diff --git a/recirq/random_circuit_sampling/rcs_experiment.py b/recirq/random_circuit_sampling/rcs_experiment.py
index 0715e577..021ccd41 100644
--- a/recirq/random_circuit_sampling/rcs_experiment.py
+++ b/recirq/random_circuit_sampling/rcs_experiment.py
@@ -83,6 +83,7 @@ def characterize_pairs(
     results = xeb_characterize.calibrate_z_phases(
         sampler=sampler,
         qubits=qubits,
+        two_qubit_gate= gate,
         options=options,
     )
 
diff --git a/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb b/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb
new file mode 100644
index 00000000..3cc8edb5
--- /dev/null
+++ b/recirq/random_circuit_sampling/rcs_experiment_demonstration.ipynb
@@ -0,0 +1,497 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Parallel Random Circuit Sampling (RCS) & XEB Analysis\n",
+        "\n",
+        "This notebook serves as an end-to-end tutorial for generating, executing, and analyzing Random Circuit Sampling (RCS) experiments using the framework within ReCirq. Because full-chip quantum simulations are computationally expensive, this guide demonstrates how to use local verification on smaller sub-grids to calculate overall system performance for noisy simulation.\n",
+        "\n",
+        "Throughout this notebook, we will walk through the following core workflow:\n",
+        "\n",
+        "* **Defining Qubit Patches:** Partitioning a larger system into disjoint, non-overlapping sub-grids.\n",
+        "* **Configuring the Noiseless Simulation:** Executing a noiseless simulation to establish ideal Cross-Entropy Benchmarking (XEB) fidelities.\n",
+        "* **Configuring the Noisy Simulation via QVM:** Utilizing the Quantum Virtual Machine configured with realistic device noise properties to simulate actual hardware execution.\n",
+        "* **Analyzing & Visualizing Results:** Calculating individual patch fidelities and total system fidelity for noisy simulations, and plotting the results to show fidelity decay caused by accumulated noise."
+      ],
+      "metadata": {
+        "id": "TKUuvR37up68"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To run this notebook in Google Colab, uncomment and run the following line:\n",
+        "#!pip install cirq cirq_google qsimcirq\n",
+        "\n",
+        "# To install the local 'recirq' package from the repository:\n",
+        "# 1. Clone the repo: !git clone https://github.com/quantumlib/ReCirq.git\n",
+        "# 2. Install: !pip install -e ReCirq/\n",
+        "# 3. IMPORTANT: Restart the Colab runtime (Runtime > Restart session) after installation."
+      ],
+      "metadata": {
+        "id": "T4JCu5Uj5zfK"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Imports"
+      ],
+      "metadata": {
+        "id": "RMz52FOW-8DO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cirq\n",
+        "import cirq_google\n",
+        "import qsimcirq\n",
+        "import recirq.random_circuit_sampling as rcs\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np"
+      ],
+      "metadata": {
+        "id": "dKWboTyH-ssA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Noiseless Simulation"
+      ],
+      "metadata": {
+        "id": "CsaS50g9COir"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Define the patches\n",
+        "\n",
+        "Patches serve as a tool for local verification and benchmarking. By partitioning the processor into non-overlapping sub-grids, we can use classical simulators to compute exact ideal states that would be computationally impossible at full-chip scale. This also allows us to characterize local noise profiles within specific hardware regions. Finally, we can estimate the overall fidelity of the larger system by calculating the product of the fidelities from these independent patches.\n",
+        "\n",
+        "Select sets of connected qubits to serve as patches. These patches must be disjoint and should not share any common qubits."
+      ],
+      "metadata": {
+        "id": "tGtt-E53Acam"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "patch_1 = cirq.GridQubit.rect(4, 3, top=0, left=0)\n",
+        "patch_2 = cirq.GridQubit.rect(4, 3, top=0, left=3)\n",
+        "patch_3 = cirq.GridQubit.rect(4, 3, top=4, left=0)\n",
+        "patches = [patch_1, patch_2, patch_3]"
+      ],
+      "metadata": {
+        "id": "kK-PGBsNAcAT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Set up and run the experiment\n",
+        "\n",
+        "Specify the qubit patches and the desired circuit depths for XEB calculation. Additionally, define the number of random circuit instances per configuration and the specific tiling pattern for the experiment. While the framework supports characterizing the $fSim$ gate parameters ($\\theta, \\phi, \\zeta, \\chi, \\gamma$), this step is bypassed here as we are performing an ideal noiseless simulation."
+      ],
+      "metadata": {
+        "id": "c0zvsbl9AnSx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "experiment = rcs.RCSExperiment(\n",
+        "    patches=patches,\n",
+        "    depths=[30, 50, 70, 90],\n",
+        "    num_instances=3,\n",
+        "    pattern_name=\"staggered\",\n",
+        "    seed= 2026\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "YCL7PrIoAfmW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = experiment.run(sampler=cirq.Simulator(), n_repetitions=10000, characterize=False)"
+      ],
+      "metadata": {
+        "id": "0yCdLPwQAqut"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate linear XEB fidelities\n",
+        "\n",
+        "To calculate fidelities via noiseless simulation, we can employ the standard `cirq.Simulator()`. Here, we use the performance-optimized `qsimcirq.QSimSimulator()` to improve execution efficiency.\n"
+      ],
+      "metadata": {
+        "id": "sgg6y0xU2hZJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fidelities = results.fidelities_lin(simulator=qsimcirq.QSimSimulator())"
+      ],
+      "metadata": {
+        "id": "lIKch40S1fX9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Plot the results\n",
+        "\n",
+        "Visualize the XEB results for each patch across the specified circuit depths. The plot reflects the mean fidelity calculated across all random circuit instances for each (patch, depth) configuration.\n",
+        "\n",
+        "Note on ideal vs. noisy simulation: You might notice that the plotted linear XEB fidelity is flat at around 1.0 across all circuit depths. This behavior is exactly what we expect from an ideal, noiseless simulation. In a real quantum hardware experiment (or a noisy simulation), incoherent errors and decoherence accumulate at each gate layer, resulting in a characteristic exponential decay curve as the circuit gets deeper. Since our simulated qubits do not suffer from any noise, the fidelity remains at ~1.0 (with microscopic fluctuations resulting purely from finite sampling variance across our 10,000 repetitions). See the next section for noisy simulation."
+      ],
+      "metadata": {
+        "id": "OGrm6xgBAxR4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8, 6))\n",
+        "\n",
+        "colors = ['olive', 'gold', 'blue']\n",
+        "\n",
+        "total_samples_noiseless = experiment.num_instances * 10000\n",
+        "err_val_noiseless = 1.0 / np.sqrt(total_samples_noiseless)\n",
+        "\n",
+        "means_matrix_noiseless = np.array([[np.mean(fidelities[(i, d)]) for d in experiment.depths] for i in range(len(patches))])\n",
+        "errs_matrix_noiseless = np.full_like(means_matrix_noiseless, err_val_noiseless)\n",
+        "\n",
+        "for i in range(len(patches)):\n",
+        "    ax.errorbar(\n",
+        "        experiment.depths,\n",
+        "        means_matrix_noiseless[i],\n",
+        "        yerr=errs_matrix_noiseless[i],\n",
+        "        marker='s',\n",
+        "        markersize=4,\n",
+        "        linewidth=2,\n",
+        "        label=f'Patch {i} (Ideal)',\n",
+        "        linestyle='--',\n",
+        "        capsize=5,\n",
+        "        color=colors[i]\n",
+        "    )\n",
+        "\n",
+        "ax.set_ylim(-0.1, 1.1)\n",
+        "ax.set_xlim(min(experiment.depths) - 2, max(experiment.depths) + 2)\n",
+        "ax.set_xlabel(r'Number of cycles, $m$', fontsize=14)\n",
+        "ax.set_ylabel('Cross-entropy benchmarking (XEB) fidelity', fontsize=14)\n",
+        "ax.set_title('Parallel RCS (Noiseless Simulation)', fontsize=16, pad=20)\n",
+        "ax.legend(frameon=False, fontsize=12)\n",
+        "ax.tick_params(\n",
+        "    axis='both',\n",
+        "    direction='in',\n",
+        "    top=True,\n",
+        "    right=True,\n",
+        "    labelsize=12,\n",
+        "    width=1.2,\n",
+        "    length=6\n",
+        ")\n",
+        "for spine in ax.spines.values():\n",
+        "    spine.set_linewidth(1.2)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "iJpv_xBQqUwy",
+        "outputId": "55c9e86c-6414-48c2-db99-2c557f6970bb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Noisy simulation with quantum virtual machine\n",
+        "\n",
+        "We perform noisy simulations using the [Quantum Virtual Machine (QVM)](https://quantumai.google/cirq/simulate/quantum_virtual_machine) to account for device-specific connectivity and calibration-based noise profiles. The resulting XEB fidelity plot demonstrates the fidelity decay as circuit depth increases, reflecting the accumulation of errors in a noisy system."
