From 1edc01ddd6631ca80899d9143efa09f44d25e9a5 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 07:28:20 +0100 Subject: [PATCH 01/22] build: Temporary switch to qibojit branch for py3.12 support Required for the quasi-circular dependency --- pyproject.toml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e06f5fc085..19f426e647 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ packages = [{ include = "qibo", from = "src" }] include = ["*.out", "*.yml"] [tool.poetry.dependencies] -python = ">=3.9,<3.12" +python = ">=3.9,<3.13" scipy = "^1.10.1" sympy = "^1.11.1" cma = "^3.3.0" @@ -32,7 +32,7 @@ openqasm3 = { version = ">=0.5.0", extras = ["parser"] } numpy = "^1.26.4" networkx = "^3.2.1" cvxpy = { version = "^1.4.2", optional = true } -tensorflow = { version = "^2.14.1,<2.16", markers = "sys_platform == 'linux' or sys_platform == 'darwin'", optional = true } +tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux' or sys_platform == 'darwin'", optional = true } torch = { version = "^2.1.1", optional = true } [tool.poetry.group.dev] @@ -67,9 +67,10 @@ scikit-learn = "^1.2.1" pytest-cov = "^4.0.0" pylint = "^3.0.3" matplotlib = "^3.7.0" -tensorflow = { version = "^2.14.1,<2.16", markers = "sys_platform == 'linux'" } +tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux'" } torch = "^2.1.1" qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } stim = "^1.12.0" @@ -87,7 +88,7 @@ optional = true [tool.poetry.group.cuda11.dependencies] cupy-cuda11x = "^12.0.0" cuquantum-python-cu11 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.group.cuda12] @@ -96,7 +97,7 @@ optional = true [tool.poetry.group.cuda12.dependencies] cupy-cuda12x = "^12.0.0" cuquantum-python-cu12 = "^23.3.0" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } +qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } [tool.poetry.extras] From 1f10f29b47a4137907053c0d064c7f08a4e83006 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 09:26:00 +0100 Subject: [PATCH 02/22] build: Add explicit setuptools dependency, to compensate the missing one in hyperopt --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 19f426e647..d94b9208e7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,8 @@ sympy = "^1.11.1" cma = "^3.3.0" joblib = "^1.2.0" hyperopt = "^0.2.7" +# `setuptools` is only required because undeclared by `hyperopt` +setuptools = "^69.1.1" tabulate = "^0.9.0" openqasm3 = { version = ">=0.5.0", extras = ["parser"] } numpy = "^1.26.4" @@ -82,6 +84,7 @@ docs = "make -C doc html" docs-clean = "make -C doc clean" test-docs = "make -C doc doctest" + [tool.poetry.group.cuda11] optional = true @@ -91,6 +94,7 @@ cuquantum-python-cu11 = "^23.3.0" qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } qibotn = { git = "https://github.com/qiboteam/qibotn.git" } + [tool.poetry.group.cuda12] optional = true From c9b0ee51ce286e6a76b9571fd1e5924c16de06cf Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 09:59:56 +0100 Subject: [PATCH 03/22] chore: Move Flake to NixOS unstable, upgrade shell to py3.12 --- flake.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index 9730d4305f..c496d690b5 100644 --- a/flake.nix +++ b/flake.nix @@ -46,7 +46,7 @@ install.enable = true; install.groups = ["dev"]; }; - version = "3.11"; + version = "3.12"; }; } ]; From a685b1700db28c9a9c029175fc151e3fc9a7a243 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 10:46:57 +0100 Subject: [PATCH 04/22] ci: Extend workflows to py3.12 --- .github/workflows/deploy.yml | 2 +- .github/workflows/rules.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 1dd3d4a778..296254ee48 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -14,7 +14,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [ 3.9, '3.10', '3.11'] + python-version: [ 3.9, '3.10', '3.11', '3.12'] uses: qiboteam/workflows/.github/workflows/deploy-pip-poetry.yml@main with: os: ${{ matrix.os }} diff --git a/.github/workflows/rules.yml b/.github/workflows/rules.yml index c2e168fe2f..bf060add1c 100644 --- a/.github/workflows/rules.yml +++ b/.github/workflows/rules.yml @@ -14,7 +14,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [ 3.9, '3.10', '3.11'] + python-version: [ 3.9, '3.10', '3.11', '3.12'] uses: qiboteam/workflows/.github/workflows/rules-poetry.yml@main with: os: ${{ matrix.os }} From ad3b9fcf2a8da119878aa3bbd096b91e4846f0a8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 10:57:02 +0100 Subject: [PATCH 05/22] ci: Update Pylint version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d94b9208e7..a251645044 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,7 +67,7 @@ cvxpy = "^1.3.1" ply = "^3.11" scikit-learn = "^1.2.1" pytest-cov = "^4.0.0" -pylint = "^3.0.3" +pylint = "^3.1.0" matplotlib = "^3.7.0" tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux'" } torch = "^2.1.1" From f94c9b79be540f99fb6221f054170e1c5a2b49e5 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 12 Mar 2024 00:21:19 +0100 Subject: [PATCH 06/22] fix: Follow local nixpkgs ref --- flake.lock | 58 +++++++++++++++++++++--------------------------------- flake.nix | 7 +++++-- 2 files changed, 27 insertions(+), 38 deletions(-) diff --git a/flake.lock b/flake.lock index b189f6f11b..1da0dd09bc 100644 --- a/flake.lock +++ b/flake.lock @@ -8,11 +8,11 @@ "pre-commit-hooks": "pre-commit-hooks" }, "locked": { - "lastModified": 1696344641, - "narHash": "sha256-cfGsdtDvzYaFA7oGWSgcd1yST6LFwvjMcHvtVj56VcU=", + "lastModified": 1710144971, + "narHash": "sha256-CjTOdoBvT/4AQncTL20SDHyJNgsXZjtGbz62yDIUYnM=", "owner": "cachix", "repo": "devenv", - "rev": "05e26941f34486bff6ebeb4b9c169b6f637f1758", + "rev": "6c0bad0045f1e1802f769f7890f6a59504825f4d", "type": "github" }, "original": { @@ -40,11 +40,11 @@ "flake-compat_2": { "flake": false, "locked": { - "lastModified": 1673956053, - "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", "owner": "edolstra", "repo": "flake-compat", - "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", "type": "github" }, "original": { @@ -76,11 +76,11 @@ "systems": "systems_2" }, "locked": { - "lastModified": 1685518550, - "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=", + "lastModified": 1701680307, + "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", "owner": "numtide", "repo": "flake-utils", - "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef", + "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", "type": "github" }, "original": { @@ -170,14 +170,16 @@ "inputs": { "flake-compat": "flake-compat_2", "flake-utils": "flake-utils_2", - "nixpkgs": "nixpkgs_3" + "nixpkgs": [ + "nixpkgs" + ] }, "locked": { - "lastModified": 1696339147, - "narHash": "sha256-KWflgPy2EUGfYV29kv4YAySyKhbwrl+Ext5yEGvd690=", + "lastModified": 1709875392, + "narHash": "sha256-ZC/8TNR2q8Q+j4vzaW3B8jLS9ZDvss61brFW4VtWr0A=", "owner": "cachix", "repo": "nixpkgs-python", - "rev": "5ebf0112541b91b926c8d4cedecea4d771c417ec", + "rev": "7296d316153575b8db614ff02dac5b7501a92071", "type": "github" }, "original": { @@ -220,32 +222,16 @@ }, "nixpkgs_2": { "locked": { - "lastModified": 1709703039, - "narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=", + "lastModified": 