Merge branch 'main' into issue_51_replace_qiskit_ibm_provider

jpmorganchase · May 10, 2024 · de148ef · de148ef
2 parents 7631531 + 9cb8259
commit de148ef
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diff --git a/.github/workflows/qokit-python-only.yml b/.github/workflows/qokit-python-only.yml
@@ -20,20 +20,6 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-###### Alex: to be used in future version once permissions issue is resolved
-#     - name: black
-#       uses: lgeiger/black-action@master
-#       with:
-#           args: .      
-#     - name: Apply black formatting if reviewdog found formatting errors
-#       if: "${{ failure() }}"
-#       run: |
-#          black .
-#     - name: Commit black formatting results
-#       if: "${{ failure() }}"
-#       uses: stefanzweifel/git-auto-commit-action@v4
-#       with:
-#          commit_message: ":art: Format Python code with psf/black push"
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v3
       with:
@@ -47,7 +33,8 @@ jobs:
           black --check .
     - name: pip install   
       run: |
-        QOKIT_PYTHON_ONLY=1 pip install .
+        export QOKIT_PYTHON_ONLY=True 
+        pip install .
     - name: Run tests and coverage
       run: |
          pytest --cov=qokit -rs tests --cov-fail-under=70

diff --git a/examples/QAOA_portfolio_optimization.ipynb b/examples/QAOA_portfolio_optimization.ipynb
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "c3de09e7-5315-4170-8263-6041a7819fac",
    "metadata": {
     "tags": []
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "57544f68-6538-4dda-a47b-254252caa54a",
    "metadata": {
     "tags": []
@@ -67,12 +67,12 @@
    "source": [
     "po_problem = get_problem(N=6,K=3,q=0.5,seed=1,pre=1)\n",
     "means_in_spins = np.array([po_problem['means'][i] - po_problem['q'] * np.sum(po_problem['cov'][i, :]) for i in range(len(po_problem['means']))])\n",
-    "scale = 1 / (np.sqrt(np.mean((( po_problem['q']*po_problem['cov'])**2).flatten()))+np.sqrt(np.mean((means_in_spins**2).flatten())))"
+    "scale = 1 / np.sqrt(np.mean((( po_problem['q']*po_problem['cov'])**2).flatten())+np.mean((means_in_spins**2).flatten()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "e49f9152-e49c-4cbd-a2b3-20894a927a9a",
    "metadata": {
     "tags": []
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a800d2c6-d7a9-449a-befc-e1620676847e",
    "metadata": {
     "tags": []
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "60942736-20e4-4519-a584-65f613b7bddf",
    "metadata": {
     "tags": []
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "c4cb1e0a-f0eb-48e4-a1dc-8d342462c156",
    "metadata": {
     "tags": []
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "1fa46296-89cb-40e7-aaf8-09b708eda12d",
    "metadata": {
     "tags": []
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "4bb70cb8-db79-45b3-b808-96b7d6647b68",
    "metadata": {
     "tags": []
@@ -158,12 +158,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "238479eb-db75-4e19-a159-36a2bed9c2ae",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy = 0.004782805426149568, Approximation ratio = 0.767014699611637\n"
+     ]
+    }
+   ],
    "source": [
     "po_energy = qaoa_obj(x0).real\n",
     "po_ar = (po_energy-best_portfolio[1])/(best_portfolio[0]-best_portfolio[1])\n",
@@ -184,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "9d730369-3d9a-4e0e-bd2b-a81d92baa7b7",
    "metadata": {
     "tags": []
@@ -197,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "55a761e4-2122-4b00-bc1f-39730d6a3dd7",
    "metadata": {
     "tags": []
@@ -224,12 +232,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "86b7ff7f-43f4-4242-95b9-82784f0632a5",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy = 0.004135567715727546, Approximation ratio = 0.8050399835274252\n"
+     ]
+    }
+   ],
    "source": [
     "_, opt_energy = minimize_nlopt(qaoa_obj, x0, p=1, rhobeg=0.01/1)\n",
     "opt_ar = (opt_energy-best_portfolio[1])/(best_portfolio[0]-best_portfolio[1])\n",
@@ -238,12 +254,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "90c5c6af-003c-4724-a943-c747bd24f3c6",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy = 0.004135567763346608, Approximation ratio = 0.8050399807298003\n"
+     ]
+    }
+   ],
    "source": [
     "res = minimize(qaoa_obj, x0, method='COBYLA', options={'rhobeg':0.001})\n",
     "print(f\"energy = {res.fun}, Approximation ratio = {(res.fun-best_portfolio[1])/(best_portfolio[0]-best_portfolio[1])}\")"
@@ -259,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "0c6e7780-0392-4a9f-b0a7-4b23c0af84dc",
    "metadata": {
     "tags": []
@@ -271,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "d5fec9f4-09e0-409f-a613-07f23194c94d",
    "metadata": {
     "tags": []
@@ -300,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "bc26bf47-72ce-4944-9a9f-d217e4c77894",
    "metadata": {
     "tags": []
@@ -314,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "0c4f35dc-9402-48ce-89ef-c5a80883aaf8",
    "metadata": {
     "tags": []
@@ -329,12 +353,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "8581ac1f-2675-4ccb-b311-ee7e658b2782",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_25329/3897112670.py:2: DeprecationWarning: The method ``qiskit.circuit.quantumcircuit.QuantumCircuit.bind_parameters()`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. Use assign_parameters() instead\n",
+      "  qc2 = parameterized_qc.bind_parameters(np.hstack([x0[p:]/2, x0[:p]/2]))\n"
+     ]
+    }
+   ],
    "source": [
     "parameterized_qc = get_parameterized_qaoa_circuit(po_problem, depth=p)\n",
     "qc2 = parameterized_qc.bind_parameters(np.hstack([x0[p:]/2, x0[:p]/2]))\n",
@@ -345,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "2ceed19a-9391-4eed-95b9-dbfc3b8d6a9d",
    "metadata": {
     "tags": []