+      ],
+      "metadata": {
+        "id": "zYOjavtjngyq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "processor_id = \"weber\"\n",
+        "noise_props = cirq_google.engine.load_device_noise_properties(processor_id)\n",
+        "noise_model = cirq_google.NoiseModelFromGoogleNoiseProperties(noise_props)\n",
+        "qvm_sampler = cirq.DensityMatrixSimulator(noise=noise_model)\n",
+        "device = cirq_google.engine.create_device_from_processor_id(processor_id)\n",
+        "print(device)"
+      ],
+      "metadata": {
+        "id": "ZlKNHGeU3pYc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4fa903c4-90e3-4460-923d-a19e0aa2e9bd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                             (0, 5)───(0, 6)\n",
+            "                                             │        │\n",
+            "                                             │        │\n",
+            "                                    (1, 4)───(1, 5)───(1, 6)───(1, 7)\n",
+            "                                    │        │        │        │\n",
+            "                                    │        │        │        │\n",
+            "                                    (2, 4)───(2, 5)───(2, 6)───(2, 7)───(2, 8)\n",
+            "                                    │        │        │        │        │\n",
+            "                                    │        │        │        │        │\n",
+            "                  (3, 2)───(3, 3)───(3, 4)───(3, 5)───(3, 6)───(3, 7)───(3, 8)───(3, 9)\n",
+            "                  │        │        │        │        │        │        │        │\n",
+            "                  │        │        │        │        │        │        │        │\n",
+            "         (4, 1)───(4, 2)───(4, 3)───(4, 4)───(4, 5)───(4, 6)───(4, 7)───(4, 8)───(4, 9)\n",
+            "         │        │        │        │        │        │        │        │\n",
+            "         │        │        │        │        │        │        │        │\n",
+            "(5, 0)───(5, 1)───(5, 2)───(5, 3)───(5, 4)───(5, 5)───(5, 6)───(5, 7)───(5, 8)\n",
+            "         │        │        │        │        │        │        │\n",
+            "         │        │        │        │        │        │        │\n",
+            "         (6, 1)───(6, 2)───(6, 3)───(6, 4)───(6, 5)───(6, 6)───(6, 7)\n",
+            "                  │        │        │        │        │\n",
+            "                  │        │        │        │        │\n",
+            "                  (7, 2)───(7, 3)───(7, 4)───(7, 5)───(7, 6)\n",
+            "                           │        │        │\n",
+            "                           │        │        │\n",
+            "                           (8, 3)───(8, 4)───(8, 5)\n",
+            "                                    │\n",
+            "                                    │\n",
+            "                                    (9, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "patch_1_on_device = [\n",
+        "    cirq.GridQubit(4, 1), cirq.GridQubit(4, 2), cirq.GridQubit(4, 3),\n",
+        "    cirq.GridQubit(5, 1), cirq.GridQubit(5, 2), cirq.GridQubit(5, 3),\n",
+        "    cirq.GridQubit(6, 1), cirq.GridQubit(6, 2), cirq.GridQubit(6, 3)\n",
+        "]\n",
+        "\n",
+        "patch_2_on_device = [\n",
+        "    cirq.GridQubit(1, 4), cirq.GridQubit(1, 5), cirq.GridQubit(1, 6),\n",
+        "    cirq.GridQubit(2, 4), cirq.GridQubit(2, 5), cirq.GridQubit(2, 6),\n",
+        "    cirq.GridQubit(3, 4), cirq.GridQubit(3, 5), cirq.GridQubit(3, 6)\n",
+        "]\n",
+        "\n",
+        "patch_3_on_device = [\n",
+        "    cirq.GridQubit(4, 4), cirq.GridQubit(4, 5), cirq.GridQubit(4, 6),\n",
+        "    cirq.GridQubit(5, 4), cirq.GridQubit(5, 5), cirq.GridQubit(5, 6),\n",
+        "    cirq.GridQubit(6, 4), cirq.GridQubit(6, 5), cirq.GridQubit(6, 6)\n",
+        "]\n",
+        "\n",
+        "patches_on_device = [patch_1_on_device, patch_2_on_device, patch_3_on_device]"
+      ],
+      "metadata": {
+        "id": "q8KMRMCBSm9S"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "noisy_experiment = rcs.RCSExperiment(\n",
+        "    patches=patches_on_device,\n",
+        "    depths=[10, 12, 14, 16, 18, 20],\n",
+        "    num_instances=3,\n",
+        "    pattern_name=\"staggered\",\n",
+        "    seed= 2026\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "RR1EM-TJSkyj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n_repetitions = 10000\n",
+        "noisy_results = noisy_experiment.run(\n",
+        "    sampler = qvm_sampler,\n",
+        "    n_repetitions = n_repetitions\n",
+        ")\n",
+        "\n",
+        "noisy_fidelities = noisy_results.fidelities_lin(simulator=qsimcirq.QSimSimulator())"
+      ],
+      "metadata": {
+        "id": "EFfllJNkPhip"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The figure below illustrates the measured linear XEB fidelities for the separate patches. It also shows the estimated total system fidelity computed by multiplying the fidelities of those individual patches."
+      ],
+      "metadata": {
+        "id": "FwKLP7EDiI4T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(8, 6))\n",
+        "\n",
+        "colors = ['olive', 'gold', 'blue']\n",
+        "\n",
+        "depths = noisy_experiment.depths\n",
+        "num_patches = len(patches_on_device)\n",
+        "num_instances = noisy_experiment.num_instances\n",
+        "\n",
+        "total_samples = num_instances * n_repetitions\n",
+        "err_val = 1.0 / np.sqrt(total_samples)\n",
+        "\n",
+        "means_matrix = np.zeros((num_patches, len(depths)))\n",
+        "errs_matrix = np.zeros((num_patches, len(depths)))\n",
+        "\n",
+        "\n",
+        "for i in range(num_patches):\n",
+        "    y_means = []\n",
+        "    y_errs = []\n",
+        "    for j, d in enumerate(depths):\n",
+        "        fids = noisy_fidelities[(i, d)]\n",
+        "        mean_val = np.mean(fids)\n",
+        "        y_means.append(mean_val)\n",
+        "        y_errs.append(err_val)\n",
+        "        means_matrix[i, j] = mean_val\n",
+        "        errs_matrix[i, j] = err_val\n",
+        "    ax.errorbar(depths, y_means, yerr=y_errs, marker='s', markersize=4,\n",
+        "                linewidth=2, label=f'Patch {i}', linestyle='--', capsize=5, color = colors[i])\n",
+        "\n",
+        "\n",
+        "overall_means = []\n",
+        "overall_errs = []\n",
+        "\n",
+        "\n",
+        "for j in range(len(depths)):\n",
+        "    f_tot = np.prod(means_matrix[:, j])\n",
+        "    overall_means.append(f_tot)\n",
+        "    rel_errors_sq = (errs_matrix[:, j] / means_matrix[:, j])**2\n",
+        "    err_tot = f_tot * np.sqrt(np.sum(rel_errors_sq))\n",
+        "    overall_errs.append(err_tot)\n",
+        "\n",
+        "ax.errorbar(depths, overall_means, yerr=overall_errs, marker='o', markersize=4,\n",
+        "            linewidth=2, label='Overall Fidelity', linestyle='solid', color='black', capsize=5)\n",
+        "\n",
