From 221b0ce6c0050f84a171561416f65944b1a170f9 Mon Sep 17 00:00:00 2001 From: "Davide Gessa (dakk)" Date: Fri, 24 Nov 2023 12:32:24 +0100 Subject: [PATCH] update docs --- .github/workflows/docs.yaml | 2 +- CONTRIBUTING.md | 2 +- TODO.md | 10 +- docs/source/conf.py | 2 +- docs/source/example_grover_1.ipynb | 165 ++++++++++++++++++ .../generated/qlasskit.algorithms.groover.rst | 29 --- .../generated/qlasskit.algorithms.grover.rst | 29 --- .../qlasskit.algorithms.qalgorithm.rst | 2 +- docs/source/generated/qlasskit.algorithms.rst | 33 ---- docs/source/generated/qlasskit.ast2ast.rst | 6 - .../generated/qlasskit.ast2logic.env.rst | 29 --- .../qlasskit.ast2logic.exceptions.rst | 37 ---- docs/source/generated/qlasskit.ast2logic.rst | 38 ---- .../qlasskit.ast2logic.t_arguments.rst | 30 ---- .../generated/qlasskit.ast2logic.t_ast.rst | 29 --- .../qlasskit.ast2logic.t_expression.rst | 30 ---- .../qlasskit.ast2logic.t_statement.rst | 29 --- .../generated/qlasskit.ast2logic.typing.rst | 29 --- .../generated/qlasskit.ast2logic.utils.rst | 29 --- .../qlasskit.boolopt.bool_optimizer.rst | 38 ---- .../qlasskit.boolopt.exp_transformers.rst | 32 ---- docs/source/generated/qlasskit.boolopt.rst | 33 ---- .../qlasskit.boolopt.sympytransformer.rst | 29 --- .../generated/qlasskit.compiler.compiler.rst | 35 ---- .../generated/qlasskit.compiler.expqmap.rst | 29 --- .../qlasskit.compiler.internalcompiler.rst | 29 --- .../qlasskit.compiler.poccompiler3.rst | 36 ---- docs/source/generated/qlasskit.compiler.rst | 41 ----- .../qlasskit.compiler.tweedledumcompiler.rst | 36 ---- .../generated/qlasskit.qcircuit.cnotsim.rst | 35 ---- .../generated/qlasskit.qcircuit.exporter.rst | 29 --- .../qlasskit.qcircuit.exporter_cirq.rst | 29 --- .../qlasskit.qcircuit.exporter_qasm.rst | 29 --- .../qlasskit.qcircuit.exporter_qiskit.rst | 29 --- .../qlasskit.qcircuit.exporter_sympy.rst | 29 --- .../generated/qlasskit.qcircuit.gates.rst | 2 +- .../generated/qlasskit.qcircuit.qcircuit.rst | 29 --- .../qlasskit.qcircuit.qcircuitenhanced.rst | 29 --- .../qlasskit.qcircuit.qcircuitwrapper.rst | 35 ---- docs/source/generated/qlasskit.qcircuit.rst | 40 ----- docs/source/generated/qlasskit.qlassf.rst | 6 - docs/source/generated/qlasskit.qlassfun.rst | 35 ---- .../source/generated/qlasskit.types.qbool.rst | 29 --- docs/source/generated/qlasskit.types.qint.rst | 35 ---- .../source/generated/qlasskit.types.qlist.rst | 30 ---- .../source/generated/qlasskit.types.qtype.rst | 29 --- docs/source/generated/qlasskit.types.rst | 43 ----- docs/source/index.rst | 2 + docs/source/quickstart.ipynb | 109 ++++-------- qlasskit/qlassfun.py | 4 +- test/test_algo_grover.py | 2 +- test/test_qlassf.py | 16 +- 52 files changed, 225 insertions(+), 1328 deletions(-) create mode 100644 docs/source/example_grover_1.ipynb delete mode 100644 docs/source/generated/qlasskit.algorithms.groover.rst delete mode 100644 docs/source/generated/qlasskit.algorithms.grover.rst delete mode 100644 docs/source/generated/qlasskit.algorithms.rst delete mode 100644 docs/source/generated/qlasskit.ast2ast.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.env.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.exceptions.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.t_arguments.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.t_ast.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.t_expression.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.t_statement.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.typing.rst delete mode 100644 docs/source/generated/qlasskit.ast2logic.utils.rst delete mode 100644 docs/source/generated/qlasskit.boolopt.bool_optimizer.rst delete mode 100644 docs/source/generated/qlasskit.boolopt.exp_transformers.rst delete mode 100644 docs/source/generated/qlasskit.boolopt.rst delete mode 100644 docs/source/generated/qlasskit.boolopt.sympytransformer.rst delete mode 100644 docs/source/generated/qlasskit.compiler.compiler.rst delete mode 100644 docs/source/generated/qlasskit.compiler.expqmap.rst delete mode 100644 docs/source/generated/qlasskit.compiler.internalcompiler.rst delete mode 100644 docs/source/generated/qlasskit.compiler.poccompiler3.rst delete mode 100644 docs/source/generated/qlasskit.compiler.rst delete mode 100644 docs/source/generated/qlasskit.compiler.tweedledumcompiler.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.cnotsim.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.exporter.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.exporter_cirq.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.exporter_qasm.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.exporter_qiskit.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.exporter_sympy.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.qcircuit.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.qcircuitenhanced.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.qcircuitwrapper.rst delete mode 100644 docs/source/generated/qlasskit.qcircuit.rst delete mode 100644 docs/source/generated/qlasskit.qlassf.rst delete mode 100644 docs/source/generated/qlasskit.qlassfun.rst delete mode 100644 docs/source/generated/qlasskit.types.qbool.rst delete mode 100644 docs/source/generated/qlasskit.types.qint.rst delete mode 100644 docs/source/generated/qlasskit.types.qlist.rst delete mode 100644 docs/source/generated/qlasskit.types.qtype.rst delete mode 100644 docs/source/generated/qlasskit.types.rst diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index 54db9479..93985bbd 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -13,7 +13,7 @@ jobs: - uses: actions/setup-python@v3 - name: Install dependencies run: | - pip install sphinx sphinx_rtd_theme myst_parser + pip install sphinx sphinx_rtd_theme nbsphinx pip install sympy - name: Sphinx build run: | diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c2024754..dc3560d5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -18,7 +18,7 @@ tox ## Make docs ``` -pip install sphinx sphinx_rtd_theme myst_parser +pip install sphinx sphinx_rtd_theme nbsphinx cd docs make html ``` \ No newline at end of