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Follow new formulation in terms of cumulant expansion #27
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…-aachen.de/tobias.hangleiter/python-filter_functions into feature/calculation_with_unitaries
New flow: error_transfer_matrix -> calculate_cumulant_function -> calculate_decay_amplitudes
Also refactor internal function _get_integrand
Pull Request Test Coverage Report for Build 182
💛 - Coveralls |
Also fix rand_unit for 1d
This removes unnecessary double calculations
diff --git a/README.md b/README.md index bba48f6..24c15d6 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ Y2.cache_filter_function(omega) X.cache_filter_function(omega) hadamard = Y2 @ X # equivalent: ff.concatenate((Y2, X)) -hadamard.is_cached('F') +hadamard.is_cached('filter function') # True (filter function cached during concatenation) ```
* Factor out some core computations into dedicated functions. This makes the code more readable. * Use util.cexp - 1 instead of np.expm1 to compute the integral. This should be significantly faster with negligible precision loss. * Use opt_einsum.contract_expression instead of np.einsum for the core computation. This should be faster by a factor of 1..1.5
This is useful for derivatives and second order calculations.
…om/qutech/filter_functions into feature/calculation_with_unitaries
Adds minor performance improvements for an edge case while adding lots of code complexity
Calculation with unitaries
Feature/cache intermediates
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Implement the new formulation in terms of a cumulant expansion. This mostly involves: