Notebook section shows difference of scheduling techniques in Pauli-Z…

… strategies
qiboteam · Jan 30, 2024 · 1948c01 · 1948c01
1 parent abddfae
commit 1948c01
Showing 1 changed file with 106 additions and 4 deletions.
diff --git a/examples/dbi/dbi_scheduling.ipynb b/examples/dbi/dbi_scheduling.ipynb
@@ -170,13 +170,16 @@
    "source": [
     "# grid_search\n",
     "step_grid = dbi.choose_step(scheduling=DoubleBracketScheduling.use_grid_search, step_max=0.6, d=d)\n",
-    "print('grid_search step:', step_grid)\n",
+    "grid_min = dbi.loss(step=step_grid, d=d)-dbi.off_diagonal_norm\n",
+    "print('grid_search step:', step_grid, 'loss', grid_min)\n",
     "# hyperopt\n",
     "step_hyperopt = dbi.choose_step(scheduling=DoubleBracketScheduling.use_hyperopt, d=d, max_evals=100, step_max=0.6)\n",
-    "print('hyperopt_search step:', step_hyperopt)\n",
+    "hyperopt_min = dbi.loss(step=step_hyperopt, d=d)-dbi.off_diagonal_norm\n",
+    "print('hyperopt_search step:', step_hyperopt, 'loss', hyperopt_min)\n",
     "# polynomial expansion\n",
     "step_poly = dbi.choose_step(scheduling=DoubleBracketScheduling.use_polynomial_approximation, d=d, n=5)\n",
-    "print('polynomial_approximation step:', step_poly)"
+    "poly_min = dbi.loss(step=step_poly, d=d)-dbi.off_diagonal_norm\n",
+    "print('polynomial_approximation step:', step_poly, 'loss', poly_min)"
    ]
   },
   {
@@ -188,6 +191,8 @@
     "# Plot the results\n",
     "plt.plot(s_space, off_diagonal_norm_diff)\n",
     "plt.axvline(x=step_grid, color='r', linestyle='-',label='grid_search')\n",
+    "plt.text(x=step_grid, y=grid_min, s=f'grid min \\n{round(grid_min,3)}')\n",
+    "plt.text(x=step_poly, y=poly_min, s=f'grid min \\n{round(poly_min,3)}')\n",
     "plt.axvline(x=step_hyperopt, color='g', linestyle='--',label='hyperopt')\n",
     "plt.axvline(x=step_poly, color='m', linestyle='-.',label='polynomial')\n",
     "plt.ylabel(r'$||\\sigma(H_0)||-\\sigma(H_k)||$')\n",
@@ -208,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Use polynomial expansion as an restriction of hyperopt/grid range"
+    "## Use polynomial expansion as an restriction for hyperopt/grid range"
    ]
   },
   {
@@ -256,6 +261,103 @@
    "source": [
     "Hence, we see that the strategy is indeed effective for finding the first minimum of the loss funciton for both the Z operator and the ZI operator."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare in Pauli-Z strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qibo.quantum_info import random_hermitian\n",
+    "from qibo.hamiltonians import Hamiltonian"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hamiltonian\n",
+    "set_backend(\"qibojit\", \"numba\")\n",
+    "nqubits = 4\n",
+    "h0 = random_hermitian(2**nqubits)\n",
+    "\n",
+    "# initialize class\n",
+    "dbi = DoubleBracketIteration(deepcopy(Hamiltonian(nqubits=nqubits, matrix=h0)),mode=DoubleBracketGeneratorType.single_commutator)\n",
+    "print(\"Initial off diagonal norm\", dbi.off_diagonal_norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generate_local_Z = generate_Z_operators(nqubits)\n",
+    "Z_ops = list(generate_local_Z.values())\n",
+    "Z_names = list(generate_local_Z.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NSTEPS = 8\n",
+    "scheduling_list = [DoubleBracketScheduling.use_grid_search,\n",
+    "                   DoubleBracketScheduling.use_hyperopt,\n",
+    "                   DoubleBracketScheduling.use_polynomial_approximation,]\n",
+    "scheduling_labels = ['grid search',\n",
+    "                     'hyperopt',\n",
+    "                     'polynomial',]\n",
+    "Z_optimal_scheduling = []\n",
+    "s_scheduling = []\n",
+    "off_norm_scheduling =[]\n",
+    "for i,scheduling in enumerate(scheduling_list):\n",
+    "    # reinitialize\n",
+    "    dbi = DoubleBracketIteration(Hamiltonian(nqubits=nqubits, matrix=deepcopy(h0)), mode=DoubleBracketGeneratorType.single_commutator)\n",
+    "    Z_optimal = []\n",
+    "    # add in initial values for plotting\n",
+    "    off_diagonal_norm_history = [dbi.off_diagonal_norm]\n",
+    "    steps = [0]\n",
+    "    print(f'----------Scheduling {scheduling_labels[i]}----------')\n",
+    "    for _ in range(NSTEPS):\n",
+    "        dbi, idx, step, flip_sign = select_best_dbr_generator(dbi, Z_ops, scheduling=scheduling, compare_canonical=False)\n",
+    "        off_diagonal_norm_history.append(dbi.off_diagonal_norm)\n",
+    "        steps.append(steps[-1]+step)\n",
+    "        if flip_sign < 0:\n",
+    "            Z_optimal.append('-' + Z_names[idx])\n",
+    "        else:\n",
+    "            Z_optimal.append(Z_names[idx])\n",
+    "        print(f\"New optimized step at iteration {_+1}/{NSTEPS}: {step} with operator {Z_optimal[-1]}, loss {dbi.off_diagonal_norm}\")\n",
+    "    Z_optimal_scheduling.append(Z_optimal)\n",
+    "    s_scheduling.append(steps)\n",
+    "    off_norm_scheduling.append(off_diagonal_norm_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure()\n",
+    "for i, scheduling in enumerate(scheduling_labels):\n",
+    "    plt.plot(s_scheduling[i], off_norm_scheduling[i], '-o', label=scheduling)\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.ylabel(\"Norm off-diagonal restriction\")\n",
+    "plt.title(\"Compare Variational Pauli-Z using different scheduling strategies\")\n",
+    "plt.legend()"
+   ]
   }
  ],
  "metadata": {