file diff --git a/TODO.md b/TODO.md index 171ca065..2146ffba 100644 --- a/TODO.md +++ b/TODO.md @@ -96,12 +96,12 @@ ## Month 3: -- [x] Improve exporting utilities -- [ ] Use cases -- [ ] Documentation +### Week 1: (20 Nov 23) +- [x] Improve exporting utilities +- [x] Publish doc on github +- [x] Documentation -### Week 1: (20 Nov 23) ### Week 2: (27 Nov 23) ### Week 3: (4 Dec 23) ### Week 4: (11 Dec 23) @@ -121,7 +121,7 @@ ## Future features -- [ ] Publish doc on github +- [ ] Use cases - [ ] Int arithmetic expressions (/, %) - [ ] Parametrized qlassf - [ ] Lambda diff --git a/docs/source/conf.py b/docs/source/conf.py index b74e6cdb..d138637c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ "sphinx.ext.coverage", "sphinx.ext.napoleon", "sphinx_rtd_theme", - "myst_nb" + "nbsphinx", ] templates_path = ["_templates"] diff --git a/docs/source/example_grover_1.ipynb b/docs/source/example_grover_1.ipynb new file mode 100644 index 00000000..e4bb4b2b --- /dev/null +++ b/docs/source/example_grover_1.ipynb @@ -0,0 +1,165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grover search\n", + "\n", + "We define a function named `and_all` that returns `True` iff all the element of an input list `a_list` are `True`. We want to use a Grover search to find the input value that led to a `True` result of the function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit import qlassf, Qlist, Qint2\n", + "\n", + "@qlassf\n", + "def and_all(a_list: Qlist[bool, 4]) -> bool:\n", + " r = True\n", + " for i in a_list:\n", + " r = r and i\n", + " return r " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The qlasskit compiler will produce an optimized quantum circuit performing the given function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "and_all.export('qiskit').draw('mpl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now can use our quantum function as an oracle for a Grover search. For instance, we want to find the input value that yeld to a `True` value of the function:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from qlasskit.algorithms import Grover\n", + "\n", + "q_algo = Grover(and_all, True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qlasskit prepares the quantum circuit for the Grover search:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qc = q_algo.export('qiskit')\n", + "qc.draw('mpl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "\n", + "The `Grover` class, along with all circuit wrappers in qlasskit, provides utilities to encode inputs and decode outputs from a quantum circuit using the high level type definition. In the output histogram, it's now evident that the input leading to a `True` result in the `and_all` function is a list where all elements are set to `True`, aligning with our expectations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit import Aer, QuantumCircuit, transpile\n", + "from qiskit.visualization import plot_histogram\n", + "\n", + "qc.measure_all()\n", + "simulator = Aer.get_backend(\"aer_simulator\")\n", + "circ = transpile(qc, simulator)\n", + "result = simulator.run(circ).result()\n", + "counts = result.get_counts(circ)\n", + "\n", + "counts_readable = q_algo.decode_counts(counts)\n", + "plot_histogram(counts_readable)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qlasskit_310-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/generated/qlasskit.algorithms.groover.rst b/docs/source/generated/qlasskit.algorithms.groover.rst deleted file mode 100644 index 57c9e937..00000000 --- a/docs/source/generated/qlasskit.algorithms.groover.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.algorithms.groover -=========================== - -.. automodule:: qlasskit.algorithms.groover - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Groover - - - - - - - - - diff --git a/docs/source/generated/qlasskit.algorithms.grover.rst b/docs/source/generated/qlasskit.algorithms.grover.rst deleted file mode 100644 index 9cf829ec..00000000 --- a/docs/source/generated/qlasskit.algorithms.grover.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.algorithms.grover -========================== - -.. automodule:: qlasskit.algorithms.grover - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Grover - - - - - - - - - diff --git a/docs/source/generated/qlasskit.algorithms.qalgorithm.rst b/docs/source/generated/qlasskit.algorithms.qalgorithm.rst index b26fb1bc..d819a261 100644 --- a/docs/source/generated/qlasskit.algorithms.qalgorithm.rst +++ b/docs/source/generated/qlasskit.algorithms.qalgorithm.rst @@ -1,4 +1,4 @@ -qlasskit.algorithms.qalgorithm +qlasskit.algorithms.qalgorithm ============================== .. automodule:: qlasskit.algorithms.qalgorithm diff --git a/docs/source/generated/qlasskit.algorithms.rst b/docs/source/generated/qlasskit.algorithms.rst deleted file mode 100644 index 353b3a62..00000000 --- a/docs/source/generated/qlasskit.algorithms.rst +++ /dev/null @@ -1,33 +0,0 @@ -qlasskit.algorithms -=================== - -.. automodule:: qlasskit.algorithms - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.algorithms.groover - qlasskit.algorithms.grover - qlasskit.algorithms.qalgorithm - diff --git a/docs/source/generated/qlasskit.ast2ast.rst b/docs/source/generated/qlasskit.ast2ast.rst deleted file mode 100644 index 39058ca6..00000000 --- a/docs/source/generated/qlasskit.ast2ast.rst +++ /dev/null @@ -1,6 +0,0 @@ -qlasskit.ast2ast -================ - -.. currentmodule:: qlasskit - -.. autofunction:: ast2ast \ No newline at end of file diff --git a/docs/source/generated/qlasskit.ast2logic.env.rst b/docs/source/generated/qlasskit.ast2logic.env.rst deleted file mode 100644 index 9da2c9b3..00000000 --- a/docs/source/generated/qlasskit.ast2logic.env.