1710021367, + "narHash": "sha256-FuMVdWqXMT38u1lcySYyv93A7B8wU0EGzUr4t4jQu8g=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d", + "rev": "b94a96839afcc56de3551aa7472b8d9a3e77e05d", "type": "github" }, "original": { "owner": "NixOS", - "ref": "nixos-unstable", - "repo": "nixpkgs", - "type": "github" - } - }, - "nixpkgs_3": { - "locked": { - "lastModified": 1685974512, - "narHash": "sha256-WLPHpe96RbPRO9iDtCxgsYkadTheRq7wqXWdGpR6g7w=", - "owner": "domenkozar", - "repo": "nixpkgs", - "rev": "1102477695918daba466123cc2ef694ed3a49939", - "type": "github" - }, - "original": { - "owner": "domenkozar", - "ref": "cpython-moduralize", + "ref": "nixos-23.11", "repo": "nixpkgs", "type": "github" } @@ -265,11 +251,11 @@ "nixpkgs-stable": "nixpkgs-stable" }, "locked": { - "lastModified": 1688056373, - "narHash": "sha256-2+SDlNRTKsgo3LBRiMUcoEUb6sDViRNQhzJquZ4koOI=", + "lastModified": 1704725188, + "narHash": "sha256-qq8NbkhRZF1vVYQFt1s8Mbgo8knj+83+QlL5LBnYGpI=", "owner": "cachix", "repo": "pre-commit-hooks.nix", - "rev": "5843cf069272d92b60c3ed9e55b7a8989c01d4c7", + "rev": "ea96f0c05924341c551a797aaba8126334c505d2", "type": "github" }, "original": { diff --git a/flake.nix b/flake.nix index c496d690b5..2634f152bd 100644 --- a/flake.nix +++ b/flake.nix @@ -1,9 +1,12 @@ { inputs = { - nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; + nixpkgs.url = "github:NixOS/nixpkgs/nixos-23.11"; systems.url = "github:nix-systems/default"; devenv.url = "github:cachix/devenv"; - nixpkgs-python.url = "github:cachix/nixpkgs-python"; + nixpkgs-python = { + url = "github:cachix/nixpkgs-python"; + inputs.nixpkgs.follows = "nixpkgs"; + }; }; outputs = { From 75612a6d2b5128713c8c9915006f740797e2f424 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 12 Mar 2024 00:34:33 +0100 Subject: [PATCH 07/22] build: Sync all branches for py3.12 --- pyproject.toml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index a251645044..345dfe9ae2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,11 +69,10 @@ scikit-learn = "^1.2.1" pytest-cov = "^4.0.0" pylint = "^3.1.0" matplotlib = "^3.7.0" +qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } +qibotn = { git = "https://github.com/qiboteam/qibotn.git", branch = "py3.12" } tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux'" } torch = "^2.1.1" -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } -qibojit = { git = "https://github.com/qiboteam/qibojit.git" } -qibotn = { git = "https://github.com/qiboteam/qibotn.git" } stim = "^1.12.0" [tool.poe.tasks] @@ -92,7 +91,7 @@ optional = true cupy-cuda11x = "^12.0.0" cuquantum-python-cu11 = "^23.3.0" qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } -qibotn = { git = "https://github.com/qiboteam/qibotn.git" } +qibotn = { git = "https://github.com/qiboteam/qibotn.git", branch = "py3.12" } [tool.poetry.group.cuda12] @@ -102,7 +101,7 @@ optional = true cupy-cuda12x = "^12.0.0" cuquantum-python-cu12 = "^23.3.0" qibojit = { git = "https://github.com/qiboteam/qibojit.git", branch = "py3.12" } -qibotn = { git = "https://github.com/qiboteam/qibotn.git" } +qibotn = { git = "https://github.com/qiboteam/qibotn.git", branch = "py3.12" } [tool.poetry.extras] tensorflow = ["tensorflow"] From 3c45b3225ef187ae36699c2044b323f3a2082cf4 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 14 Mar 2024 11:17:52 +0100 Subject: [PATCH 08/22] build: Upgrade Nix shell to use Poetry 1.8 --- flake.lock | 8 ++++---- flake.nix | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/flake.lock b/flake.lock index 1da0dd09bc..39d808d920 100644 --- a/flake.lock +++ b/flake.lock @@ -222,16 +222,16 @@ }, "nixpkgs_2": { "locked": { - "lastModified": 1710021367, - "narHash": "sha256-FuMVdWqXMT38u1lcySYyv93A7B8wU0EGzUr4t4jQu8g=", + "lastModified": 1710272261, + "narHash": "sha256-g0bDwXFmTE7uGDOs9HcJsfLFhH7fOsASbAuOzDC+fhQ=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b94a96839afcc56de3551aa7472b8d9a3e77e05d", + "rev": "0ad13a6833440b8e238947e47bea7f11071dc2b2", "type": "github" }, "original": { "owner": "NixOS", - "ref": "nixos-23.11", + "ref": "nixos-unstable", "repo": "nixpkgs", "type": "github" } diff --git a/flake.nix b/flake.nix index 2634f152bd..9aea3a4aa8 100644 --- a/flake.nix +++ b/flake.nix @@ -1,6 +1,6 @@ { inputs = { - nixpkgs.url = "github:NixOS/nixpkgs/nixos-23.11"; + nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; systems.url = "github:nix-systems/default"; devenv.url = "github:cachix/devenv"; nixpkgs-python = { From 203c79a014d59c8b83d2cd694c5931afe2104b86 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 14 Mar 2024 17:03:37 +0100 Subject: [PATCH 09/22] fix: Attempt fix reducing complex size --- src/qibo/models/qgan.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibo/models/qgan.py b/src/qibo/models/qgan.py index c14b5caf19..b209e0f584 100644 --- a/src/qibo/models/qgan.py +++ b/src/qibo/models/qgan.py @@ -259,11 +259,11 @@ def generate_real_samples(samples, distribution, real_samples): # determine half the size of one batch, for updating the discriminator half_samples = int(self.batch_samples / 2) if self.initial_params is not None: - initial_params = tf.Variable(self.initial_params, dtype=tf.complex128) + initial_params = tf.Variable(self.initial_params, dtype=tf.complex64) else: n = 10 * self.layers * self.nqubits + 2 * self.nqubits initial_params = tf.Variable( - np.random.uniform(-0.15, 0.15, n), dtype=tf.complex128 + np.random.uniform(-0.15, 0.15, n), dtype=tf.complex64 ) optimizer = tf.optimizers.Adadelta( # pylint: disable=no-member From 5375e353f7e84b537b759e11974ffb02f8a54891 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 14 Mar 2024 17:59:19 +0100 Subject: [PATCH 10/22] fix: Temporarily drop cvxpy All code is kept, the only test making use of it is skipped --- pyproject.toml | 5 +++-- tests/test_quantum_info_metrics.py | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 345dfe9ae2..43764897d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,6 @@ tabulate = "^0.9.0" openqasm3 = { version = ">=0.5.0", extras = ["parser"] } numpy = "^1.26.4" networkx = "^3.2.1" -cvxpy = { version = "^1.4.2", optional = true } tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux' or sys_platform == 'darwin'", optional = true } torch = { version = "^2.1.1", optional = true } @@ -63,7 +62,6 @@ optional = true [tool.poetry.group.tests.dependencies] pytest = "^7.2.1" cirq = "^1.1.0" -cvxpy = "^1.3.1" ply = "^3.11" scikit-learn = "^1.2.1" pytest-cov = "^4.0.0" @@ -108,6 +106,9 @@ tensorflow = ["tensorflow"] torch = ["torch"] qinfo = ["cvxpy", "stim"] +[tool.pylint.main] +ignored-modules = ["cvxpy"] + [tool.pylint.reports] output-format = "colorized" diff --git a/tests/test_quantum_info_metrics.py b/tests/test_quantum_info_metrics.py index 793bb510a5..ee0dda211f 100644 --- a/tests/test_quantum_info_metrics.py +++ b/tests/test_quantum_info_metrics.py @@ -311,6 +311,7 @@ def test_process_fidelity_and_infidelity(backend): ) +@pytest.mark.skip @pytest.mark.parametrize("nqubits", [1, 2]) def test_diamond_norm(backend, nqubits): with pytest.raises(TypeError): From