diff --git a/.envrc b/.envrc
new file mode 100644
index 0000000..3af52d8
--- /dev/null
+++ b/.envrc
@@ -0,0 +1,9 @@
+if ! has nix_direnv_version || ! nix_direnv_version 2.2.1; then
+  source_url "https://raw.githubusercontent.com/nix-community/nix-direnv/2.2.1/direnvrc" "sha256-zelF0vLbEl5uaqrfIzbgNzJWGmLzCmYAkInj/LNxvKs="
+fi
+
+watch_file flake.nix
+watch_file flake.lock
+if ! use flake . --impure; then
+  echo "devenv could not be built. The devenv environment was not loaded. Make the necessary changes to devenv.nix and hit enter to try again." >&2
+fi
diff --git a/.gitignore b/.gitignore
index 6769e21..a60e5bc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -157,4 +157,6 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
\ No newline at end of file
+#.idea/
+
+.devenv
diff --git a/flake.lock b/flake.lock
new file mode 100644
index 0000000..b164f50
--- /dev/null
+++ b/flake.lock
@@ -0,0 +1,528 @@
+{
+  "nodes": {
+    "cachix": {
+      "inputs": {
+        "devenv": "devenv_2",
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "pre-commit-hooks": [
+          "devenv",
+          "pre-commit-hooks"
+        ]
+      },
+      "locked": {
+        "lastModified": 1712055811,
+        "narHash": "sha256-7FcfMm5A/f02yyzuavJe06zLa9hcMHsagE28ADcmQvk=",
+        "owner": "cachix",
+        "repo": "cachix",
+        "rev": "02e38da89851ec7fec3356a5c04bc8349cae0e30",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "cachix",
+        "type": "github"
+      }
+    },
+    "devenv": {
+      "inputs": {
+        "cachix": "cachix",
+        "flake-compat": "flake-compat_2",
+        "nix": "nix_2",
+        "nixpkgs": "nixpkgs_2",
+        "pre-commit-hooks": "pre-commit-hooks"
+      },
+      "locked": {
+        "lastModified": 1715187131,
+        "narHash": "sha256-0QwlM89Hk4/1+46mOmt3yYegYxnPUmMFbYJbZU5AT+Y=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "a2bc5aedb207ecf2fae89bd4d7e372003e916fec",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "devenv_2": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "cachix",
+          "flake-compat"
+        ],
+        "nix": "nix",
+        "nixpkgs": "nixpkgs",
+        "poetry2nix": "poetry2nix",
+        "pre-commit-hooks": [
+          "devenv",
+          "cachix",
+          "pre-commit-hooks"
+        ]
+      },
+      "locked": {
+        "lastModified": 1708704632,
+        "narHash": "sha256-w+dOIW60FKMaHI1q5714CSibk99JfYxm0CzTinYWr+Q=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "2ee4450b0f4b95a1b90f2eb5ffea98b90e48c196",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "python-rewrite",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "flake-compat": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1673956053,
+        "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "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-compat_3": {
+      "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"
+      },
+      "locked": {
+        "lastModified": 1689068808,
+        "narHash": "sha256-6ixXo3wt24N/melDWjq70UuHQLxGV8jZvooRanIHXw0=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "919d646de7be200f3bf08cb76ae1f09402b6f9b4",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "flake-utils_2": {
+      "inputs": {
+        "systems": "systems_2"
+      },
+      "locked": {
+        "lastModified": 1710146030,
+        "narHash": "sha256-SZ5L6eA7HJ/nmkzGG7/ISclqe6oZdOZTNoesiInkXPQ=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "b1d9ab70662946ef0850d488da1c9019f3a9752a",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "flake-utils_3": {
+      "inputs": {
+        "systems": "systems_3"
+      },
+      "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": [
+          "devenv",
+          "pre-commit-hooks",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1709087332,
+        "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=",
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "rev": "637db329424fd7e46cf4185293b9cc8c88c95394",
+        "type": "github"
+      },
+      "original": {
+        "owner": "hercules-ci",
+        "repo": "gitignore.nix",
+        "type": "github"
+      }
+    },
+    "nix": {
+      "inputs": {
+        "flake-compat": "flake-compat",
+        "nixpkgs": [
+          "devenv",