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.ast2logic.env -====================== - -.. automodule:: qlasskit.ast2logic.env - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Env - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.exceptions.rst b/docs/source/generated/qlasskit.ast2logic.exceptions.rst deleted file mode 100644 index d5ae90d4..00000000 --- a/docs/source/generated/qlasskit.ast2logic.exceptions.rst +++ /dev/null @@ -1,37 +0,0 @@ -qlasskit.ast2logic.exceptions -============================= - -.. automodule:: qlasskit.ast2logic.exceptions - - - - - - - - - - - - - - - - .. rubric:: Exceptions - - .. autosummary:: - - ExpressionNotHandledException - NoReturnTypeException - OperationNotSupportedException - OutOfBoundException - StatementNotHandledException - TypeErrorException - UnboundException - UnknownSymbolException - UnknownTypeException - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.rst b/docs/source/generated/qlasskit.ast2logic.rst deleted file mode 100644 index f75feb6d..00000000 --- a/docs/source/generated/qlasskit.ast2logic.rst +++ /dev/null @@ -1,38 +0,0 @@ -qlasskit.ast2logic -================== - -.. automodule:: qlasskit.ast2logic - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.ast2logic.env - qlasskit.ast2logic.exceptions - qlasskit.ast2logic.t_arguments - qlasskit.ast2logic.t_ast - qlasskit.ast2logic.t_expression - qlasskit.ast2logic.t_statement - qlasskit.ast2logic.typing - qlasskit.ast2logic.utils - diff --git a/docs/source/generated/qlasskit.ast2logic.t_arguments.rst b/docs/source/generated/qlasskit.ast2logic.t_arguments.rst deleted file mode 100644 index 4ef36d77..00000000 --- a/docs/source/generated/qlasskit.ast2logic.t_arguments.rst +++ /dev/null @@ -1,30 +0,0 @@ -qlasskit.ast2logic.t\_arguments -=============================== - -.. automodule:: qlasskit.ast2logic.t_arguments - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - translate_argument - translate_arguments - - - - - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.t_ast.rst b/docs/source/generated/qlasskit.ast2logic.t_ast.rst deleted file mode 100644 index ab4eb0c3..00000000 --- a/docs/source/generated/qlasskit.ast2logic.t_ast.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.ast2logic.t\_ast -========================= - -.. automodule:: qlasskit.ast2logic.t_ast - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - translate_ast - - - - - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.t_expression.rst b/docs/source/generated/qlasskit.ast2logic.t_expression.rst deleted file mode 100644 index 60018cb2..00000000 --- a/docs/source/generated/qlasskit.ast2logic.t_expression.rst +++ /dev/null @@ -1,30 +0,0 @@ -qlasskit.ast2logic.t\_expression -================================ - -.. automodule:: qlasskit.ast2logic.t_expression - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - decompose_to_symbols - translate_expression - - - - - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.t_statement.rst b/docs/source/generated/qlasskit.ast2logic.t_statement.rst deleted file mode 100644 index 12ff979c..00000000 --- a/docs/source/generated/qlasskit.ast2logic.t_statement.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.ast2logic.t\_statement -=============================== - -.. automodule:: qlasskit.ast2logic.t_statement - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - translate_statement - - - - - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.typing.rst b/docs/source/generated/qlasskit.ast2logic.typing.rst deleted file mode 100644 index 09788cd3..00000000 --- a/docs/source/generated/qlasskit.ast2logic.typing.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.ast2logic.typing -========================= - -.. automodule:: qlasskit.ast2logic.typing - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Arg - - - - - - - - - diff --git a/docs/source/generated/qlasskit.ast2logic.utils.rst b/docs/source/generated/qlasskit.ast2logic.utils.rst deleted file mode 100644 index 59a153ca..00000000 --- a/docs/source/generated/qlasskit.ast2logic.utils.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.ast2logic.utils -======================== - -.. automodule:: qlasskit.ast2logic.utils - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - flatten - - - - - - - - - - - - - diff --git a/docs/source/generated/qlasskit.boolopt.bool_optimizer.rst b/docs/source/generated/qlasskit.boolopt.bool_optimizer.rst deleted file mode 100644 index 6c7482ee..00000000 --- a/docs/source/generated/qlasskit.boolopt.bool_optimizer.rst +++ /dev/null @@ -1,38 +0,0 @@ -qlasskit.boolopt.bool\_optimizer -================================ - -.. automodule:: qlasskit.boolopt.bool_optimizer - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - apply_cse - custom_simplify_logic - merge_expressions - print_step - - - - - - .. rubric:: Classes - - .. autosummary:: - - BoolOptimizerProfile - - - - - - - - - diff --git a/docs/source/generated/qlasskit.boolopt.exp_transformers.rst b/docs/source/generated/qlasskit.boolopt.exp_transformers.rst deleted file mode 100644 index 99ccd642..00000000 --- a/docs/source/generated/qlasskit.boolopt.exp_transformers.rst +++ /dev/null @@ -1,32 +0,0 @@ -qlasskit.boolopt.exp\_transformers -================================== - -.. automodule:: qlasskit.boolopt.exp_transformers - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - remove_ITE - remove_Implies - transform_or2and - transform_or2xor - - - - - - - - - diff --git a/docs/source/generated/qlasskit.boolopt.rst b/docs/source/generated/qlasskit.boolopt.rst deleted file mode 100644 index c00d91b0..00000000 --- a/docs/source/generated/qlasskit.boolopt.rst +++ /dev/null @@ -1,33 +0,0 @@ -qlasskit.boolopt -================ - -.. automodule:: qlasskit.boolopt - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.boolopt.bool_optimizer - qlasskit.boolopt.exp_transformers - qlasskit.boolopt.sympytransformer - diff --git a/docs/source/generated/qlasskit.boolopt.sympytransformer.rst b/docs/source/generated/qlasskit.boolopt.sympytransformer.rst deleted file mode 100644 index b46bcee4..00000000 --- a/docs/source/generated/qlasskit.boolopt.sympytransformer.