f9d53c48df0261554b115019b38088d123ee739e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 14 Mar 2024 18:14:19 +0100 Subject: [PATCH 11/22] chore: Poetry lock --- poetry.lock | 1138 ++++++++++++++++++++------------------------------- 1 file changed, 447 insertions(+), 691 deletions(-) diff --git a/poetry.lock b/poetry.lock index ab3cc99ef6..3860db1b0f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -523,27 +523,6 @@ files = [ [package.dependencies] cirq-core = "1.3.0" -[[package]] -name = "clarabel" -version = "0.7.1" -description = "Clarabel Conic Interior Point Solver for Rust / Python" -optional = false -python-versions = ">=3.7" -files = [ - {file = "clarabel-0.7.1-cp37-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:32980b578842d17abe3d277df2d31a6b8bff3facf33b169c74d59b2ada10b61b"}, - {file = "clarabel-0.7.1-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:b4d414b9db040effa186f9e048de2c7d3e38b6edcadaaa2432a52b6451ba9999"}, - {file = "clarabel-0.7.1-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec0fde90a467e07010176b0bd159ab8cb11c59c8ff0a09580af1ac77d7861971"}, - {file = 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else: n = 10 * self.layers * self.nqubits + 2 * self.nqubits initial_params = tf.Variable( - np.random.uniform(-0.15, 0.15, n), dtype=tf.complex64 + np.random.uniform(-0.15, 0.15, n), dtype=tf.complex128 ) optimizer = tf.optimizers.Adadelta( # pylint: disable=no-member From 31a5a3edb61945f0b84f54e851538a15c672eef0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 11:59:37 +0200 Subject: [PATCH 13/22] feat!: Drop the QGAN model and related tests and docs --- doc/source/api-reference/qibo.rst | 8 - src/qibo/models/__init__.py | 1 - src/qibo/models/qgan.py | 417 ------------------------------ tests/conftest.py | 9 +- tests/test_models_qgan.py | 142 ---------- 5 files changed, 1 insertion(+), 576 deletions(-) delete mode 100644 src/qibo/models/qgan.py delete mode 100644 tests/test_models_qgan.py diff --git a/doc/source/api-reference/qibo.rst b/doc/source/api-reference/qibo.rst index 29d059ee29..c6b44d2645 100644 --- a/doc/source/api-reference/qibo.rst +++ b/doc/source/api-reference/qibo.rst @@ -167,14 +167,6 @@ Feedback-based Algorithm for Quantum Optimization (FALQON) :member-order: bysource -Style-based Quantum Generative Adversarial Network (style-qGAN) -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -.. autoclass:: qibo.models.qgan.StyleQGAN - :members: - :member-order: bysource - - Grover's Algorithm """""""""""""""""" diff --git a/src/qibo/models/__init__.py b/src/qibo/models/__init__.py index 71963b9b50..e9c2abb92c 100644 --- a/src/qibo/models/__init__.py +++ b/src/qibo/models/__init__.py @@ -11,5 +11,4 @@ from qibo.models.evolution import AdiabaticEvolution, StateEvolution from qibo.models.grover import Grover from qibo.models.qft import QFT -from qibo.models.qgan import StyleQGAN from qibo.models.variational import AAVQE, FALQON, QAOA, VQE diff --git a/src/qibo/models/qgan.py b/src/qibo/models/qgan.py deleted file mode 100644 index c14b5caf19..0000000000 --- a/src/qibo/models/qgan.py +++ /dev/null @@ -1,417 +0,0 @@ -import numpy as np -from numpy.random import randn - -from qibo import gates, hamiltonians, models -from qibo.backends import matrices -from qibo.config import raise_error - - -class StyleQGAN: - """Model that implements and trains a style-based quantum generative adversarial network. - - For original manuscript: `arXiv:2110.06933 `_ - - Args: - latent_dim (int): number of latent dimensions. - layers (int): number of layers for the quantum generator. Provide this value only if not using - a custom quantum generator. - circuit (:class:`qibo.models.circuit.Circuit`): custom quantum generator circuit. If not provided, - the default quantum circuit will be used. - set_parameters (function): function that creates the array of parameters for the quantum generator. - If not provided, the default function will be used. - - Example: - .. testcode:: - - import numpy as np - import qibo - from qibo.models.qgan import StyleQGAN - # set qibo backend to tensorflow which supports gradient descent training - qibo.set_backend("tensorflow") - # Create reference distribution. - # Example: 3D correlated Gaussian distribution normalized between [-1,1] - reference_distribution = [] - samples = 10 - mean = [0, 0, 0] - cov = [[0.5, 0.1, 0.25], [0.1, 0.5, 0.1], [0.25, 0.1, 0.5]] - x, y, z = np.random.multivariate_normal(mean, cov, samples).T/4 - s1 = np.reshape(x, (samples,1)) - s2 = np.reshape(y, (samples,1)) - s3 = np.reshape(z, (samples,1)) - reference_distribution = np.hstack((s1,s2,s3)) - # Train qGAN with your particular setup - train_qGAN = StyleQGAN(latent_dim=1, layers=2) - train_qGAN.fit(reference_distribution, n_epochs=1) - """ - - def __init__( - self, - latent_dim, - layers=None, - circuit=None, - set_parameters=None, - discriminator=None, - ): - # qgan works only with tensorflow - from qibo.backends import TensorflowBackend - - self.backend = TensorflowBackend() - - if layers is not None and circuit is not None: - raise_error( - ValueError, - "Set the number of layers for the default quantum generator " - "or use a custom quantum generator, do not define both.", - ) - elif layers is None and circuit is None: - raise_error( - ValueError, - "Set the number of layers for the default quantum generator " - "or use a custom quantum generator.", - ) - - if set_parameters is None and circuit is not None: - raise_error( - ValueError, - "Set parameters function has to be given for your custom quantum generator.", - ) - elif set_parameters is not None and circuit is None: - raise_error( - ValueError, - "Define the custom quantum generator to use custom set parameters function.", - ) - - self.discriminator = discriminator - self.circuit = circuit - self.layers = layers - self.latent_dim = latent_dim - if set_parameters is not None: - self.set_parameters = set_parameters - else: - self.set_parameters = self.set_params - - def define_discriminator(self, alpha=0.2, dropout=0.2): - """Define the standalone discriminator model.""" - from tensorflow.keras.layers import ( # pylint: disable=E0611,import-error - Conv2D, - Dense, - Dropout, - Flatten, - LeakyReLU, - Reshape, - ) - from tensorflow.keras.models import ( # pylint: disable=E0611,import-error - Sequential, - ) - from tensorflow.keras.optimizers import ( # pylint: disable=E0611,import-error - Adadelta, - ) - - model = Sequential() - model.add(Dense(200, use_bias=False, input_dim=self.nqubits)) - model.add(Reshape((10, 10, 2))) - model.add( - Conv2D( - 64, - kernel_size=3, - strides=1, - padding="same", - kernel_initializer="glorot_normal", - ) - ) - model.add(LeakyReLU(alpha=alpha)) - model.add( - Conv2D( - 32, - kernel_size=3, - strides=1, - padding="same", - kernel_initializer="glorot_normal", - ) - ) - model.add(LeakyReLU(alpha=alpha)) - model.add( - Conv2D( - 16, - kernel_size=3, - strides=1, - padding="same", - kernel_initializer="glorot_normal", - ) - ) - model.add(LeakyReLU(alpha=alpha)) - model.add( - Conv2D( - 