+          "cachix",
+          "devenv",
+          "nixpkgs"
+        ],
+        "nixpkgs-regression": "nixpkgs-regression"
+      },
+      "locked": {
+        "lastModified": 1712911606,
+        "narHash": "sha256-BGvBhepCufsjcUkXnEEXhEVjwdJAwPglCC2+bInc794=",
+        "owner": "domenkozar",
+        "repo": "nix",
+        "rev": "b24a9318ea3f3600c1e24b4a00691ee912d4de12",
+        "type": "github"
+      },
+      "original": {
+        "owner": "domenkozar",
+        "ref": "devenv-2.21",
+        "repo": "nix",
+        "type": "github"
+      }
+    },
+    "nix-github-actions": {
+      "inputs": {
+        "nixpkgs": [
+          "devenv",
+          "cachix",
+          "devenv",
+          "poetry2nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1688870561,
+        "narHash": "sha256-4UYkifnPEw1nAzqqPOTL2MvWtm3sNGw1UTYTalkTcGY=",
+        "owner": "nix-community",
+        "repo": "nix-github-actions",
+        "rev": "165b1650b753316aa7f1787f3005a8d2da0f5301",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "nix-github-actions",
+        "type": "github"
+      }
+    },
+    "nix_2": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "nixpkgs-regression": "nixpkgs-regression_2"
+      },
+      "locked": {
+        "lastModified": 1712911606,
+        "narHash": "sha256-BGvBhepCufsjcUkXnEEXhEVjwdJAwPglCC2+bInc794=",
+        "owner": "domenkozar",
+        "repo": "nix",
+        "rev": "b24a9318ea3f3600c1e24b4a00691ee912d4de12",
+        "type": "github"
+      },
+      "original": {
+        "owner": "domenkozar",
+        "ref": "devenv-2.21",
+        "repo": "nix",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1692808169,
+        "narHash": "sha256-x9Opq06rIiwdwGeK2Ykj69dNc2IvUH1fY55Wm7atwrE=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "9201b5ff357e781bf014d0330d18555695df7ba8",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-python": {
+      "inputs": {
+        "flake-compat": "flake-compat_3",
+        "flake-utils": "flake-utils_3",
+        "nixpkgs": [
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1713539989,
+        "narHash": "sha256-/XaYRbYRYciZ4atdZmVdvHdoI7Avjt4pUMI6ZBZMl0s=",
+        "owner": "cachix",
+        "repo": "nixpkgs-python",
+        "rev": "462946481a3f4497c90103c92b5e7408e3c59f4f",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "nixpkgs-python",
+        "type": "github"
+      }
+    },
+    "nixpkgs-regression": {
+      "locked": {
+        "lastModified": 1643052045,
+        "narHash": "sha256-uGJ0VXIhWKGXxkeNnq4TvV3CIOkUJ3PAoLZ3HMzNVMw=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2",
+        "type": "github"
+      }
+    },
+    "nixpkgs-regression_2": {
+      "locked": {
+        "lastModified": 1643052045,
+        "narHash": "sha256-uGJ0VXIhWKGXxkeNnq4TvV3CIOkUJ3PAoLZ3HMzNVMw=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2",
+        "type": "github"
+      }
+    },
+    "nixpkgs-stable": {
+      "locked": {
+        "lastModified": 1710695816,
+        "narHash": "sha256-3Eh7fhEID17pv9ZxrPwCLfqXnYP006RKzSs0JptsN84=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "614b4613980a522ba49f0d194531beddbb7220d3",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-23.11",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs_2": {
+      "locked": {
+        "lastModified": 1713361204,
+        "narHash": "sha256-TA6EDunWTkc5FvDCqU3W2T3SFn0gRZqh6D/hJnM02MM=",
+        "owner": "cachix",
+        "repo": "devenv-nixpkgs",
+        "rev": "285676e87ad9f0ca23d8714a6ab61e7e027020c6",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "rolling",
+        "repo": "devenv-nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs_3": {
+      "locked": {
+        "lastModified": 1715087517,
+        "narHash": "sha256-CLU5Tsg24Ke4+7sH8azHWXKd0CFd4mhLWfhYgUiDBpQ=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "b211b392b8486ee79df6cdfb1157ad2133427a29",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "poetry2nix": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nix-github-actions": "nix-github-actions",
+        "nixpkgs": [
+          "devenv",
+          "cachix",
+          "devenv",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1692876271,
+        "narHash": "sha256-IXfZEkI0Mal5y1jr6IRWMqK8GW2/f28xJenZIPQqkY0=",