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.boolopt.sympytransformer -================================= - -.. automodule:: qlasskit.boolopt.sympytransformer - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - SympyTransformer - - - - - - - - - diff --git a/docs/source/generated/qlasskit.compiler.compiler.rst b/docs/source/generated/qlasskit.compiler.compiler.rst deleted file mode 100644 index c1cb75bb..00000000 --- a/docs/source/generated/qlasskit.compiler.compiler.rst +++ /dev/null @@ -1,35 +0,0 @@ -qlasskit.compiler.compiler -========================== - -.. automodule:: qlasskit.compiler.compiler - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Compiler - - - - - - .. rubric:: Exceptions - - .. autosummary:: - - CompilerException - - - - - diff --git a/docs/source/generated/qlasskit.compiler.expqmap.rst b/docs/source/generated/qlasskit.compiler.expqmap.rst deleted file mode 100644 index 00d9caaa..00000000 --- a/docs/source/generated/qlasskit.compiler.expqmap.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.compiler.expqmap -========================= - -.. automodule:: qlasskit.compiler.expqmap - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - ExpQMap - - - - - - - - - diff --git a/docs/source/generated/qlasskit.compiler.internalcompiler.rst b/docs/source/generated/qlasskit.compiler.internalcompiler.rst deleted file mode 100644 index 1282e670..00000000 --- a/docs/source/generated/qlasskit.compiler.internalcompiler.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.compiler.internalcompiler -================================== - -.. automodule:: qlasskit.compiler.internalcompiler - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - InternalCompiler - - - - - - - - - diff --git a/docs/source/generated/qlasskit.compiler.poccompiler3.rst b/docs/source/generated/qlasskit.compiler.poccompiler3.rst deleted file mode 100644 index 104b474f..00000000 --- a/docs/source/generated/qlasskit.compiler.poccompiler3.rst +++ /dev/null @@ -1,36 +0,0 @@ -qlasskit.compiler.poccompiler3 -============================== - -.. automodule:: qlasskit.compiler.poccompiler3 - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - count_symbol_in_expr - count_symbols_in_exprs - - - - - - .. rubric:: Classes - - .. autosummary:: - - POCCompiler3 - - - - - - - - - diff --git a/docs/source/generated/qlasskit.compiler.rst b/docs/source/generated/qlasskit.compiler.rst deleted file mode 100644 index 16886b65..00000000 --- a/docs/source/generated/qlasskit.compiler.rst +++ /dev/null @@ -1,41 +0,0 @@ -qlasskit.compiler -================= - -.. automodule:: qlasskit.compiler - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - to_quantum - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.compiler.compiler - qlasskit.compiler.expqmap - qlasskit.compiler.internalcompiler - qlasskit.compiler.poccompiler3 - qlasskit.compiler.tweedledumcompiler - diff --git a/docs/source/generated/qlasskit.compiler.tweedledumcompiler.rst b/docs/source/generated/qlasskit.compiler.tweedledumcompiler.rst deleted file mode 100644 index d354f947..00000000 --- a/docs/source/generated/qlasskit.compiler.tweedledumcompiler.rst +++ /dev/null @@ -1,36 +0,0 @@ -qlasskit.compiler.tweedledumcompiler -==================================== - -.. automodule:: qlasskit.compiler.tweedledumcompiler - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - sympy_to_logic_network - twcircuit_to_qcircuit - - - - - - .. rubric:: Classes - - .. autosummary:: - - TweedledumCompiler - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.cnotsim.rst b/docs/source/generated/qlasskit.qcircuit.cnotsim.rst deleted file mode 100644 index 9a869e94..00000000 --- a/docs/source/generated/qlasskit.qcircuit.cnotsim.rst +++ /dev/null @@ -1,35 +0,0 @@ -qlasskit.qcircuit.cnotsim -========================= - -.. automodule:: qlasskit.qcircuit.cnotsim - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - CNotSim - - - - - - .. rubric:: Exceptions - - .. autosummary:: - - GateNotSimulableException - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.exporter.rst b/docs/source/generated/qlasskit.qcircuit.exporter.rst deleted file mode 100644 index da1b76c6..00000000 --- a/docs/source/generated/qlasskit.qcircuit.exporter.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.exporter -========================== - -.. automodule:: qlasskit.qcircuit.exporter - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - QCircuitExporter - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.exporter_cirq.rst b/docs/source/generated/qlasskit.qcircuit.exporter_cirq.rst deleted file mode 100644 index 847f1de9..00000000 --- a/docs/source/generated/qlasskit.qcircuit.exporter_cirq.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.exporter\_cirq -================================ - -.. automodule:: qlasskit.qcircuit.exporter_cirq - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - CirqExporter - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.exporter_qasm.rst b/docs/source/generated/qlasskit.qcircuit.exporter_qasm.rst deleted file mode 100644 index fe47f6a9..00000000 --- a/docs/source/generated/qlasskit.qcircuit.exporter_qasm.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.exporter\_qasm -================================ - -.. automodule:: qlasskit.qcircuit.exporter_qasm - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - QasmExporter - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.exporter_qiskit.rst b/docs/source/generated/qlasskit.qcircuit.exporter_qiskit.rst deleted file mode 100644 index e096dace..00000000 --- a/docs/source/generated/qlasskit.qcircuit.exporter_qiskit.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.exporter\_qiskit -================================== - -.. automodule:: qlasskit.qcircuit.exporter_qiskit - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - QiskitExporter - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.exporter_sympy.rst b/docs/source/generated/qlasskit.qcircuit.exporter_sympy.rst deleted file mode 100644 index 35217b04..00000000 --- a/docs/source/generated/qlasskit.qcircuit.exporter_sympy.