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--- calcoloscientifico:cluster:softwareapplicativo:conda [03/07/2024 17:34] – fabio.spataro
+++ calcoloscientifico:cluster:softwareapplicativo:conda [05/07/2024 10:04] (versione attuale) – federico.prost
@@ Linea 652: / Linea 652: @@
 conda deactivate
+</code>
+==== Virtualenv "pennylane" ====
+Available versions of the "pennylane" environment:
+^ condaenv        ^ **build date**  ^
+| pennylane       | 2023.11.03      |
+| pennylane-gpu   | 2023.11.03      |
+Enable the default version of the "pennylane" environment:
+<code>
+module load miniconda3
+source "$CONDA_PREFIX/etc/profile.d/conda.sh"
+conda activate pennylane
+</code>
+Enable the gpu version of the "pennylane" environment:
+<code>
+module load miniconda3
+source "$CONDA_PREFIX/etc/profile.d/conda.sh"
+conda activate pennylane-gpu
+</code>
+Packages in the "pennylane" environment (any version):
+<code>
+conda list
+</code>
+Disable the "pennylane" environment (any version):
+<code>
+conda deactivate
+</code>
+Script ''slurm-pennylane-gpu.sh'':
+<code bash slurm-pennylane-gpu.sh>
+#!/bin/bash
+#SBATCH --job-name=plExample
+#SBATCH --output=%x.o%j
+#SBATCH --error=%x.e%j
+#SBATCH --nodes=1
+#SBATCH --partition=gpu
+#SBATCH --qos=gpu
+#SBATCH --gres=gpu:a100_40g:1
+#SBATCH --mem=8G
+#SBATCH --time=0-10:00:00
+#SBATCH --ntasks-per-node 4
+#SBATCH --account=<account>
+module load miniconda3
+source "$CONDA_PREFIX/etc/profile.d/conda.sh"
+conda activate pennylane-gpu
+PYTHON_VERSION=$(python -V | awk '{ print $2 }')
+echo $PYTHON_VERSION
+python -u ./plExample.py
+conda deactivate
+</code>
+Script ''plExample.py'':
+<code pyton plExample.py>
+#!/usr/bin/env python3
+# Simple example where we train a parameterized quantum circuit
+# to minimize the expectation value of a Pauli-Z operator.
+import pennylane as qml
+from pennylane import numpy as np
+# Define a quantum device with one qubit
+dev = qml.device("default.qubit", wires=1)
+# Define a variational quantum circuit
+@qml.qnode(dev)
+def circuit(params):
+    qml.RX(params[0], wires=0)  # Apply a rotation around the X-axis
+    qml.RY(params[1], wires=0)  # Apply a rotation around the Y-axis
+    return qml.expval(qml.PauliZ(0))
+# Define the cost function to minimize
+def cost(params):
+    return circuit(params)
+# Initialize parameters
+init_params = np.array([0.01, 0.01], requires_grad=True)
+# Set up the optimizer
+opt = qml.GradientDescentOptimizer(stepsize=0.1)
+# Number of optimization steps
+steps = 100
+# Optimize the circuit parameters
+params = init_params
+for i in range(steps):
+    params = opt.step(cost, params)
+    if (i + 1) % 10 == 0:
+        print(f"Step {i + 1}: Cost = {cost(params):.6f}, Params = {params}")
+print(f"Optimized Parameters: {params}")
+print(f"Final Cost: {cost(params)}")
 </code>