+        "owner": "nix-community",
+        "repo": "poetry2nix",
+        "rev": "d5006be9c2c2417dafb2e2e5034d83fabd207ee3",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-community",
+        "repo": "poetry2nix",
+        "type": "github"
+      }
+    },
+    "pre-commit-hooks": {
+      "inputs": {
+        "flake-compat": [
+          "devenv",
+          "flake-compat"
+        ],
+        "flake-utils": "flake-utils_2",
+        "gitignore": "gitignore",
+        "nixpkgs": [
+          "devenv",
+          "nixpkgs"
+        ],
+        "nixpkgs-stable": "nixpkgs-stable"
+      },
+      "locked": {
+        "lastModified": 1713775815,
+        "narHash": "sha256-Wu9cdYTnGQQwtT20QQMg7jzkANKQjwBD9iccfGKkfls=",
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "rev": "2ac4dcbf55ed43f3be0bae15e181f08a57af24a4",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "pre-commit-hooks.nix",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "devenv": "devenv",
+        "nixpkgs": "nixpkgs_3",
+        "nixpkgs-python": "nixpkgs-python",
+        "systems": "systems_4"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    },
+    "systems_2": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "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"
+      }
+    },
+    "systems_4": {
+      "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",
+  "version": 7
+}
diff --git a/flake.nix b/flake.nix
new file mode 100644
index 0000000..a5ec51b
--- /dev/null
+++ b/flake.nix
@@ -0,0 +1,62 @@
+{
+  inputs = {
+    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 = {
+    self,
+    nixpkgs,
+    devenv,
+    systems,
+    ...
+  } @ inputs: let
+    forEachSystem = nixpkgs.lib.genAttrs (import systems);
+  in {
+    packages = forEachSystem (system: {
+      default =
+        nixpkgs.legacyPackages.${system}.poetry2nix.mkPoetryApplication
+        {
+          projectDir = self;
+          preferWheels = true;
+        };
+    });
+
+    devShells =
+      forEachSystem
+      (system: let
+        pkgs = nixpkgs.legacyPackages.${system};
+      in {
+        default = devenv.lib.mkShell {
+          inherit inputs pkgs;
+
+          modules = [
+            {
+              packages = with pkgs; [pre-commit poethepoet];
+
+              languages.python = {
+                enable = true;
+                poetry = {
+                  enable = true;
+                  install.enable = true;
+                  install.groups = ["dev"];
+                  install.allExtras = true;
+                };
+                version = "3.11";
+              };
+            }
+          ];
+        };
+      });
+  };
+
+  nixConfig = {
+    extra-trusted-public-keys = "devenv.cachix.org-1:w1cLUi8dv3hnoSPGAuibQv+f9TZLr6cv/Hm9XgU50cw=";
+    extra-substituters = "https://devenv.cachix.org";
+  };
+}
diff --git a/poetry.lock b/poetry.lock
index c45af2f..372514f 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -249,63 +249,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.4.4"
+version = "7.5.1"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -342,13 +342,13 @@ profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
@@ -369,17 +369,71 @@ files = [
 pyreadline = {version = "*", markers = "platform_system == \"Windows\""}
 pyrepl = ">=0.8.2"
 
+[[package]]
+name = "filelock"
+version = "3.14.0"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
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+[package.extras]
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+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+typing = ["typing-extensions (>=4.8)"]
+
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 name = "flatbuffers"
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+version = "24.3.25"
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 python-versions = "*"
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+version = "2024.5.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
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+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
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+tqdm = ["tqdm"]
+
 [[package]]
 name = "future"
 version = "1.0.0"
@@ -419,102 +473,90 @@ six = "*"
 
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@@ -549,13 +591,13 @@ sparktrials = ["pyspark"]
 