+        "ax.set_xlim(min(depths) - 2, max(depths) + 2)\n",
+        "ax.set_xlabel(r'Number of cycles, $m$', fontsize=14)\n",
+        "ax.set_ylabel('Cross-entropy benchmarking (XEB) fidelity', fontsize=14)\n",
+        "ax.set_ylim(0.0009, 1.1)\n",
+        "ax.set_xlim(9, 21)\n",
+        "ax.set_yscale('log')\n",
+        "ax.set_title('Parallel RCS (Noisy Simulation)', fontsize=16, pad=20)\n",
+        "ax.legend(frameon=False, fontsize=12)\n",
+        "ax.tick_params(\n",
+        "    axis='both',\n",
+        "    direction='in',\n",
+        "    top=True,\n",
+        "    right=True,\n",
+        "    labelsize=12,\n",
+        "    width=1.2,\n",
+        "    length=6\n",
+        ")\n",
+        "for spine in ax.spines.values():\n",
+        "    spine.set_linewidth(1.2)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "h4E1SOHfpkI3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "outputId": "d051b7ac-664c-49c4-c919-ba0dd5c56686"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Conclusion\n",
+        "In this tutorial, we successfully demonstrated the workflow for running a Random Circuit Sampling (RCS) experiment within the ReCirq framework. By dividing the quantum processor into independent, non-overlapping patches, we were able to efficiently estimate the performance of a larger system without the massive computational overhead of a large-system simulation.\n",
+        "\n",
+        "The final visualizations illustrate the expected behavior of noisy quantum execution: as the depth of the random circuits increases, the Cross-Entropy Benchmarking (XEB) fidelity decays. This decay reflects the accumulation of gate errors over time. Finally, this patching and cross-entropy analysis provides a scalable method for characterizing and benchmarking the performance of near-term quantum devices."
+      ],
+      "metadata": {
+        "id": "1cjY_M4b7ZiU"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/recirq/random_circuit_sampling/rcs_experiment_test.py b/recirq/random_circuit_sampling/rcs_experiment_test.py
index 378c2b10..b8f926f9 100644
--- a/recirq/random_circuit_sampling/rcs_experiment_test.py
+++ b/recirq/random_circuit_sampling/rcs_experiment_test.py
@@ -139,3 +139,41 @@ def test_get_calibrated_circuit():
                     cirq.is_measurement(op)]
     assert len(measurements) == 1
     assert measurements[0].qubits == (q0, q1)
+
+
+def test_characterize_pairs_ideal():
+    """Verifies that characterization on a noiseless simulator returns ideal angles."""
+    q0, q1 = cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)
+    qubits = [q0, q1]
+    sampler = cirq.Simulator()
+
+    theta = np.pi / 2
+    phi = np.pi / 6
+    base_gate = cirq.FSimGate(theta=theta, phi=phi)
+
+    char_results = rcs.characterize_pairs(
+        sampler=sampler,
+        qubits=qubits,
+        gate=base_gate,
+        theta=False,
+        phi=False,
+        zeta=True,
+        chi=True,
+        gamma=True
+    )
+
+
+    pair = tuple(sorted((q0, q1)))
+    assert pair in char_results
+
+    actual_gate = char_results[pair]
+    assert isinstance(actual_gate, cirq.PhasedFSimGate)
+
+    # Theta and phi angles should be close to the ideal values in ideal sim
+    assert actual_gate.theta == pytest.approx(theta, abs=1e-2)
+    assert actual_gate.phi == pytest.approx(phi, abs=1e-2)
+
+    # Z-phases (zeta, chi, gamma) should be effectively zero in ideal sim
+    assert actual_gate.zeta == pytest.approx(0, abs=1e-1)
+    assert actual_gate.chi == pytest.approx(0, abs=1e-1)
+    assert actual_gate.gamma == pytest.approx(0, abs=1e-1)