diff --git a/qokit/portfolio_optimization.py b/qokit/portfolio_optimization.py
@@ -157,8 +157,8 @@ def get_problem(N, K, q, seed=1, pre=False) -> dict[str, Any]:
     po_problem["pre"] = pre
     if pre == "rule":
         means_in_spins = np.array([po_problem["means"][i] - q * np.sum(po_problem["cov"][i, :]) for i in range(len(po_problem["means"]))])
-        scale = 1 / (np.sqrt(np.mean(((q * po_problem["cov"]) ** 2).flatten())) + np.sqrt(np.mean((means_in_spins**2).flatten())))
-        # scale = 1 / (0.5*(np.sqrt(np.mean((po_problem['cov']**2).flatten()))+np.sqrt(np.mean((po_problem['means']**2).flatten()))))
+        scale = 1 / np.sqrt(np.mean(((q * po_problem["cov"]) ** 2).flatten()) + np.mean((means_in_spins**2).flatten()))
+        # scale = 1 / (0.5*(np.sqrt(np.mean((po_problem['cov']**2).flatten())+np.mean((po_problem['means']**2).flatten())))
     elif np.isscalar(pre):
         scale = pre
     else:

diff --git a/tests/test_qaoa_objective_maxcut.py b/tests/test_qaoa_objective_maxcut.py
@@ -165,3 +165,29 @@ def test_sk_ini_maxcut(simulator):
             else:
                 assert cur_ar > last_ar
                 last_ar = cur_ar
+
+
+@pytest.mark.parametrize("simulator", simulators_to_run_names_no_qiskit)
+def test_overlap_maxcut(simulator):
+    N = 4
+    d = 3
+    seed = 1
+    G = nx.random_regular_graph(d, N, seed=seed)
+    p = 1
+    beta = [np.random.uniform(0, 1)]
+    gamma = [np.random.uniform(0, 1)]
+
+    obj = partial(maxcut_obj, w=get_adjacency_matrix(G))
+    precomputed_energies = precompute_energies(obj, N)
+
+    f1 = get_qaoa_maxcut_objective(N, p, precomputed_cuts=precomputed_energies, parameterization="gamma beta", objective="overlap")
+    f2 = get_qaoa_maxcut_objective(N, p, G=G, parameterization="gamma beta", objective="overlap")
+
+    assert np.isclose(f1(gamma, beta), f2(gamma, beta))
+    assert np.isclose(f1([0], [0]), f2([0], [0]))
+
+    maxval = precomputed_energies.max()
+    bitstring_loc = (precomputed_energies == maxval).nonzero()
+    assert len(bitstring_loc) == 1
+    bitstring_loc = bitstring_loc[0]
+    assert np.isclose(1 - f1([0], [0]), len(bitstring_loc) / len(precomputed_energies))