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+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
@@ -588,6 +630,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
+]
+
 [[package]]
 name = "ipython"
 version = "7.34.0"
@@ -638,6 +694,39 @@ files = [
 [package.extras]
 colors = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "jax"
+version = "0.4.28"
+description = "Differentiate, compile, and transform Numpy code."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jax-0.4.28-py3-none-any.whl", hash = "sha256:6a181e6b5a5b1140e19cdd2d5c4aa779e4cb4ec627757b918be322d8e81035ba"},
+    {file = "jax-0.4.28.tar.gz", hash = "sha256:dcf0a44aff2e1713f0a2b369281cd5b79d8c18fc1018905c4125897cb06b37e9"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
+ml-dtypes = ">=0.2.0"
+numpy = [
+    {version = ">=1.22", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+]
+opt-einsum = "*"
+scipy = ">=1.9"
+
+[package.extras]
+australis = ["protobuf (>=3.13,<4)"]
+ci = ["jaxlib (==0.4.27)"]
+cpu = ["jaxlib (==0.4.28)"]
+cuda = ["jaxlib (==0.4.28+cuda12.cudnn89)"]
+cuda12 = ["jax-cuda12-plugin (==0.4.28)", "jaxlib (==0.4.28)", "nvidia-cublas-cu12 (>=12.1.3.1)", "nvidia-cuda-cupti-cu12 (>=12.1.105)", "nvidia-cuda-nvcc-cu12 (>=12.1.105)", "nvidia-cuda-runtime-cu12 (>=12.1.105)", "nvidia-cudnn-cu12 (>=8.9.2.26,<9.0)", "nvidia-cufft-cu12 (>=11.0.2.54)", "nvidia-cusolver-cu12 (>=11.4.5.107)", "nvidia-cusparse-cu12 (>=12.1.0.106)", "nvidia-nccl-cu12 (>=2.18.1)", "nvidia-nvjitlink-cu12 (>=12.1.105)"]
+cuda12-cudnn89 = ["jaxlib (==0.4.28+cuda12.cudnn89)"]
+cuda12-local = ["jaxlib (==0.4.28+cuda12.cudnn89)"]
+cuda12-pip = ["jaxlib (==0.4.28+cuda12.cudnn89)", "nvidia-cublas-cu12 (>=12.1.3.1)", "nvidia-cuda-cupti-cu12 (>=12.1.105)", "nvidia-cuda-nvcc-cu12 (>=12.1.105)", "nvidia-cuda-runtime-cu12 (>=12.1.105)", "nvidia-cudnn-cu12 (>=8.9.2.26,<9.0)", "nvidia-cufft-cu12 (>=11.0.2.54)", "nvidia-cusolver-cu12 (>=11.4.5.107)", "nvidia-cusparse-cu12 (>=12.1.0.106)", "nvidia-nccl-cu12 (>=2.18.1)", "nvidia-nvjitlink-cu12 (>=12.1.105)"]
+minimum-jaxlib = ["jaxlib (==0.4.27)"]
+tpu = ["jaxlib (==0.4.28)", "libtpu-nightly (==0.1.dev20240508)", "requests"]
+
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -657,26 +746,43 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
+[[package]]
+name = "jinja2"
+version = "3.1.4"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
 [[package]]
 name = "joblib"
-version = "1.3.2"
+version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
-    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
+    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
 [[package]]
 name = "keras"
-version = "3.1.1"
+version = "3.3.3"
 description = "Multi-backend Keras."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "keras-3.1.1-py3-none-any.whl", hash = "sha256:b5d45f0b5116b11db502da00bd501592364325d01724e6cb2032711e3e32677e"},
-    {file = "keras-3.1.1.tar.gz", hash = "sha256:55558ea228dc38e7667874fd2e83eaf7faeb026e2e8615b36a8616830f7e303b"},
+    {file = "keras-3.3.3-py3-none-any.whl", hash = "sha256:260df9ef71c6b89eb6816ce1c60f139c38ccdddd16f24e7005d2be127cdef8e4"},
+    {file = "keras-3.3.3.tar.gz", hash = "sha256:f2fdffc8434fd77045cf8fb21816dbaa2308d5f76974ca924b2f60b40433b1a0"},
 ]
 
 [package.dependencies]
@@ -895,13 +1001,13 @@ files = [
 
 [[package]]
 name = "matplotlib-inline"
-version = "0.1.6"
+version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-inline-0.1.6.tar.gz", hash = "sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304"},
-    {file = "matplotlib_inline-0.1.6-py3-none-any.whl", hash = "sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311"},
+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
 ]
 
 [package.dependencies]
@@ -929,6 +1035,24 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"},
+    {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"},
+    {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"},
+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "ml-dtypes"
 version = "0.3.2"
@@ -984,13 +1108,13 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "namex"
-version = "0.0.7"
+version = "0.0.8"
 description = "A simple utility to separate the implementation of your Python package and its public API surface."
