix_exp_arg):
[[0.99386593+0.j         0.        +0.11059168j]
 [0.        +0.11059168j 0.99386593+0.j        ]]
result dtype: complex128

Result analysis:
[0,0] element: (0.9938659271237102+0j)
[0,1] element: 0.11059167646133211j
[1,0] element: 0.11059167646133211j
[1,1] element: (0.9938659271237102+0j)
Actual rotation angle: 12.698848 degrees

Theoretical value:
[[ 0.70710678+0.j         -0.        -0.70710678j]
 [-0.        -0.70710678j  0.70710678+0.j        ]]
Difference: 1.23e+00

This result should be reproducible, but it must be installed with conda version 1.15.2 of scipy. Installing with pip works fine. I saw a similar error on this website.
Obviously, this will cause an error in the pygsti.tools.internalgates.standard_gatename_unitaries function _np.array(_spl.expm(-1j * exp / 2), complex) (line 221 in pygsti\tools\internalgates.py), which will cause the compute_clifford_symplectic_reps() in the QuditProcessorSpec class to fail to identify the Clifford gate (line 1190 in pygsti\processors\processorspec.py)

try:
    self._symplectic_reps[gn] = _symplectic.unitary_to_symplectic(unitary)
except ValueError:
    self._symplectic_reps[gn] = None # `gn` is not a Clifford

which will cause the tutorial code CCR.create_standard(pspec, 'absolute', ('paulis', '1Qcliffords')) actually returns an empty dictionary, so it will report an error!

[coreyostrove]

Thanks for the detective work! This looks like a very tricky bug to have identified.

Since this looks to be a bug somewhere at the interface between scipy and conda, I don't think there is anything else for us to do on our end other than keep this in mind so we can advise users to be aware of this in case they run into the same issue in the future. For what it is worth, based on the github issue you shared from scipy, that issue was marked closed and tagged as having been included in version 1.15.3 (and version 1.16.0 just came out a few days ago too), so this problem may well go away as soon as conda makes that the default version for new builds.

I'll keep this issue open for the time being as a reminder to add a note about this in the release notes for the upcoming 0.9.14 release. Thanks again for your report and for your investigation into this matter.