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.exporter\_sympy -================================= - -.. automodule:: qlasskit.qcircuit.exporter_sympy - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - SympyExporter - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.gates.rst b/docs/source/generated/qlasskit.qcircuit.gates.rst index a9955961..35c21029 100644 --- a/docs/source/generated/qlasskit.qcircuit.gates.rst +++ b/docs/source/generated/qlasskit.qcircuit.gates.rst @@ -1,4 +1,4 @@ -qlasskit.qcircuit.gates +qlasskit.qcircuit.gates ======================= .. automodule:: qlasskit.qcircuit.gates diff --git a/docs/source/generated/qlasskit.qcircuit.qcircuit.rst b/docs/source/generated/qlasskit.qcircuit.qcircuit.rst deleted file mode 100644 index 74bb8181..00000000 --- a/docs/source/generated/qlasskit.qcircuit.qcircuit.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.qcircuit -========================== - -.. automodule:: qlasskit.qcircuit.qcircuit - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - QCircuit - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.qcircuitenhanced.rst b/docs/source/generated/qlasskit.qcircuit.qcircuitenhanced.rst deleted file mode 100644 index 20b57127..00000000 --- a/docs/source/generated/qlasskit.qcircuit.qcircuitenhanced.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.qcircuit.qcircuitenhanced -================================== - -.. automodule:: qlasskit.qcircuit.qcircuitenhanced - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - QCircuitEnhanced - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.qcircuitwrapper.rst b/docs/source/generated/qlasskit.qcircuit.qcircuitwrapper.rst deleted file mode 100644 index 92a6d2e3..00000000 --- a/docs/source/generated/qlasskit.qcircuit.qcircuitwrapper.rst +++ /dev/null @@ -1,35 +0,0 @@ -qlasskit.qcircuit.qcircuitwrapper -================================= - -.. automodule:: qlasskit.qcircuit.qcircuitwrapper - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - reindex - - - - - - .. rubric:: Classes - - .. autosummary:: - - QCircuitWrapper - - - - - - - - - diff --git a/docs/source/generated/qlasskit.qcircuit.rst b/docs/source/generated/qlasskit.qcircuit.rst deleted file mode 100644 index ce1089ca..00000000 --- a/docs/source/generated/qlasskit.qcircuit.rst +++ /dev/null @@ -1,40 +0,0 @@ -qlasskit.qcircuit -================= - -.. automodule:: qlasskit.qcircuit - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.qcircuit.cnotsim - qlasskit.qcircuit.exporter - qlasskit.qcircuit.exporter_cirq - qlasskit.qcircuit.exporter_qasm - qlasskit.qcircuit.exporter_qiskit - qlasskit.qcircuit.exporter_sympy - qlasskit.qcircuit.gates - qlasskit.qcircuit.qcircuit - qlasskit.qcircuit.qcircuitenhanced - qlasskit.qcircuit.qcircuitwrapper - diff --git a/docs/source/generated/qlasskit.qlassf.rst b/docs/source/generated/qlasskit.qlassf.rst deleted file mode 100644 index dc2fa1da..00000000 --- a/docs/source/generated/qlasskit.qlassf.rst +++ /dev/null @@ -1,6 +0,0 @@ -qlasskit.qlassf -=============== - -.. currentmodule:: qlasskit - -.. autofunction:: qlassf \ No newline at end of file diff --git a/docs/source/generated/qlasskit.qlassfun.rst b/docs/source/generated/qlasskit.qlassfun.rst deleted file mode 100644 index 22d9d490..00000000 --- a/docs/source/generated/qlasskit.qlassfun.rst +++ /dev/null @@ -1,35 +0,0 @@ -qlasskit.qlassfun -================= - -.. automodule:: qlasskit.qlassfun - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - qlassf - - - - - - .. rubric:: Classes - - .. autosummary:: - - QlassF - - - - - - - - - diff --git a/docs/source/generated/qlasskit.types.qbool.rst b/docs/source/generated/qlasskit.types.qbool.rst deleted file mode 100644 index 0262cb65..00000000 --- a/docs/source/generated/qlasskit.types.qbool.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.types.qbool -==================== - -.. automodule:: qlasskit.types.qbool - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Qbool - - - - - - - - - diff --git a/docs/source/generated/qlasskit.types.qint.rst b/docs/source/generated/qlasskit.types.qint.rst deleted file mode 100644 index 5662ef4c..00000000 --- a/docs/source/generated/qlasskit.types.qint.rst +++ /dev/null @@ -1,35 +0,0 @@ -qlasskit.types.qint -=================== - -.. automodule:: qlasskit.types.qint - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Qint - Qint12 - Qint16 - Qint2 - Qint3 - Qint4 - Qint8 - - - - - - - - - diff --git a/docs/source/generated/qlasskit.types.qlist.rst b/docs/source/generated/qlasskit.types.qlist.rst deleted file mode 100644 index 363bebaa..00000000 --- a/docs/source/generated/qlasskit.types.qlist.rst +++ /dev/null @@ -1,30 +0,0 @@ -qlasskit.types.qlist -==================== - -.. automodule:: qlasskit.types.qlist - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Qlist - QlistMeta - - - - - - - - - diff --git a/docs/source/generated/qlasskit.types.qtype.rst b/docs/source/generated/qlasskit.types.qtype.rst deleted file mode 100644 index 17b41b87..00000000 --- a/docs/source/generated/qlasskit.types.qtype.rst +++ /dev/null @@ -1,29 +0,0 @@ -qlasskit.types.qtype -==================== - -.. automodule:: qlasskit.types.qtype - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - Qtype - - - - - - - - - diff --git a/docs/source/generated/qlasskit.types.rst b/docs/source/generated/qlasskit.types.rst deleted file mode 100644 index 4278208f..00000000 --- a/docs/source/generated/qlasskit.types.rst +++ /dev/null @@ -1,43 +0,0 @@ -qlasskit.types -============== - -.. automodule:: qlasskit.types - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - const_to_qtype - format_outcome - interpret_as_qtype - type_repr - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - qlasskit.types.qbool - qlasskit.types.qint - qlasskit.types.qlist - qlasskit.types.qtype - diff --git a/docs/source/index.rst b/docs/source/index.rst index 41eb9a01..fca8ed0e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -18,6 +18,8 @@ Python and translate them into unitary operators (gates) for use in quantum circ :maxdepth: 2 :caption: Examples + example_grover_1.ipynb + Indices and tables ================== diff --git a/docs/source/quickstart.ipynb b/docs/source/quickstart.ipynb index 644bb187..c78f106f 100644 --- a/docs/source/quickstart.ipynb +++ b/docs/source/quickstart.ipynb @@ -6,128 +6,103 @@ "source": [ "# Quickstart\n", "\n", - "First, install the qlasskit library using pip:\n", + "First install qlasskit using pip.