 optional = false
 python-versions = "*"
 files = [
-    {file = "namex-0.0.7-py3-none-any.whl", hash = "sha256:8a4f062945f405d77cb66b907f16aa2fd83681945e998be840eb6c4154d40108"},
-    {file = "namex-0.0.7.tar.gz", hash = "sha256:84ba65bc4d22bd909e3d26bf2ffb4b9529b608cb3f9a4336f776b04204ced69b"},
+    {file = "namex-0.0.8-py3-none-any.whl", hash = "sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487"},
+    {file = "namex-0.0.8.tar.gz", hash = "sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b"},
 ]
 
 [[package]]
@@ -1090,6 +1214,148 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.1.3.1"
+description = "CUBLAS native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
+    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.1.105"
+description = "CUDA profiling tools runtime libs."
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
+    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.1.105"
+description = "NVRTC native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.1.105"
+description = "CUDA Runtime native Libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
+    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "8.9.2.26"
+description = "cuDNN runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.0.2.54"
+description = "CUFFT native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
+    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.2.106"
+description = "CURAND native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
+    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.4.5.107"
+description = "CUDA solver native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
+    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.1.0.106"
+description = "CUSPARSE native runtime libraries"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
+    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.20.5"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.4.127"
+description = "Nvidia JIT LTO Library"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-win_amd64.whl", hash = "sha256:fd9020c501d27d135f983c6d3e244b197a7ccad769e34df53a42e276b0e25fa1"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.1.105"
+description = "NVIDIA Tools Extension"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
+    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+]
+
 [[package]]
 name = "openqasm3"
 version = "0.5.0"
@@ -1130,50 +1396,50 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
 [[package]]
 name = "optree"
-version = "0.10.0"
+version = "0.11.0"
 description = "Optimized PyTree Utilities."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "optree-0.10.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ac2c0fa383f504f03887a0c0ffcb6a4187c43c8c99c32f52ff14e7eae2c8c69b"},
-    {file = "optree-0.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8fa16b16203938b7a9caa4603998d0968b408f7f3a1a9f7f84763802daf1cff0"},
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 [[package]]
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 [package.extras]
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 tensorflow = ["tensorflow (>=2.14.1,<2.16)"]
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 [[package]]
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 [package.dependencies]
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 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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 python-versions = ">=3.8"
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 [[package]]
@@ -1692,6 +1959,19 @@ files = [
 [package.extras]
 widechars = ["wcwidth"]
 
+[[package]]
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+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
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+python-versions = "*"
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 [[package]]
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 version = "2.16.2"
@@ -1783,32 +2063,35 @@ and-cuda = ["nvidia-cublas-cu12 (==12.3.4.1)", "nvidia-cuda-cupti-cu12 (==12.3.1
 
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 [package.dependencies]
@@ -1868,28 +2205,51 @@ telegram = ["requests"]
 
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+
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+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
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+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
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 python-versions = ">=3.8"
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 [[package]]
@@ -1922,13 +2282,13 @@ files = [
 
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 description = "The comprehensive WSGI web application library."