8, - kernel_size=3, - strides=1, - padding="same", - kernel_initializer="glorot_normal", - ) - ) - model.add(Flatten()) - model.add(LeakyReLU(alpha=alpha)) - model.add(Dropout(dropout)) - model.add(Dense(1, activation="sigmoid")) - - # compile model - opt = Adadelta(learning_rate=0.1) - model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"]) - return model - - def set_params(self, circuit, params, x_input, i): - """Set the parameters for the quantum generator circuit.""" - p = [] - index = 0 - noise = 0 - for l in range(self.layers): - for q in range(self.nqubits): - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - for i in range(0, self.nqubits - 1): - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - for q in range(self.nqubits): - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % self.latent_dim - circuit.set_parameters(p) - - def generate_latent_points(self, samples): - """Generate points in latent space as input for the quantum generator.""" - # generate points in the latent space - x_input = randn(self.latent_dim * samples) - # reshape into a batch of inputs for the network - x_input = x_input.reshape(samples, self.latent_dim) - return x_input - - def generate_fake_samples(self, params, samples, circuit, hamiltonians_list): - import tensorflow as tf # pylint: disable=import-error - - """Use the generator to generate fake examples, with class labels.""" - # generate points in latent space - x_input = self.generate_latent_points(samples) - x_input = np.transpose(x_input) - # generator outputs - X = [] - for i in range(self.nqubits): - X.append([]) - # quantum generator circuit - for i in range(samples): - self.set_parameters(circuit, params, x_input, i) - final_state = self.backend.execute_circuit(circuit)._state - for ii in range(self.nqubits): - X[ii].append(hamiltonians_list[ii].expectation(final_state)) - # shape array - X = tf.stack([X[i] for i in range(len(X))], axis=1) - # create class labels - y = np.zeros((samples, 1)) - return X, y - - def define_cost_gan( - self, params, discriminator, samples, circuit, hamiltonians_list - ): - import tensorflow as tf # pylint: disable=import-error - - """Define the combined generator and discriminator model, for updating the generator.""" - # generate fake samples - x_fake, y_fake = self.generate_fake_samples( - params, samples, circuit, hamiltonians_list - ) - # create inverted labels for the fake samples - y_fake = np.ones((samples, 1)) - # evaluate discriminator on fake examples - disc_output = discriminator(x_fake) - loss = tf.keras.losses.binary_crossentropy( # pylint: disable=no-member - y_fake, disc_output - ) - loss = tf.reduce_mean(loss) - return loss - - def train(self, d_model, circuit, hamiltonians_list, save=True): - """Train the quantum generator and classical discriminator.""" - import tensorflow as tf # pylint: disable=import-error - - def generate_real_samples(samples, distribution, real_samples): - """Generate real samples with class labels.""" - # generate samples from the distribution - idx = np.random.randint(real_samples, size=samples) - X = distribution[idx, :] - # generate class labels - y = np.ones((samples, 1)) - return X, y - - d_loss = [] - g_loss = [] - # determine half the size of one batch, for updating the discriminator - half_samples = int(self.batch_samples / 2) - if self.initial_params is not None: - initial_params = tf.Variable(self.initial_params, dtype=tf.complex128) - else: - n = 10 * self.layers * self.nqubits + 2 * self.nqubits - initial_params = tf.Variable( - np.random.uniform(-0.15, 0.15, n), dtype=tf.complex128 - ) - - optimizer = tf.optimizers.Adadelta( # pylint: disable=no-member - learning_rate=self.lr - ) - # prepare real samples - s = self.reference - # manually enumerate epochs - for i in range(self.n_epochs): - # prepare real samples - x_real, y_real = generate_real_samples( - half_samples, s, self.training_samples - ) - # prepare fake examples - x_fake, y_fake = self.generate_fake_samples( - initial_params, half_samples, circuit, hamiltonians_list - ) - # update discriminator - d_loss_real, _ = d_model.train_on_batch(x_real, y_real) - d_loss_fake, _ = d_model.train_on_batch(x_fake, y_fake) - d_loss.append((d_loss_real + d_loss_fake) / 2) - # update generator - with tf.GradientTape() as tape: - loss = self.define_cost_gan( - initial_params, - d_model, - self.batch_samples, - circuit, - hamiltonians_list, - ) - grads = tape.gradient(loss, initial_params) - optimizer.apply_gradients([(grads, initial_params)]) - g_loss.append(loss) - if save: # pragma: no cover - # saving is skipped in tests to avoid creating files - params = ( - self.nqubits, - self.latent_dim, - self.layers, - self.training_samples, - self.batch_samples, - self.lr, - ) - filename = "_".join(str(p) for p in params) - np.savetxt(f"PARAMS_{filename}", [initial_params.numpy()], newline="") - np.savetxt(f"dloss_{filename}", [d_loss], newline="") - np.savetxt(f"gloss_{filename}", [g_loss], newline="") - # serialize weights to HDF5 - d_model.save_weights(f"discriminator_{filename}.h5") - - def fit( - self, - reference, - initial_params=None, - batch_samples=128, - n_epochs=20000, - lr=0.5, - save=True, - ): - """Execute qGAN training. - - Args: - reference (array): samples from the reference input distribution. - initial_parameters (array): initial parameters for the quantum generator. If not provided, - the default initial parameters will be used. - discriminator (:class:`tensorflow.keras.models`): custom classical discriminator. If not provided, - the default classical discriminator will be used. - batch_samples (int): number of training examples utilized in one iteration. - n_epochs (int): number of training iterations. - lr (float): initial learning rate for the quantum generator. - It controls how much to change the model each time the weights are updated. - save (bool): If ``True`` the results of training (trained parameters and losses) - will be saved on disk. Default is ``True``. - """ - if initial_params is None and self.circuit is not None: - raise_error( - ValueError, - "Set the initial parameters for your custom quantum generator.", - ) - elif initial_params is not None and self.circuit is None: - raise_error( - ValueError, - "Define the custom quantum generator to use custom initial parameters.", - ) - - self.reference = reference - self.nqubits = reference.shape[1] - self.training_samples = reference.shape[0] - self.initial_params = initial_params - self.batch_samples = batch_samples - self.n_epochs = n_epochs - self.lr = lr - - # create classical discriminator - if self.discriminator is None: - discriminator = self.define_discriminator() - else: - discriminator = self.discriminator - - if discriminator.input_shape[1] is not self.nqubits: - raise_error( - ValueError, - "The number of input neurons in the discriminator has to be equal to " - "the number of qubits in the circuit (dimension of the input reference distribution).", - ) - - # create quantum generator - if self.circuit is None: - circuit = models.Circuit(self.nqubits) - for l in range(self.layers): - for q in range(self.nqubits): - circuit.add(gates.RY(q, 0)) - circuit.add(gates.RZ(q, 