quantumlib/OpenFermion

Is your feature request related to a use case or problem? A kind third party has been maintain recipes for Conda distributions: https://anaconda.org/channels/conda-forge/packages/openfermion/overview https://anaconda.org/channels/conda-forge/packages/openfermionpsi4/overview https://anaconda.org/channels/conda-forge/packages/openfermionpyscf/overview The versions there are slightly behind the latest releases. I don't know the process that's used to determine when those distributions are updated, but perhaps there is some mechanism to trigger updates on their side. What solution or approach do you envision? Investigate what is needed to notify the Conda distribution maintainers about new releases of OpenFermion. How urgent is this for you? P3 – not blocked; it's an idea

Is your feature request related to a use case or problem? Gemini Code Assist reported the following for linear_qubit_operator.py. This looks simple enough to do, but it should be checked for correctness, and some kind of unit tests should be added to go along with this. Performance/Memory Overhead in Single-Process Path (Lines 189-190): Using functools.reduce(numpy.add, ...) creates $N-1$ intermediate numpy arrays, where $N$ is the number of operators. For large state vectors, this causes significant memory churn and garbage collection overhead. Fix: Initialize a single zero array and accumulate the results in-place using += as suggested below. if self.options.processes <= 1: res = numpy.zeros(x.shape, dtype=complex) for operator in self.linear_operators: res += operator * x return res Originally posted by @gemini-code-assist[bot] in #1404 (comment) What solution or approach do you envision? GCA's suggestion should be implemen