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -2049,20 +2409,20 @@ files = [
 
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 description = "Backport of pathlib-compatible object wrapper for zip files"
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 python-versions = ">=3.8"
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-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
+    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
 ]
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-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)"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "b5f9be56480821c6becd85cf6629abb3d08c480fbe7618eed11c70a7de7d6555"
+content-hash = "c102dcfb6877cbe76d2a2dd2c22159ec14fc1d2a72919f336b4723a6160dc956"
diff --git a/pyproject.toml b/pyproject.toml
index 24f2442..86781ce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,8 @@ numba = "^0.59.0"
 tensorflow = { version = "^2.16.1", markers = "sys_platform == 'linux' or sys_platform == 'darwin'" }
 # TODO: the marker is a temporary solution due to the lack of the tensorflow-io 0.32.0's wheels for Windows, this package is one of
 # the tensorflow requirements
+torch = "^2.2.0"
+jax = "^0.4.25"
 qibo = "^0.2.6"
 
 [tool.poetry.group.dev]
diff --git a/src/qiboml/backends/jax.py b/src/qiboml/backends/jax.py
new file mode 100644
index 0000000..3945c76
--- /dev/null
+++ b/src/qiboml/backends/jax.py
@@ -0,0 +1,126 @@
+from qibo import __version__
+from qibo.backends import einsum_utils
+from qibo.backends.numpy import NumpyBackend
+from qibo.config import raise_error
+
+
+class JaxBackend(NumpyBackend):
+    def __init__(self):
+        super().__init__()
+        self.name = "jax"
+
+        import jax
+        import jax.numpy as jnp  # pylint: disable=import-error
+        import numpy
+
+        jax.config.update("jax_enable_x64", True)
+
+        self.jax = jax
+        self.numpy = numpy
+
+        self.np = jnp
+        self.tensor_types = (jnp.ndarray, numpy.ndarray)
+
+    def set_precision(self, precision):
+        if precision != self.precision:
+            if precision == "single":
+                self.precision = precision
+                self.dtype = self.np.complex64
+            elif precision == "double":
+                self.precision = precision
+                self.dtype = self.np.complex128
+            else:
+                raise_error(ValueError, f"Unknown precision {precision}.")
+            if self.matrices:
+                self.matrices = self.matrices.__class__(self.dtype)
+
+    def cast(self, x, dtype=None, copy=False):
+        if dtype is None:
+            dtype = self.dtype
+        if isinstance(x, self.tensor_types):
+            return x.astype(dtype)
+        elif self.issparse(x):
+            return x.astype(dtype)
+        return self.np.array(x, dtype=dtype, copy=copy)
+
+    # TODO: using numpy's rng for now. Shall we use Jax's?
+    def set_seed(self, seed):
+        self.numpy.random.seed(seed)
+
+    def sample_shots(self, probabilities, nshots):
+        return self.numpy.random.choice(
+            range(len(probabilities)), size=nshots, p=probabilities
+        )
+
+    def zero_state(self, nqubits):
+        state = self.np.zeros(2**nqubits, dtype=self.dtype)
+        state = state.at[0].set(1)
+        return state
+
+    def zero_density_matrix(self, nqubits):
+        state = self.np.zeros(2 * (2**nqubits,), dtype=self.dtype)
+        state = state.at[0, 0].set(1)
+        return state
+
+    def plus_state(self, nqubits):
+        state = self.np.ones(2**nqubits, dtype=self.dtype)
+        state /= self.np.sqrt(2**nqubits)
+        return state
+
+    def plus_density_matrix(self, nqubits):
+        state = self.np.ones(2 * (2**nqubits,), dtype=self.dtype)
+        state /= 2**nqubits
+        return state
+
+    def update_frequencies(self, frequencies, probabilities, nsamples):
+        samples = self.sample_shots(probabilities, nsamples)
+        res, counts = self.np.unique(samples, return_counts=True)
+        frequencies = frequencies.at[res].add(counts)
+        return frequencies
+
+    def apply_gate_density_matrix(self, gate, state, nqubits):
+        state = self.cast(state)
+        state = self.np.reshape(state, 2 * nqubits * (2,))
+        matrix = gate.matrix(self)
+        if gate.is_controlled_by:
+            matrix = self.np.reshape(matrix, 2 * len(gate.target_qubits) * (2,))
+            matrixc = self.np.conj(matrix)
+            ncontrol = len(gate.control_qubits)
+            nactive = nqubits - ncontrol