0)) - circuit.add(gates.RY(q, 0)) - circuit.add(gates.RZ(q, 0)) - for i in range(0, self.nqubits - 1): - circuit.add(gates.CRY(i, i + 1, 0)) - circuit.add(gates.CRY(self.nqubits - 1, 0, 0)) - for q in range(self.nqubits): - circuit.add(gates.RY(q, 0)) - else: - circuit = self.circuit - - if circuit.nqubits != self.nqubits: - raise_error( - ValueError, - "The number of qubits in the circuit has to be equal to " - "the number of dimensions in the reference distribution.", - ) - - # define hamiltonian to generate fake samples - def hamiltonian(nqubits, position): - kron = [] - for i in range(nqubits): - if i == position: - kron.append(matrices.Z) - else: - kron.append(matrices.I) - for i in range(nqubits - 1): - if i == 0: - ham = np.kron(kron[i + 1], kron[i]) - else: - ham = np.kron(kron[i + 1], ham) - return hamiltonians.Hamiltonian(nqubits, ham, backend=self.backend) - - hamiltonians_list = [] - for i in range(self.nqubits): - hamiltonians_list.append(hamiltonian(self.nqubits, i)) - - # train model - self.train(discriminator, circuit, hamiltonians_list, save) diff --git a/tests/conftest.py b/tests/conftest.py index f320321576..862983358e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,4 @@ -""" -conftest.py +"""conftest.py. Pytest fixtures. """ @@ -69,12 +68,6 @@ def backend(backend_name): def pytest_generate_tests(metafunc): module_name = metafunc.module.__name__ - if ( - module_name == "tests.test_models_qgan" - and "tensorflow" not in AVAILABLE_BACKENDS - ): # pragma: no cover - pytest.skip("Skipping QGAN tests because tensorflow is not available.") - if module_name == "tests.test_models_distcircuit_execution": config = [(bk, acc) for acc in ACCELERATORS for bk in MULTIGPU_BACKENDS] metafunc.parametrize("backend_name,accelerators", config) diff --git a/tests/test_models_qgan.py b/tests/test_models_qgan.py deleted file mode 100644 index 429137d2fc..0000000000 --- a/tests/test_models_qgan.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Test style-qGAN model defined in `qibo/models/qgan.py`.""" - -import sys - -import numpy as np -import pytest - -from qibo import gates, models - - -def generate_distribution(samples): - mean = [0, 0, 0] - cov = [[0.5, 0.1, 0.25], [0.1, 0.5, 0.1], [0.25, 0.1, 0.5]] - x, y, z = np.random.multivariate_normal(mean, cov, samples).T / 4.0 - s1 = np.reshape(x, (samples, 1)) - s2 = np.reshape(y, (samples, 1)) - s3 = np.reshape(z, (samples, 1)) - return np.hstack((s1, s2, s3)) - - -def test_default_qgan(): - reference_distribution = generate_distribution(10) - qgan = models.StyleQGAN(latent_dim=2, layers=1) - qgan.fit(reference_distribution, n_epochs=1, save=False) - assert qgan.layers == 1 - assert qgan.latent_dim == 2 - assert qgan.batch_samples == 128 - assert qgan.n_epochs == 1 - assert qgan.lr == 0.5 - - -def test_custom_qgan(): - def set_params(circuit, params, x_input, i): - """Set the parameters for the quantum generator circuit.""" - p = [] - index = 0 - noise = 0 - for l in range(1): - for q in range(3): - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % 2 - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % 2 - for q in range(3): - p.append(params[index] * x_input[noise][i] + params[index + 1]) - index += 2 - noise = (noise + 1) % 2 - circuit.set_parameters(p) - - nqubits = 3 - nlayers = 1 - reference_distribution = generate_distribution(10) - circuit = models.Circuit(nqubits) - for l in range(nlayers): - for q in range(nqubits): - circuit.add(gates.RY(q, 0)) - circuit.add(gates.RZ(q, 0)) - for i in range(0, nqubits - 1): - circuit.add(gates.CZ(i, i + 1)) - circuit.add(gates.CZ(nqubits - 1, 0)) - for q in range(nqubits): - circuit.add(gates.RY(q, 0)) - - initial_params = np.random.uniform(-0.15, 0.15, 18) - qgan = models.StyleQGAN(latent_dim=2, circuit=circuit, set_parameters=set_params) - qgan.fit( - reference_distribution, initial_params=initial_params, n_epochs=1, save=False - ) - assert qgan.latent_dim == 2 - assert qgan.batch_samples == 128 - assert qgan.n_epochs == 1 - assert qgan.lr == 0.5 - - -def test_qgan_errors(): - with pytest.raises(ValueError): - qgan = models.StyleQGAN(latent_dim=2) - circuit = models.Circuit(2) - with pytest.raises(ValueError): - qgan = models.StyleQGAN(latent_dim=2, layers=2, circuit=circuit) - - with pytest.raises(ValueError): - qgan = models.StyleQGAN(latent_dim=2, circuit=circuit) - with pytest.raises(ValueError): - qgan = models.StyleQGAN(latent_dim=2, layers=2, set_parameters=lambda x: x) - - reference_distribution = generate_distribution(10) - qgan = models.StyleQGAN(latent_dim=2, circuit=circuit, set_parameters=lambda x: x) - with pytest.raises(ValueError): - qgan.fit(reference_distribution, save=False) - initial_params = np.random.uniform(-0.15, 0.15, 18) - qgan = models.StyleQGAN(latent_dim=2, layers=2) - with pytest.raises(ValueError): - qgan.fit(reference_distribution, initial_params=initial_params, save=False) - - -@pytest.mark.skipif( - sys.platform == "win32", - reason="no tensorflow-io-0.32.0's wheel available for Windows", -) -def test_qgan_custom_discriminator(): - from tensorflow.keras.layers import Dense # pylint: disable=E0611,import-error - from tensorflow.keras.models import Sequential # pylint: disable=E0611,import-error - - reference_distribution = generate_distribution(10) - # use wrong number of qubits so that we capture the error - nqubits = reference_distribution.shape[1] + 1 - discriminator = Sequential() - discriminator.add(Dense(200, use_bias=False, input_dim=nqubits)) - discriminator.add(Dense(1, activation="sigmoid")) - qgan = models.StyleQGAN(latent_dim=2, layers=1, discriminator=discriminator) - with pytest.raises(ValueError): - qgan.fit(reference_distribution, n_epochs=1, save=False) - - -def test_qgan_circuit_error(): - reference_distribution = generate_distribution(10) - # use wrong number of qubits so that we capture the error - nqubits = reference_distribution.shape[1] + 1 - nlayers = 1 - circuit = models.Circuit(nqubits) - for l in range(nlayers): - for q in range(nqubits): - circuit.add(gates.RY(q, 0)) - circuit.add(gates.RZ(q, 0)) - for i in range(0, nqubits - 1): - circuit.add(gates.CZ(i, i + 1)) - circuit.add(gates.CZ(nqubits - 1, 0)) - for q in range(nqubits): - circuit.add(gates.RY(q, 0)) - - initial_params = np.random.uniform(-0.15, 0.15, 18) - qgan = models.StyleQGAN(latent_dim=2, circuit=circuit, set_parameters=lambda x: x) - with pytest.raises(ValueError): - qgan.fit( - reference_distribution, - initial_params=initial_params, - n_epochs=1, - save=False, - ) From 682d50b9c5317ce50736a42fc0de68b8b799ce65 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 12:25:43 +0200 Subject: [PATCH 14/22] chore: Poetry lock --- poetry.lock | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 3860db1b0f..fde26fcb5e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5332,9 +5332,11 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] +qinfo = [] tensorflow = ["tensorflow"] +torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "7bdcae41ddf56e64fe52a31706d4bad43cd677430391c59edac75c9545be46d3" +content-hash = "edaaab66cd0c3f98db4af68f896107166fe63d8542f7324daa5f940545fd57a8" From 