\n", "\n", - "```\n", - "pip install qlasskit\n", - "```\n", + "```pip install qlasskit```\n", "\n", - "We now define a function named `and_all` that returns `True` iff all the element of an input list `a_list` are `True`." + "We now define a qlassf function that sums two numbers:" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "from qlasskit import qlassf, Qlist, Qint2\n", + "from qlasskit import qlassf, Qint2\n", "\n", - "@qlassf\n", - "def and_all(a_list: Qlist[bool, 4]) -> bool:\n", - " r = True\n", - " for i in a_list:\n", - " r = r and i\n", - " return r " + "@qlassf \n", + "def sum_two_numbers(a: Qint2, b: Qint2) -> Qint2:\n", + " return a + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The qlasskit compiler will produce an optimized quantum circuit performing the given function." + "We can now export the resulting quantum circuit to any supported framework:" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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J8uXLcTgcbNq0KdJlioSUzwc9vdDZA33eSFcjkeDzWT8X/9su4iJdQKjV1dVRUVGB2+0mMTGRwsJCTp48ycaNG2loaKC1tRWAkpKSyBYqEiId3fB2A7x1BE53WK/FOGBOFtySDwUZ4HBEtkYJrc5WcNVByz7o7bFeO98JB16DrBJISo9kdSNn6yDzeDwsWbIEt9vN6tWrWbt2LcnJyQA8+eSTPPLII8TFxeFwOCgqKopwtSLB994J+Pe34EJf/9e9Pni/yfrJvxa+eRskjI1MjRI6Ph80vAmNbw883VVn/UyZB/l3mLtDY+tTiytXrsTlcrFixQo2bNhwKcQAKisrKS4upre3l5ycHFJSUiJYqUjwvXcCfrnzyhD7tMMfws+3W6cdxV4O/3HwELvciXesozNTTzfaNsjq6+upqqoiPT2ddevWDTjPvHnzACguLr702rPPPst9993H1KlTSUhIYObMmXz/+9+no6MjLHWLBENnD/zqLRjudunEafj9+yEtScLs9HE4UTP8+Zvfg48aQldPKNk2yLZs2YLX62Xp0qUkJSUNOE98fDzQP8g2bNhAbGwsP/rRj3j55Zf5zne+w89+9jPuuusuvF5dIRcz/PkonB/iSOzT3m6A8zoqsw3Xu+FpEw1se41s+/btACxYsGDQeVwuF9A/yF566SUmTZp06d+33347kyZNYunSpbz55pvcdtttIapYJHjeOux/m4/PQ90JKJsW/HokvHo6Aju6aj0OH5+BhAnBrii0bBtkx48fB2Dq1KkDTu/t7WXXrl1A/yC7PMQuKi0tBaC5uTmgWkpLS3G73QG1DbbPP1ZNwvhMWtwtZGXdGOlywm40vH9HTBz3rWsMqO3adf/E3pd/FNyCJOxmZs3ne1/6dUBt71/yEO8dez3IFQ0tIyODmho/zoVexrZB1tnZCUBXV9eA06uqqvB4PCQnJ5Obm3vVZe3YsQOAWbNmBVSL2+0OOASDra+v79LvaKkpnEbD+48blxBw24+7z9v27zKaZCadC7jtubMdxq0Dtg2yjIwM2traqK2tpby8vN+0lpYW1qxZA0BRURGOq4w5bW5u5gc/+AF33XVXwPeaZWRkBNQuFGJjYy/9njx5coSrCb/R8v77LnQTO8bpd7sxjvO2/ruMFuMSAh/+MDbeEZF1YCTbSdsG2cKFC6mvr2f9+vUsWrSIgoICAKqrq1m2bBkejwe4+o3QHR0dfOELX2Ds2LE888wzAdcS6OFyKKx9Hs52QWZG5qVrhKPJaHn//74LahoDaPdUJRnjK4Nej4SXzwtv/QK6zvrXbmwivPrWs8TEhqauULHtqMXKykrS0tJoampi9uzZzJ07l/z8fMrKypg2bRp33nkn0P/62OW6urpYsmQJx44d4w9/+AOZmZnhLF9kRG6d4X+b/GshY3zwa5Hwc8TA5IE3bVc1uQjjQgxsHGRZWVns3LmTxYsX43Q6aWxsJDU1lc2bN7Nt2zYOHToEDBxkFy5c4Etf+hI1NTW8/PLLFBYWhrt8kRGZmmY9gmq4YmOgQg+3sZXJRRDvx47JuGTILglZOSFl21OLYA3O2Lp16xWvd3R00NjYSExMDHPmzOk37eK9Z6+//jr/9V//RVlZWbjKFQkahwOW3QL//AYc+fDq88bGwLL5MO2asJQmYTLGCSX3Qe1voKf96vOOTYTr/9L6bSJbB9lg9u3bh8/no6CggISE/iO8vvvd7/Kb3/yG733veyQkJLBnz55L06ZPnz7g8HyRaDQuDr69AF7bZ91Xdq77ynlmZMLn5ijE7CoxFcqWQsMucNeD91M3vMfEwrUzYfot4DT4KX2jMsj27t0LDHxa8eWXXwbgiSee4Iknnug37V//9V/5xje+EfL6RIIlLhbuKoKFs+GDZtiy23qmojMOVlfAJIM3XjI845Kg8HOQfzt8eBAOvwF9FyBuHMz/KxgbH+kKR05B9imNjY1hrkYk9OJioWQKvFBjBdm4MQqx0WaME7KK4dhuK8hix9gjxMDGgz2u5mpBJiIiZhmVR2QXn8MoIiLmG5VHZCIiYh8KMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRooyLIPB4PlZWV5OXl4XQ6yc7OZtWqVXR2drJ8+XIcDgebNm2KdJkiIhKAuEgXEGp1dXVUVFTgdrtJTEyksLCQkydPsnHjRhoaGmhtbQWgpKQksoWKSMj09sH7TbC/GbouwNg4yE6FsmmQ5Ix0dTJStg4yj8fDkiVLcLvdrF69mrVr15KcnAzAk08+ySOPPEJcXBwOh4OioqIIVysiwebzwZ8Owmv7oL27/7R3j8N/vQeluXDvPBg3JjI1ysjZ+tTiypUrcblcrFixgg0bNlwKMYDKykqKi4vp7e0lJyeHlJSUCFYqIsHm88Hz78AL71wZYhf1emFPA2x6DbrOh7c+CR7bBll9fT1VVVWkp6ezbt26AeeZN28eAMXFxZde27lzJwsXLiQzM5Nx48aRlZXFAw88QH19fVjqFpHgePMQ7Dw4vHmbWuH/7QptPRI6tj21uGXLFrxeL0uXLiUpKWnAeeLj44H+QdbW1sbcuXN5+OGHueaaa3C5XKxbt47y8nI++OADsrKywlK/iASuz2udTvTH/pPgaoWs1NDUJKFj2yDbvn07AAsWLBh0HpfLBfQPsnvuuYd77rmn33w33ngjM2bM4LnnnmPVqlUhqFZEgukDF5zt8r/drsPwwE3Br0dCy7anFo8fPw7A1KlTB5ze29vLrl3WuYTLg2wgaWlpAMTF2Tb3RWyl/mR420lk2XbL3NnZCUBX18C7ZVVVVXg8HpKTk8nNzb1iel9fH16vl+PHj/Poo4+SkZHBl7/85YBqKS0txe12B9Q22D7/WDUJ4zNpcbeQlXVjpMsJO73/0fH+b1r6M7KLlvjd7qO2DrKyZoagoujx4+XVpCZn0tISXetARkYGNTU1AbW1bZBlZGTQ1tZGbW0t5eXl/aa1tLSwZs0aAIqKinA4HFe0v/322y8dseXl5bF9+3YmTZoUUC1ut5vm5uaA2gZbX1/fpd/RUlM46f2PjvfffuZ0QO3Od7Xb+u8C9lwHbBtkCxcupL6+nvXr17No0SIKCgoAqK6uZtmyZXg8HmDwG6F/8YtfcObMGY4dO8bf//3f89nPfpZdu3YxZcoUv2vJyMgI+H0EW2xs7KXfkydPjnA14af3Pzref/fpQwG1O9v8vq3/LhC968BItpMOn8/nC2ItUcPlclFSUsLp06eJi4tj5syZdHd3c+TIESoqKvB6vbzyyis8/fTTfOtb37rqss6cOUNOTg4PPvig8Y+yWvu8dRF8fDz88C8jXU346f2PjvfffcF6rz29/rV7eAHMui40NUWLnT+Hng4YlwSf+XakqwkO2w72yMrKYufOnSxevBin00ljYyOpqals3ryZbdu2ceiQtcc21EAPgAkTJpCXl8eRI0dCXbaIBIFzDNw03b8216bAjMzQ1COhZdtTiwCzZs1i69atV7ze0dFBY2MjMTExzJkzZ8jlnDp1ioMHD3LTTRqXK2KKJddDcxs0nBp63sRxsPx2iLnycrkYwNZBNph9+/bh8/koKCggISGh37QHH3yQvLw8SkpKmDBhAocPH+YnP/kJcXFx/M3f/E2EKhYRf42JtU4VbtljPVdxMBnj4b/dBtfoKXXGGpVBtnfvXmDg04o333wz//Zv/8Y//uM/0t3dTXZ2NgsWLOCxxx4b9J40EYlOY+PgoVvhriJ467D19HtPO/iAuBj47wsg/1oYYOCyGERB9ikrVqxgxYoV4S5JRELo2hTrCff3zvtkwEviOCiIngHFMgK2HexxNVcLMhERMcuoPCK7+BxGEREx36g8IhMREftQkImIiNEUZCIiYjQFmYiIGE1BJiIiRlOQiYiI0RRkIiJiNAWZiIgYTUEmIiJGU5CJiIjRFGQiImI0BZmIiBhNQSYiIkZTkImIiNEUZCIiYjQFmYiIGE1BJiIiRlOQiYiI0RRkIiJiNAWZiIgYTUEmIiJGU5CJiIjRFGQiImI0BZmIiBhNQSYiIkZTkImIiNEUZCIiYjQFmYiIGE1BJiIiRlOQiYiI0RRkIiJiNAWZiIgYTUEmIiJGU5CJiIjRFGQiImI0BZmIiBhNQSYiIkZTkI0ifV7w+SJdhUSKz/fJ56/1QOwkLtIFSGj4fHD0FBxogaZW66ez55Pp57rg59shOxVmZsK0a8DhiFy9Eny9ffBBs7UeNLVCcxuc77WmneuG//1byE6z1oG52XBtSkTLFQmYgsxmzvfC2w2w6zC4zw4+nw8r5A60wKv7IGM83JIPN02HsVorjHb2Y3jzEOxugI7uwedr7bR+3jsBW+ugIANuLYC5WdqpEbNok2UjDadgy27wdPjf1n0WnquBPx6Ar5bD9GuCX5+Els8Hexrgt+9AT6//7Q+5rZ+CDPjKTZCaFPwaRUJB18hswOuF39XCplcDC7HLeTqs5fyu1lqumKGzBzbvgKq3Awuxyx1yw/ptUHMsOLWJhJqOyAzX54V/fwvePR68ZfqAHfVw5mN4cD7EancnqrV3wT+9Di1XOZXsr55ea73q6IE7ZgZvuSKhoE2UwXw++M89wQ2xy7173Fq+RrhFr67z8LMdwQ2xy/32Hdh9JDTLFgkWBZnB9jRAdYhP/1QfswaPSHR6vgZOtoW2j2erQ9+HyEjo1KKh2jqtvWV//e1dkBJvDb//8e+H1+a3tTAjEyYm+t+fhM4HLv93ZAL5/Pu88B974G8+p9PMEp1sv1p6PB4qKyvJy8vD6XSSnZ3NqlWr6OzsZPny5TgcDjZt2hTpMv32fE1gF/VT4mFCgvV7uLovwAsBhKaETm8f/ObP/rcL5PMHcLXCzoP+9ycSDrY+Iqurq6OiogK3201iYiKFhYWcPHmSjRs30tDQQGtrKwAlJSWRLdRPpzusvfFw2ttk9ZumIdlRoe4EnO0Kb587D8FtMyDG9ru/YhrbrpIej4clS5bgdrtZvXo1LS0t1NbW4na7Wb9+Pdu2baO6uhqHw0FRUVGky/XLrsPWyMJw8gFvHQ5zpzKoNw+Fv8/THVDfEv5+RYZi2yBbuXIlLpeLFStWsGHDBpKTky9Nq6yspLi4mN7eXnJyckhJMefZPD5f5O7vqT6mEYzRwNMOjZ7I9F19NDL9ilyNLYOsvr6eqqoq0tPTWbdu3YDzzJs3D4Di4uJBl1NRUYHD4eDxxx8PRZkBOdtlXaiPhHNd4T+dJVc6fjpyfTe1Rq5vkcHYMsi2bNmC1+tl6dKlJCUNfFEnPt662j1YkP3617+mrq4uVCUGrCmCG7Fo6F8i+xmc7uj/8GmRaGDLINu+fTsACxYsGHQel8saLTFQkJ07d46//uu/ZsOGDaEpcASu9iDg0dC/RP4ziHT/Ip9my1GLx49bj7qYOnXqgNN7e3vZtWsXMHCQff/736egoIClS5fy4IMPjrie0tJS3G73iJcDMOdzjzDzzv8x4LSL9whdTYrzk9+P3zv4fIPdZ/QPP3mKb76yfpjVRp/PP1ZNwvhMWtwtZGXdGOlyAnLHt58jPfemAacNtQ4M9/OHwdeBB766DPfBHcOsNvrYYR0YiR8vryY1OZOWluh6/xkZGdTU1ATU1pZB1tnZCUBX18AXdKqqqvB4PCQnJ5Obm9tvWk1NDf/8z//MO+8E78Ypt9tNc3NzUJY1pf3coNMu3iM0HDExw5/3cufOnQ3ae4mEvr6+S79NfR89PYOf2xvuOhDo5w/w0UenjP3bgT3WgZGw4/u3ZZBlZGTQ1tZGbW0t5eXl/aa1tLSwZs0aAIqKinBc9sVLfX19PPzww6xYsYLZs2cHtZ5giR87+Nng4QwCSXFaGzGv1/pyRX+XleCMY/LkyUN3FKViY2Mv/Tb1fcQ6+gadNtQ6MNzP/2rLmpiSSJ+hfzuwxzowEtH6/keynbRlkC1cuJD6+nrWr1/PokWLKCgoAKC6upply5bh8Vhjlz99I/SmTZv48MMPgz5KMdDD5YF84IJ/+ePA04bzyKHH77X2xM91w+Mv+N///133PeZkfc//hlFi7fPWyMvMjMxL10lN87ta69sJBjLUOjDSzx9g1+vPkzgusLbRwA7rwEjs/Dn0dEBmpn3evy0He1RWVpKWlkZTUxOzZ89m7ty55OfnU1ZWxrRp07jzzjuB/tfHPB4PP/jBD/i7v/s7ent7OXPmDGfOnAGgu7ubM2fO4I2CL+jKSh3d/UtkP4O0JIwOMbEnWwZZVlYWO3fuZPHixTidThobG0lNTWXz5s1s27aNQ4esxyJcHmQul4v29nYefvhhJk6ceOkHYP369UycOJETJ05E5P1cbny89TPa+pZPTE2LXN9TIti3yGBseWoRYNasWWzduvWK1zs6OmhsbCQmJoY5c+Zcej0vL48dO64cibVgwQIeeughvvGNbwT1WlegHA4ozYXX94e/7xunWf1LZKUnQ+4kOPZR+Pu+MXfoeUTCzbZBNph9+/bh8/koKCggIeGTYVtJSUnccccdA7bJyckZdFokzM+H7fvD+7xFhwPm54WxQ7mqW/PDH2RpSTDzuvD2KTIctjy1eDV79+4Frv5oqmiXlgRF2eHtsygLUvXk+6hRPCXw4fOBum0GxOiIXKLQqDsi8zfIfFH6lNx7S+Gg2/qusFBzjrH6k+gRFwtfLoOn3whPf1PS4NaC8PQl4i8dkRlqQgLcO8//due64MzH/j14+N554d/7l6EVToayaf61CeTzj42Br5Xr26Eleo26I7KLz2G0g7JpcPQjeLth+G2G+/X2F9003f+NpYTPX5bCyTPWNzgPh7+fP1hHfhnj/W8nEi7axzKYw2FtZOblhGb583LggTKNVIxmzjHw7QVw3cTQLP++UmtnRiSaKcgMFxsDS+fDXxQGL3AcDmt5S+fra+1NkOSEFQthdhCfNuQcA1+/BT4zI3jLFAmVUXdq0Y5iHLDkepiTBVv2wKnBnys8pGtS4Ks3W/cpiTkSxsJf3W59i/cL70DX+cCXNTMTvnKzrouKORRkNpI7Cf5nBdQcgzcPWddOhuu6ida9SaW5MFZrhZEcDut65qxM2HUYdh8Z/jd6O7DuEbs13xpEotPJYhJtsmxmbJx1w3R5HjR64GCL9fX0rlZrpJoPa6OVEm89sy87FWZkQk66Nl52kRwPdxXBojmwv9kaEORqBVfbJ0dqMQ7rfsTsVMhOg7lZ1hNDREykILMph8M6Qrv8FKHPB16ftRFTaNlfbAzMzbZ+LvL6rPVAQ+nFThRko4jDAbEKsFEtxoF1SC5iI9ovExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRocZEuQCSUPu4BVxs0nYZT7fDxeev1rvPw5iHISoXrJsBY/Z9gSz4fnO2yPv+mVmjr7L8OVB+11oFrUyDGprv1veeh4xSc+xA6T8OFbuv1C91wvAZSroXkayFubGTrHAmHz+fzRboIkWDyeqG+xQqqAydhqBV8TCzMy4FbCiA7NRwVSqj19EJto7UONLcNPX+yE8rzrJ+JiSEvL+R8PjjbDE11cOoQ+LxXn9/hgEn5kFUCE7Otf5tEQSa2crAFfvNn8HQE1j7/WvjyTTApObh1SXh4fbDrEGx7D7ov+N/e4YCbp8M910O8oUco7aeg/hXrCCwQSZNg1mdhfGZw6wolBZnYQvcFeLEW3joy8mWNiYW7S+AzMyDGsD3T0czTDv/5NhwJcAN+uQkJ8MBNMOu6kS8rXLx9cGwPNL499BHYkByQcyNMmw8xBpx2V5CJ8dq74efbh3cKyR+lOfDVcoi16bUTOznugc07Prn+FSz3lVo7NNGu7wK8/zs43Rjc5U7MhuJ7o//6mYJMjNbZA0+9Cu6zoVn+9VNh2Xz7DgSwg6ZW+OlrgZ1KHI5oDzNvL9S9AK3HQ7P88ZPhhi9B7JjQLD8Y9L+nGMvrg1/uDF2IAbx7HH6/N3TLl5Hp6LaOxEIVYgDP1UD9ydAtf6QOvB66EANr0Mj+V0K3/GAw4OynyMB2HYLDfl4P+du7ICUeznXBj38/vDav7YM5WTAlzf8aJbSerbbCzB+BrANVb8Mji6NvAIjnKJz0c0er7EEYmwjnO+HP/z68Nh8egGsK4NoC/2sMh1FxRObxeKisrCQvLw+n00l2djarVq2is7OT5cuX43A42LRpU6TLFD+c7oCX3vW/XUq8dSE/JX74bbw+2LIbevv8709C570TUHfC/3aBrANnPobf1frfVyj19kD9H/xvNzYRnMnWb38ceBXOd/nfXzjY/oisrq6OiooK3G43iYmJFBYWcvLkSTZu3EhDQwOtra0AlJSURLZQ8cuO/XA+jMHSchbeb4IbcsLXpwzO5wv/Kd+3j8Ln5kbPfWYnP4CeAG8zCcSFLmh+D3JvDl+fw2XrIzKPx8OSJUtwu92sXr2alpYWamtrcbvdrF+/nm3btlFdXY3D4aCoqCjS5cowdV+A6mPh73fX4fD3KQM79hG0nAlvnz4fvBUl64DPB6668Pfres964EC0sXWQrVy5EpfLxYoVK9iwYQPJyZ/c5VpZWUlxcTG9vb3k5OSQkpISwUrFH7WN1pMbwq3hVGgHlsjwRWqnYk8D9EXBhrytCT4O8u0mw9HTDqePhr/fodg2yOrr66mqqiI9PZ1169YNOM+8efMAKC4uvvTaG2+8gcPhuOJHpx6jh78DPIIpGDfbyshF6nNo74ZT5yLT9+XaArg2GLS+myLX92Bse41sy5YteL1eli5dSlJS0oDzxMdbV3svD7KLfvrTn3LDDTdc+ndiYpScGBdcraOzb7Gc67IeBBwpTa2QOSFy/UPgj58yve/B2DbItm/fDsCCBQsGncflcgEDB1lhYSE33xyFVzVHue4L8FF75PpvUpBFXKR3JppaoWxaZGtoj2CYtJ+yrtFF04OFbRtkx49bdwhOnTp1wOm9vb3s2rULGDjIgqm0tBS32x3SPkaLhInZfP57uwedfvEeocGkOD/5/fi9g8832D1Gh4+dJCurbJjVSihMueE+yh74xwGnDfX5w8jXgV/9529Z9cUVw6w2NJ5ZeZyYmNgBp128T2ww4xI/+X3rw4PPN9h9Zn3nITdnGr19wX0eWEZGBjU1NQG1tW2QdXZ2AtDVNfA5iKqqKjweD8nJyeTm5l4x/YEHHsDj8ZCWlsY999zDE088QXp6ekC1uN1umpubA2or/U3oS7jq9Iv3CA0lJmZ4813BEavPMsIm5A0+5ny4nz8Evg70XOiN6DrgcDgGDTH45D6xIZcTM7z5BnLqw4/oCufY/yHYNsgyMjJoa2ujtraW8vLyftNaWlpYs2YNAEVFRTguO0YeP348a9as4bbbbiMpKYndu3ezbt069uzZQ01NDU6nM6BaJDgSJky86vRzQ1w7SXFaGzCvF85d5YkQgy3H573A5MmTh6hSQiklefDDjaE+fxj5OjA2Libi60Bv33niYgd+zMj5zqu3HZdohZjPCz1Xmfdqy5l0TRp93vHDqHT4RrKdtO1Dg1euXMlTTz1FdnY2r732GgUF1rNVqqurWbZsGUePHuXChQt897vfHfKpHi+99BL33HMPzzzzDN/85jfDUb4MorcPHvl14EOgH7/X2gs/8zE8/oL/7adNgpWfDaxvCY6GD+Gp1wJvP9J1YNFsWFwSeP/BsOtfoOtMYG1vfdg6Eutuhzc3+99+bCLc9p3A+g4V2w6/r6ysJC0tjaamJmbPns3cuXPJz8+nrKyMadOmceeddwLDuz529913k5iYGPD5WwmeuNjIjhjL1vMWI25yKkRynEFWFHyLeMq1o7Pvwdg2yLKysti5cyeLFy/G6XTS2NhIamoqmzdvZtu2bRw6dAjwb6CHI5qG6Yxi2RHckESyb7E4x8CkCD6/IBrWgeQIhkkk+x6Mba+RAcyaNYutW7de8XpHRweNjY3ExMQwZ86cIZfz4osv0tnZSVmZRqtFg9mTYXcQvgnaX7ExMMOgr3+3s9mTI3NjcuaE6HjWYvo0OPKnyPUdbWwdZIPZt28fPp+PgoICEhL6D1t68MEHmTZtGjfccMOlwR5PPvkkJSUlfOUrX4lQxXK5wutgYgK0fRzefouzIdn/sT4SArfkw476yPQbDSdmktKtb28O91M2UjJgfBTuzNn21OLV7N1rPTZ7oNOKs2fP5oUXXuDrX/86FRUVPPPMM3zrW9/ijTfeYOzYKPsyolEqJgbm54e/31ui9LuYRqP0ZJgZ5g3quDgovfJOnYjJKolAn6G95TZgo/KI7GpB9uijj/Loo4+GuyTx02dmwFtHoG2IocbBUpRtjViU6LHkejjktr4vLhwqiqzrc9HimnwYP9n6BudwSL4WMgrD05e/dEQmRnKOga+G6QliCWPh/huj45SSfGLyRPjs3PD0lTsJbpsRnr6GyxEDhZ+DmDAcjjhioPAuuMp92BE1Ko/ILj6HUcxWkAG3z4Q/Hhh+m4s3uQ7nxtmLvnwTJPvxbcISPotmQ/1JOO4Zfht/14H4sfC1m61T2tEmMRXyb4eDrw+/zcUbnYe6cfpy02+F5Cg+I2HbG6JldPB64Ve74Z3G0Cz/vlLrNKZEr84e2PRaaL5oc2wcfOdO64gsmjW8Ccf2hGbZU+ZB/h3RfUZCQSbG83rhN9XBHZIf44D7y6A8L3jLlNDp7IGn3/DvyGwoiePgW3dATmCPWA0rnw8a37YCLZhyb4Zpt0R3iIGCTGyk7rgVaJ09I1vOdRPga+XR8QQHGb4+L7y6D/6wd+QDQIqyreuipp1SbmuC/b+HrhF+k/m4ZOv6W1pOUMoKOQWZ2Ep7N2yrs041Xujzr22SE24rgDsLrUdhiZma2+Cld+FAi/9tM8bDZ+fA9VOj/yhkMH3n4ehuaH4fev3cqYsdC9fNhenzIW5caOoLBQWZ2FJnD/z5KNQ2wskzgz9keFwcTE2Hm6ZbNzwrwOzjo3bYdQj2NV/9y1iTnZB3rXVvYt415gbYp/WdB/dBaPnA+lZnb+/A88XEQfI1kFloDa+PM/B2WQWZ2F5vnxVmp85ZR2kOrOH7mRNhUrJ1PUzsres8uNqs+w57+6zHjSWOs04fj4+3T3gNxuuFj09Dx2nouwD4IHYMJKZbIx+jdVj9cCnIRETEaFF4Z4SIiMjwKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMZqCTEREjKYgExERoynIRETEaAoyERExmoJMRESMpiATERGjKchERMRoCjIRETGagkxERIymIBMREaMpyERExGgKMhERMdr/B6Ec2X7W6GUmAAAAAElFTkSuQmCC", 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" ] }, - "execution_count": 28, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "and_all.export('qiskit').draw('mpl')" + "circuit = sum_two_numbers.export('qiskit')\n", + "circuit.draw('mpl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We now can use our quantum function as an oracle for a Grover search. For instance, we want to find the input value that yeld to a `True` value of the function:" + "The qlassf function can be also exported as a gate, if the destination framwork supports it. We can use `encode_input` and `decode_output` in order to conver from/to high level types of qlasskit without worrying about the binary representation." ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "from qlasskit.algorithms import Grover\n", - "\n", - "q_algo = Grover(and_all, True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Qlasskit prepares the quantum circuit for the Grover search:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] }, - "execution_count": 32, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "qc = q_algo.export('qiskit')\n", - "qc.draw('mpl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use our prefered framework and simulator for sampling the result; this is an example using `qiskit` with `aer_simulator`.\n", + "from qiskit import QuantumCircuit\n", "\n", - "The `Grover` class, along with all circuit wrappers in qlasskit, provides utilities to encode inputs and decode outputs from a quantum circuit using the high level type definition. In the output histogram, it's now evident that the input leading to a `True` result in the `and_all` function is a list where all elements are set to `True`, aligning with our expectations.\n" + "qc = QuantumCircuit(sum_two_numbers.num_qubits,len(sum_two_numbers.output_qubits))\n", + "\n", + "qc.initialize(sum_two_numbers.encode_input(Qint2(1), Qint2(2)), sum_two_numbers.input_qubits)\n", + "qc.append(sum_two_numbers.gate('qiskit'), sum_two_numbers.qubits)\n", + "qc.measure(sum_two_numbers.output_qubits, range(len(sum_two_numbers.output_qubits)))\n", + "qc.draw('mpl')" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, - "execution_count": 35, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -136,34 +111,24 @@ "from qiskit import Aer, QuantumCircuit, transpile\n", "from qiskit.visualization import plot_histogram\n", "\n", - "qc.measure_all()\n", "simulator = Aer.get_backend(\"aer_simulator\")\n", "circ = transpile(qc, simulator)\n", "result = simulator.run(circ).result()\n", "counts = result.get_counts(circ)\n", "\n", - "counts_readable = q_algo.decode_counts(counts)\n", + "counts_readable = sum_two_numbers.decode_counts(counts)\n", "plot_histogram(counts_readable)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { - "kernelspec": { - "display_name": "qlasskit_310-env", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" + "name": "python" } }, "nbformat": 4, diff --git a/qlasskit/qlassfun.py b/qlasskit/qlassfun.py index c06f7bcc..be4e75d7 100644 --- a/qlasskit/qlassfun.py +++ b/qlasskit/qlassfun.py @@ -99,13 +99,13 @@ def val_to_bin(argt, val): vl = "" for arg, val in zip(self.args, qvals): vl += val_to_bin(arg.ttype, val) - return vl + return vl[::-1] # TODO: we need an endianess paramter # @ovveride def decode_output( self, istr: Union[str, int, List[bool]] ) -> Union[bool, Tuple, Qtype]: - fcome = format_outcome(istr) + fcome = format_outcome(istr)[::-1] # TODO: we need an endianess paramter return interpret_as_qtype(fcome[::-1], self.returns.ttype, len(self.returns)) def __add__(self, qf2) -> "QlassF": diff --git a/test/test_algo_grover.py b/test/test_algo_grover.py index 47cff3ad..a8de76f2 100644 --- a/test/test_algo_grover.py +++ b/test/test_algo_grover.py @@ -59,7 +59,7 @@ def hash(k: Qint4) -> bool: self.assertEqual(15 in counts_readable, True) self.assertEqual(algo.output_qubits, [0, 1, 2, 3]) self.assertEqual(counts_readable[7] > 600, True) - + def test_grover_without_element_to_search(self): f = """ def hash(k: Qint4) -> bool: diff --git a/test/test_qlassf.py b/test/test_qlassf.py index 3415d484..a20c128d 100644 --- a/test/test_qlassf.py +++ b/test/test_qlassf.py @@ -73,8 +73,8 @@ def test_encode_decode_bool(self): def test_encode_decode_qint(self): f = "def test(a: Qint2) -> Qint2:\n\treturn a" qf = qlassf(f, to_compile=False) - self.assertEqual(qf.encode_input(Qint2(2)), "01") - self.assertEqual(qf.decode_output("01"), Qint2(2)) + self.assertEqual(qf.encode_input(Qint2(2)), "01"[::-1]) + self.assertEqual(qf.decode_output("01"[::-1]), Qint2(2)) self.assertEqual(qf.encode_input(Qint2(0)), "00") self.assertEqual(qf.decode_output("00"), Qint2(0)) @@ -85,17 +85,17 @@ def test_encode_decode_tuple(self): self.assertEqual(qf.encode_input((Qint2(2), False)), "010") self.assertEqual(qf.decode_output("010"), (Qint2(2), False)) - self.assertEqual(qf.encode_input((Qint2(0), True)), "001") - self.assertEqual(qf.decode_output("001"), (Qint2(0), True)) + self.assertEqual(qf.encode_input((Qint2(0), True)), "001"[::-1]) + self.assertEqual(qf.decode_output("001"[::-1]), (Qint2(0), True)) def test_encode_decode_tuple2(self): f = "def test(a: Tuple[Qint2, Qint4]) -> Tuple[Qint2, Qint4]:\n\treturn a" qf = qlassf(f, to_compile=False) - self.assertEqual(qf.encode_input((Qint2(2), Qint4(3))), "011100") - self.assertEqual(qf.decode_output("011100"), (Qint2(2), Qint4(3))) + self.assertEqual(qf.encode_input((Qint2(2), Qint4(3))), "011100"[::-1]) + self.assertEqual(qf.decode_output("011100"[::-1]), (Qint2(2), Qint4(3))) - self.assertEqual(qf.encode_input((Qint2(0), Qint4(2))), "000100") - self.assertEqual(qf.decode_output("000100"), (Qint2(0), Qint4(2))) + self.assertEqual(qf.encode_input((Qint2(0), Qint4(2))), "000100"[::-1]) + self.assertEqual(qf.decode_output("000100"[::-1]), (Qint2(0), Qint4(2))) class TestQlassfTruthTable(unittest.TestCase):