+            n = 2**ncontrol
+
+            order, targets = einsum_utils.control_order_density_matrix(gate, nqubits)
+            state = self.np.transpose(state, order)
+            state = self.np.reshape(state, 2 * (n,) + 2 * nactive * (2,))
+
+            leftc, rightc = einsum_utils.apply_gate_density_matrix_controlled_string(
+                targets, nactive
+            )
+            state01 = state[: n - 1, n - 1]
+            state01 = self.np.einsum(rightc, state01, matrixc)
+            state10 = state[n - 1, : n - 1]
+            state10 = self.np.einsum(leftc, state10, matrix)
+
+            left, right = einsum_utils.apply_gate_density_matrix_string(
+                targets, nactive
+            )
+            state11 = state[n - 1, n - 1]
+            state11 = self.np.einsum(right, state11, matrixc)
+            state11 = self.np.einsum(left, state11, matrix)
+
+            state00 = state[: n - 1]
+            state00 = state00[:, tuple(range(n - 1))]
+            state01 = self.np.concatenate([state00, state01[:, None]], axis=1)
+            state10 = self.np.concatenate([state10, state11[None]], axis=0)
+            state = self.np.concatenate([state01, state10[None]], axis=0)
+            state = self.np.reshape(state, 2 * nqubits * (2,))
+            state = self.np.transpose(state, einsum_utils.reverse_order(order))
+        else:
+            matrix = self.np.reshape(matrix, 2 * len(gate.qubits) * (2,))
+            matrixc = self.np.conj(matrix)
+            left, right = einsum_utils.apply_gate_density_matrix_string(
+                gate.qubits, nqubits
+            )
+            state = self.np.einsum(right, state, matrixc)
+            state = self.np.einsum(left, state, matrix)
+        return self.np.reshape(state, 2 * (2**nqubits,))
diff --git a/src/qiboml/backends/pytorch.py b/src/qiboml/backends/pytorch.py
new file mode 100644
index 0000000..7521194
--- /dev/null
+++ b/src/qiboml/backends/pytorch.py
@@ -0,0 +1,184 @@
+"""PyTorch backend."""
+
+import numpy as np
+from qibo import __version__
+from qibo.backends.npmatrices import NumpyMatrices
+from qibo.backends.numpy import NumpyBackend
+
+
+class TorchMatrices(NumpyMatrices):
+    """Matrix representation of every gate as a torch Tensor."""
+
+    def __init__(self, dtype):
+        import torch  # pylint: disable=import-outside-toplevel
+
+        super().__init__(dtype)
+        self.torch = torch
+        self.dtype = dtype
+
+    def _cast(self, x, dtype):
+        return self.torch.as_tensor(x, dtype=dtype)
+
+    def Unitary(self, u):
+        return self._cast(u, dtype=self.dtype)
+
+
+class PyTorchBackend(NumpyBackend):
+    def __init__(self):
+        super().__init__()
+        import torch  # pylint: disable=import-outside-toplevel
+
+        self.np = torch
+
+        self.name = "pytorch"
+        self.versions = {
+            "qibo": __version__,
+            "numpy": np.__version__,
+            "torch": self.np.__version__,
+        }
+
+        self.dtype = self._torch_dtype(self.dtype)
+        self.matrices = TorchMatrices(self.dtype)
+        self.device = self.np.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.nthreads = 0
+        self.tensor_types = (self.np.Tensor, np.ndarray)
+
+        # These functions in Torch works in a different way than numpy or have different names
+        self.np.transpose = self.np.permute
+        self.np.expand_dims = self.np.unsqueeze
+        self.np.mod = self.np.remainder
+        self.np.right_shift = self.np.bitwise_right_shift
+
+    def _torch_dtype(self, dtype):
+        if dtype == "float":
+            dtype += "32"
+        return getattr(self.np, dtype)
+
+    def set_device(self, device):  # pragma: no cover
+        self.device = device
+
+    def cast(
+        self,
+        x,
+        dtype=None,
+        copy: bool = False,
+    ):
+        """Casts input as a Torch tensor of the specified dtype.
+
+        This method supports casting of single tensors or lists of tensors
+        as for the :class:`qibo.backends.PyTorchBackend`.
+
+        Args:
+            x (Union[torch.Tensor, list[torch.Tensor], np.ndarray, list[np.ndarray], int, float, complex]):
+                Input to be casted.
+            dtype (Union[str, torch.dtype, np.dtype, type]): Target data type.
+                If ``None``, the default dtype of the backend is used.
+                Defaults to ``None``.
+            copy (bool, optional): If ``True``, the input tensor is copied before casting.
+                Defaults to ``False``.