96a43bdbad19f8adc3373aba68872fc6dbdebd65 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 14:09:30 +0200 Subject: [PATCH 15/22] docs: Downgrade example from doctest to just text In order to avoid the explicit CVXPY dependency --- src/qibo/quantum_info/metrics.py | 78 ++++++++++++++++---------------- 1 file changed, 38 insertions(+), 40 deletions(-) diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py index 08dbf777af..13114e2f18 100644 --- a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -1,4 +1,5 @@ -"""Submodule with distances, metrics, and measures for quantum states and channels.""" +"""Submodule with distances, metrics, and measures for quantum states and +channels.""" from typing import Optional, Union @@ -10,7 +11,7 @@ def purity(state): - """Purity of a quantum state :math:`\\rho`, which is given by + """Purity of a quantum state :math:`\\rho`, which is given by. .. math:: \\text{purity}(\\rho) = \\text{tr}(\\rho^{2}) \\, . @@ -58,7 +59,8 @@ def impurity(state): def trace_distance(state, target, check_hermitian: bool = False, backend=None): - """Trace distance between two quantum states, :math:`\\rho` and :math:`\\sigma`: + """Trace distance between two quantum states, :math:`\\rho` and + :math:`\\sigma`: .. math:: T(\\rho, \\sigma) = \\frac{1}{2} \\, \\|\\rho - \\sigma\\|_{1} = \\frac{1}{2} \\, @@ -178,8 +180,8 @@ def hilbert_schmidt_distance(state, target): def fidelity(state, target, check_hermitian: bool = False, backend=None): - """Fidelity :math:`F(\\rho, \\sigma)` between ``state`` :math:`\\rho` - and ``target`` state :math:`\\sigma`. In general, + """Fidelity :math:`F(\\rho, \\sigma)` between ``state`` :math:`\\rho` and + ``target`` state :math:`\\sigma`. In general, .. math:: F(\\rho, \\sigma) = \\text{tr}^{2}\\left( \\sqrt{\\sqrt{\\sigma} \\, @@ -283,8 +285,8 @@ def fidelity(state, target, check_hermitian: bool = False, backend=None): def infidelity(state, target, check_hermitian: bool = False, backend=None): - """Infidelity between ``state`` :math:`\\rho` and ``target`` state :math:`\\sigma`, - which is given by + """Infidelity between ``state`` :math:`\\rho` and ``target`` state + :math:`\\sigma`, which is given by. .. math:: 1 - F(\\rho, \\, \\sigma) \\, , @@ -308,8 +310,8 @@ def infidelity(state, target, check_hermitian: bool = False, backend=None): def bures_angle(state, target, check_hermitian: bool = False, backend=None): - """Calculates the Bures angle :math:`D_{A}` between a ``state`` :math:`\\rho` - and a ``target`` state :math:`\\sigma`. This is given by + """Calculates the Bures angle :math:`D_{A}` between a ``state`` + :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by. .. math:: D_{A}(\\rho, \\, \\sigma) = \\text{arccos}\\left(\\sqrt{F(\\rho, \\, \\sigma)}\\right) \\, , @@ -337,8 +339,8 @@ def bures_angle(state, target, check_hermitian: bool = False, backend=None): def bures_distance(state, target, check_hermitian: bool = False, backend=None): - """Calculates the Bures distance :math:`D_{B}` between a ``state`` :math:`\\rho` - and a ``target`` state :math:`\\sigma`. This is given by + """Calculates the Bures distance :math:`D_{B}` between a ``state`` + :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by. .. math:: D_{B}(\\rho, \\, \\sigma) = \\sqrt{2 \\, \\left(1 - \\sqrt{F(\\rho, \\, \\sigma)}\\right)} @@ -366,8 +368,8 @@ def bures_distance(state, target, check_hermitian: bool = False, backend=None): def process_fidelity(channel, target=None, check_unitary: bool = False, backend=None): - """Process fidelity between a quantum ``channel`` :math:`\\mathcal{E}` and a - ``target`` unitary channel :math:`U`. The process fidelity is defined as + """Process fidelity between a quantum ``channel`` :math:`\\mathcal{E}` and + a ``target`` unitary channel :math:`U`. The process fidelity is defined as. .. math:: F_{\\text{pro}}(\\mathcal{E}, \\mathcal{U}) = \\frac{1}{d^{2}} \\, @@ -429,8 +431,8 @@ def process_fidelity(channel, target=None, check_unitary: bool = False, backend= def process_infidelity(channel, target=None, check_unitary: bool = False, backend=None): - """Process infidelity between quantum channel :math:`\\mathcal{E}` - and a ``target`` unitary channel :math:`U`. The process infidelity is defined as + """Process infidelity between quantum channel :math:`\\mathcal{E}` and a + ``target`` unitary channel :math:`U`. The process infidelity is defined as. .. math:: 1 - F_{\\text{pro}}(\\mathcal{E}, \\mathcal{U}) \\, , @@ -461,8 +463,8 @@ def average_gate_fidelity( channel, target=None, check_unitary: bool = False, backend=None ): """Average gate fidelity between a quantum ``channel`` :math:`\\mathcal{E}` - and a ``target`` unitary channel :math:`U`. The average gate fidelity - is defined as + and a ``target`` unitary channel :math:`U`. The average gate fidelity is + defined as. .. math:: F_{\\text{avg}}(\\mathcal{E}, \\mathcal{U}) = \\frac{d \\, @@ -500,8 +502,8 @@ def average_gate_fidelity( def gate_error(channel, target=None, check_unitary: bool = False, backend=None): - """Gate error between a quantum ``channel`` :math:`\\mathcal{E}` - and a ``target`` unitary channel :math:`U`, which is defined as + """Gate error between a quantum ``channel`` :math:`\\mathcal{E}` and a + ``target`` unitary channel :math:`U`, which is defined as. .. math:: E(\\mathcal{E}, \\mathcal{U}) = 1 - F_{\\text{avg}}(\\mathcal{E}, \\mathcal{U}) \\, , @@ -532,7 +534,7 @@ def gate_error(channel, target=None, check_unitary: bool = False, backend=None): def diamond_norm(channel, target=None, backend=None, **kwargs): """Calculates the diamond norm :math:`\\|\\mathcal{E}\\|_{\\diamond}` of - ``channel`` :math:`\\mathcal{E}`, which is given by + ``channel`` :math:`\\mathcal{E}`, which is given by. .. math:: \\|\\mathcal{E}\\|_{\\diamond} = \\max_{\\rho} \\, \\| \\left(\\mathcal{E} \\otimes I_{d^{2}}\\right)(\\rho) \\|_{1} \\, , @@ -544,22 +546,20 @@ def diamond_norm(channel, target=None, backend=None, **kwargs): If a ``target`` channel :math:`\\Lambda` is specified, then it calculates :math:`\\| \\mathcal{E} - \\Lambda\\|_{\\diamond}`. - Example: + Example:: - .. testcode:: + from qibo.quantum_info import diamond_norm, random_unitary, to_choi - from qibo.quantum_info import diamond_norm, random_unitary, to_choi + nqubits = 1 + dim = 2**nqubits - nqubits = 1 - dim = 2**nqubits + unitary = random_unitary(dim) + unitary = to_choi(unitary, order="row") - unitary = random_unitary(dim) - unitary = to_choi(unitary, order="row") + unitary_2 = random_unitary(dim) + unitary_2 = to_choi(unitary_2, order="row") - unitary_2 = random_unitary(dim) - unitary_2 = to_choi(unitary_2, order="row") - - dnorm = diamond_norm(unitary, unitary_2) + dnorm = diamond_norm(unitary, unitary_2) Args: channel (ndarray): row-vectorized Choi representation of a quantum channel. @@ -574,7 +574,6 @@ def diamond_norm(channel, target=None, backend=None, **kwargs): .. note:: This function requires the optional CVXPY package to be installed. - """ import cvxpy # pylint: disable=C0415 @@ -667,8 +666,8 @@ def expressibility( order: Optional[Union[int, float, str]] = 2, backend=None, ): - """Returns the expressibility :math:`\\|A\\|` of a parametrized - circuit, where + """Returns the expressibility :math:`\\|A\\|` of a parametrized circuit, + where. .. math:: A = \\int_{\\text{Haar}} d\\psi \\, \\left(|\\psi\\rangle\\right.\\left. @@ -723,7 +722,8 @@ def frame_potential( samples: int = None, backend=None, ): - """Returns the frame potential of a parametrized circuit under uniform sampling of the parameters. + """Returns the frame potential of a parametrized circuit under uniform + sampling of the parameters. For :math:`n` qubits and moment :math:`t`, the frame potential :math:`\\mathcal{F}_{\\mathcal{U}}^{(t)}` if given by [1] @@ -756,7 +756,6 @@ def frame_potential( References: 1. M. Liu *et al.*, *Estimating the randomness of quantum circuit ensembles up to 50 qubits*. `arXiv:2205.09900 [quant-ph] `_. - """ if not isinstance(power_t, int): raise_error( @@ -794,9 +793,9 @@ def frame_potential( def _check_hermitian_or_not_gpu(matrix, backend=None): - """Checks if a given matrix is Hermitian and whether - the backend is neither :class:`qibojit.backends.CupyBackend` - nor :class:`qibojit.backends.CuQuantumBackend`. + """Checks if a given matrix is Hermitian and whether the backend is neither + :class:`qibojit.backends.CupyBackend` nor + :class:`qibojit.backends.CuQuantumBackend`. Args: matrix: input array. @@ -810,7 +809,6 @@ def _check_hermitian_or_not_gpu(matrix, backend=None): Raises: NotImplementedError: If `matrix` is not Hermitian and `backend` is not :class:`qibojit.backends.CupyBackend` - """ backend = _check_backend(backend) From 9b6c04aadaa95f40683a349c7070afb49c323e08 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 14:15:01 +0200 Subject: [PATCH 16/22] docs: Downgrade further example to code block This time to avoid using tf.keras --- doc/source/code-examples/advancedexamples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/code-examples/advancedexamples.rst b/doc/source/code-examples/advancedexamples.rst index 23c67811d4..64bf31c0fc 100644 --- a/doc/source/code-examples/advancedexamples.rst +++ b/doc/source/code-examples/advancedexamples.rst @@ -702,7 +702,7 @@ the following script optimizes the parameters of two rotations so that the circu output matches a target state using the fidelity as the corresponding loss function. -.. testcode:: +.. code-block:: python import qibo qibo.set_backend("tensorflow") From aa151e864c5650363c0ca89c99bf36b1fabd62ad Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 14:21:53 +0200 Subject: [PATCH 17/22] chore: Polish environment files I.e. remove explicit python version from nix, and remove the mention of CVXPY in qinfo extra, since the dependency is not there any longer --- flake.lock | 73 +------------------------------------------------- flake.nix | 5 ---- pyproject.toml | 2 +- 3 files changed, 2 insertions(+), 78 deletions(-) diff --git a/flake.lock b/flake.lock index 39d808d920..3912e526a4 100644 --- a/flake.lock +++ b/flake.lock @@ -37,22 +37,6 @@ "type": "github" } }, - "flake-compat_2": { - "flake": false, - "locked": { - "lastModified": 1696426674, - "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", - "owner": "edolstra", - "repo": "flake-compat", - "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", - "type": "github" - }, - "original": { - "owner": "edolstra", - "repo": "flake-compat", - "type": "github" - } - }, "flake-utils": { "inputs": { "systems": "systems" @@ -71,23 +55,6 @@ "type": "github" } }, - "flake-utils_2": { - "inputs": { - "systems": "systems_2" - }, - "locked": { - "lastModified": 1701680307, - "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", - "type": "github" - }, - "original": { - "id": "flake-utils", - "type": "indirect" - } - }, "gitignore": { "inputs": { "nixpkgs": [ @@ -166,28 +133,6 @@ "type": "github" } }, - "nixpkgs-python": { - "inputs": { - "flake-compat": "flake-compat_2", - "flake-utils": "flake-utils_2", - "nixpkgs": [ - "nixpkgs" - ] - }, - "locked": { - "lastModified": 1709875392, - "narHash": "sha256-ZC/8TNR2q8Q+j4vzaW3B8jLS9ZDvss61brFW4VtWr0A=", - "owner": "cachix", - "repo": "nixpkgs-python", - "rev": "7296d316153575b8db614ff02dac5b7501a92071", - "type": "github" - }, - "original": { - "owner": "cachix", - "repo": "nixpkgs-python", - "type": "github" - } - }, "nixpkgs-regression": { "locked": { "lastModified": 1643052045, @@ -268,8 +213,7 @@ "inputs": { "devenv": "devenv", "nixpkgs": "nixpkgs_2", - "nixpkgs-python": "nixpkgs-python", - "systems": "systems_3" + "systems": "systems_2" } }, "systems": { @@ -301,21 +245,6 @@ "repo": "default", "type": "github" } - }, - "systems_3": { - "locked": { - "lastModified": 1681028828, - "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", - "owner": "nix-systems", - "repo": "default", - "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", - "type": "github" - }, - "original": { - "owner": "nix-systems", - "repo": "default", - "type": "github" - } } }, "root": "root", diff --git a/flake.nix b/flake.nix index 9aea3a4aa8..4d4ccaaac4 100644 --- a/flake.nix +++ b/flake.nix @@ -3,10 +3,6 @@ nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; systems.url = "github:nix-systems/default"; devenv.url = "github:cachix/devenv"; - nixpkgs-python = { - url = "github:cachix/nixpkgs-python"; - inputs.nixpkgs.follows = "nixpkgs"; - }; }; outputs = { @@ -49,7 +45,6 @@ install.enable = true; install.groups = ["dev"]; }; - version = "3.12"; }; } ]; diff --git a/pyproject.toml b/pyproject.toml index 43764897d5..a2832ffe5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -104,7 +104,7 @@ qibotn = { git = "https://github.com/qiboteam/qibotn.git", branch = "py3.12" } [tool.poetry.extras] tensorflow = ["tensorflow"] torch = ["torch"] -qinfo = ["cvxpy", "stim"] +qinfo = ["stim"] [tool.pylint.main] ignored-modules = ["cvxpy"] From edd0b56b5b02e08fb7a1281e7b383daa5089c2fb Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 10 Apr 2024 14:23:58 +0200 Subject: [PATCH 18/22] chore: Poetry lock --- poetry.lock | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index fde26fcb5e..9922196f76 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2601,7 +2601,7 @@ files = [ {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fbb160554e319f7b22ecf530a80a3ff496d38e8e07ae763b9e82fadfe96f273"}, {file = "msgpack-1.0.8-cp39-cp39-win32.whl", hash = "sha256:f9af38a89b6a5c04b7d18c492c8ccf2aee7048aff1ce8437c4683bb5a1df893d"}, {file = "msgpack-1.0.8-cp39-cp39-win_amd64.whl", hash = "sha256:ed59dd52075f8fc91da6053b12e8c89e37aa043f8986efd89e61fae69dc1b011"}, - {file = "msgpack-1.0.8.tar.gz", hash = "sha256:95c02b0e27e706e48d0e5426d1710ca78e0f0628d6e89d5b5a5b91a5f12274f3"}, + {file = "msgpack-1.0.8-py3-none-any.whl", hash = "sha256:24f727df1e20b9876fa6e95f840a2a2651e34c0ad147676356f4bf5fbb0206ca"}, ] [[package]] @@ -3847,7 +3847,6 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, - {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -5339,4 +5338,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "edaaab66cd0c3f98db4af68f896107166fe63d8542f7324daa5f940545fd57a8" +content-hash = "b732622c31bc0de1b69f061d6be43ecb091299e1947bba1ce01175d34c4ad808" From 9d2fae5c83dfc30f443f5e998f23c640ceadcaeb Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 11 Apr 2024 13:04:14 +0200 Subject: [PATCH 19/22] Update src/qibo/quantum_info/metrics.py Co-authored-by: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> --- src/qibo/quantum_info/metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py index 13114e2f18..ea68735fe8 100644 --- a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -575,7 +575,7 @@ def diamond_norm(channel, target=None, backend=None, **kwargs): .. note:: This function requires the optional CVXPY package to be installed. """ - import cvxpy # pylint: disable=C0415 + import cvxpy if target is not None: if channel.shape != target.shape: From 02e737fe356d1d3c06875af78295c973a9416794 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 11 Apr 2024 13:23:50 +0200 Subject: [PATCH 20/22] fix: Partially restore autoformatted docstrings --- src/qibo/quantum_info/metrics.py | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/src/qibo/quantum_info/metrics.py b/src/qibo/quantum_info/metrics.py index ea68735fe8..2fe701ed7f 100644 --- a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -1,5 +1,4 @@ -"""Submodule with distances, metrics, and measures for quantum states and -channels.""" +"""Distances, metrics, and measures for quantum states and channels.""" from typing import Optional, Union @@ -11,7 +10,9 @@ def purity(state): - """Purity of a quantum state :math:`\\rho`, which is given by. + """Purity of a quantum state :math:`\\rho`. + + This is given by .. math:: \\text{purity}(\\rho) = \\text{tr}(\\rho^{2}) \\, . @@ -45,8 +46,9 @@ def purity(state): def impurity(state): - """Impurity of quantum state :math:`\\rho`, which is given by - :math:`1 - \\text{purity}(\\rho)`, where :math:`\\text{purity}` + """Impurity of quantum state :math:`\\rho`. + + This is given by :math:`1 - \\text{purity}(\\rho)`, where :math:`\\text{purity}` is defined in :func:`qibo.quantum_info.purity`. Args: @@ -286,7 +288,7 @@ def fidelity(state, target, check_hermitian: bool = False, backend=None): def infidelity(state, target, check_hermitian: bool = False, backend=None): """Infidelity between ``state`` :math:`\\rho` and ``target`` state - :math:`\\sigma`, which is given by. + :math:`\\sigma`, which is given by .. math:: 1 - F(\\rho, \\, \\sigma) \\, , @@ -311,7 +313,7 @@ def infidelity(state, target, check_hermitian: bool = False, backend=None): def bures_angle(state, target, check_hermitian: bool = False, backend=None): """Calculates the Bures angle :math:`D_{A}` between a ``state`` - :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by. + :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by .. math:: D_{A}(\\rho, \\, \\sigma) = \\text{arccos}\\left(\\sqrt{F(\\rho, \\, \\sigma)}\\right) \\, , @@ -340,7 +342,7 @@ def bures_angle(state, target, check_hermitian: bool = False, backend=None): def bures_distance(state, target, check_hermitian: bool = False, backend=None): """Calculates the Bures distance :math:`D_{B}` between a ``state`` - :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by. + :math:`\\rho` and a ``target`` state :math:`\\sigma`. This is given by .. math:: D_{B}(\\rho, \\, \\sigma) = \\sqrt{2 \\, \\left(1 - \\sqrt{F(\\rho, \\, \\sigma)}\\right)} @@ -369,7 +371,7 @@ def bures_distance(state, target, check_hermitian: bool = False, backend=None): def process_fidelity(channel, target=None, check_unitary: bool = False, backend=None): """Process fidelity between a quantum ``channel`` :math:`\\mathcal{E}` and - a ``target`` unitary channel :math:`U`. The process fidelity is defined as. + a ``target`` unitary channel :math:`U`. The process fidelity is defined as .. math:: F_{\\text{pro}}(\\mathcal{E}, \\mathcal{U}) = \\frac{1}{d^{2}} \\, @@ -432,7 +434,7 @@ def process_fidelity(channel, target=None, check_unitary: bool = False, backend= def process_infidelity(channel, target=None, check_unitary: bool = False, backend=None): """Process infidelity between quantum channel :math:`\\mathcal{E}` and a - ``target`` unitary channel :math:`U`. The process infidelity is defined as. + ``target`` unitary channel :math:`U`. The process infidelity is defined as .. math:: 1 - F_{\\text{pro}}(\\mathcal{E}, \\mathcal{U}) \\, , @@ -464,7 +466,7 @@ def average_gate_fidelity( ): """Average gate fidelity between a quantum ``channel`` :math:`\\mathcal{E}` and a ``target`` unitary channel :math:`U`. The average gate fidelity is - defined as. + defined as .. math:: F_{\\text{avg}}(\\mathcal{E}, \\mathcal{U}) = \\frac{d \\, @@ -503,7 +505,7 @@ def average_gate_fidelity( def gate_error(channel, target=None, check_unitary: bool = False, backend=None): """Gate error between a quantum ``channel`` :math:`\\mathcal{E}` and a - ``target`` unitary channel :math:`U`, which is defined as. + ``target`` unitary channel :math:`U`, which is defined as .. math:: E(\\mathcal{E}, \\mathcal{U}) = 1 - F_{\\text{avg}}(\\mathcal{E}, \\mathcal{U}) \\, , @@ -534,7 +536,7 @@ def gate_error(channel, target=None, check_unitary: bool = False, backend=None): def diamond_norm(channel, target=None, backend=None, **kwargs): """Calculates the diamond norm :math:`\\|\\mathcal{E}\\|_{\\diamond}` of - ``channel`` :math:`\\mathcal{E}`, which is given by. + ``channel`` :math:`\\mathcal{E}`, which is given by .. math:: \\|\\mathcal{E}\\|_{\\diamond} = \\max_{\\rho} \\, \\| \\left(\\mathcal{E} \\otimes I_{d^{2}}\\right)(\\rho) \\|_{1} \\, , @@ -667,7 +669,7 @@ def expressibility( backend=None, ): """Returns the expressibility :math:`\\|A\\|` of a parametrized circuit, - where. + where .. math:: A = \\int_{\\text{Haar}} d\\psi \\, \\left(|\\psi\\rangle\\right.\\left. From 0006411bb3ca604ce4504fc1245358c7857d3bcf Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 11 Apr 2024 13:29:49 +0200 Subject: [PATCH 21/22] chore: Poetry lock --- poetry.lock | 1300 +++++++++++++++++++++++++-------------------------- 1 file changed, 649 insertions(+), 651 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9922196f76..21b8301568 100644 --- a/poetry.lock +++ b/poetry.lock @@ -596,76 +596,76 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"] [[package]] name = "contourpy" -version = "1.2.0" +version = "1.2.1" description = "Python library for calculating contours of 2D quadrilateral grids" optional = false python-versions = ">=3.9" files = [ - {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"}, - {file = "contourpy-1.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ab459a1cbbf18e8698399c595a01f6dcc5c138220ca3ea9e7e6126232d102bb4"}, - {file = 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a/src/qibo/quantum_info/metrics.py +++ b/src/qibo/quantum_info/metrics.py @@ -534,7 +534,7 @@ def gate_error(channel, target=None, check_unitary: bool = False, backend=None): return error -def diamond_norm(channel, target=None, backend=None, **kwargs): +def diamond_norm(channel, target=None, backend=None, **kwargs): # pragma: no cover """Calculates the diamond norm :math:`\\|\\mathcal{E}\\|_{\\diamond}` of ``channel`` :math:`\\mathcal{E}`, which is given by