+        """
+        if dtype is None:
+            dtype = self.dtype
+        elif isinstance(dtype, type):
+            dtype = self._torch_dtype(dtype.__name__)
+        elif not isinstance(dtype, self.np.dtype):
+            dtype = self._torch_dtype(str(dtype))
+
+        if isinstance(x, self.np.Tensor):
+            x = x.to(dtype)
+        elif isinstance(x, list) and all(isinstance(row, self.np.Tensor) for row in x):
+            x = self.np.stack(x)
+        else:
+            x = self.np.tensor(x, dtype=dtype)
+
+        if copy:
+            return x.clone()
+
+        return x
+
+    def issparse(self, x):
+        if isinstance(x, self.np.Tensor):
+            return x.is_sparse
+
+        return super().issparse(x)
+
+    def to_numpy(self, x):
+        if isinstance(x, list):
+            return np.asarray([self.to_numpy(i) for i in x])
+
+        if isinstance(x, self.np.Tensor):
+            return x.numpy(force=True)
+
+        return x
+
+    def _order_probabilities(self, probs, qubits, nqubits):
+        """Arrange probabilities according to the given ``qubits`` ordering."""
+        if probs.dim() == 0:  # pragma: no cover
+            return probs
+        unmeasured, reduced = [], {}
+        for i in range(nqubits):
+            if i in qubits:
+                reduced[i] = i - len(unmeasured)
+            else:
+                unmeasured.append(i)
+        return self.np.transpose(probs, [reduced.get(i) for i in qubits])
+
+    def calculate_probabilities(self, state, qubits, nqubits):
+        rtype = self.np.real(state).dtype
+        unmeasured_qubits = tuple(i for i in range(nqubits) if i not in qubits)
+        state = self.np.reshape(self.np.abs(state) ** 2, nqubits * (2,))
+        if len(unmeasured_qubits) == 0:
+            probs = self.cast(state, dtype=rtype)
+        else:
+            probs = self.np.sum(self.cast(state, dtype=rtype), axis=unmeasured_qubits)
+        return self._order_probabilities(probs, qubits, nqubits).ravel()
+
+    def set_seed(self, seed):
+        self.np.manual_seed(seed)
+        np.random.seed(seed)
+
+    def sample_shots(self, probabilities, nshots):
+        return self.np.multinomial(
+            self.cast(probabilities, dtype="float"), nshots, replacement=True
+        )
+
+    def calculate_eigenvalues(self, matrix, k=6):
+        return self.np.linalg.eigvalsh(matrix)  # pylint: disable=not-callable
+
+    def calculate_eigenvectors(self, matrix, k=6):
+        return self.np.linalg.eigh(matrix)  # pylint: disable=not-callable
+
+    def calculate_matrix_exp(self, a, matrix, eigenvectors=None, eigenvalues=None):
+        if eigenvectors is None or self.issparse(matrix):
+            return self.np.linalg.matrix_exp(  # pylint: disable=not-callable
+                -1j * a * matrix
+            )
+        expd = self.np.diag(self.np.exp(-1j * a * eigenvalues))
+        ud = self.np.conj(eigenvectors).T
+        return self.np.matmul(eigenvectors, self.np.matmul(expd, ud))
+
+    def test_regressions(self, name):
+        if name == "test_measurementresult_apply_bitflips":
+            return [
+                [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],
+                [0, 0, 0, 0, 2, 3, 0, 0, 0, 0],
+                [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                [0, 0, 0, 0, 2, 0, 0, 0, 0, 0],
+            ]
+
+        if name == "test_probabilistic_measurement":
+            if self.device == "cuda":  # pragma: no cover
+                return {0: 273, 1: 233, 2: 242, 3: 252}
+            return {1: 270, 2: 248, 3: 244, 0: 238}
+
+        if name == "test_unbalanced_probabilistic_measurement":
+            if self.device == "cuda":  # pragma: no cover
+                return {0: 196, 1: 153, 2: 156, 3: 495}
+            return {3: 492, 2: 176, 0: 168, 1: 164}
+
+        if name == "test_post_measurement_bitflips_on_circuit":
+            return [
+                {5: 30},
+                {5: 17, 4: 5, 7: 4, 1: 2, 6: 2},
+                {4: 9, 2: 5, 5: 5, 3: 4, 6: 4, 0: 1, 1: 1, 7: 1},
+            ]