diff --git a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb index 06530b640..c1a6563d6 100644 --- a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb +++ b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb @@ -1,1040 +1,1040 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph optimization with QAOA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "n_nodes = 6\n", - "p = 0.5 # probability of an edge\n", - "seed = 1967\n", - "\n", - "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions, node_size=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", - "\n", - "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", - "\n", - "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", - "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", - "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", - "\n", - "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note This notebook requires PennyLane version 0.17 or above.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## QAOA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", - "\n", - "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", - "\n", - "\n", - "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", - "\n", - "
\n", - "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fixing the problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", - "\n", - "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cost Hamiltonian:\n", - " (-0.5) [Z1]\n", - "+ (-0.5) [Z5]\n", - "+ (0.25) [Z0]\n", - "+ (0.25) [Z4]\n", - "+ (0.25) [Z2]\n", - "+ (0.25) [Z3]\n", - "+ (0.75) [Z0 Z1]\n", - "+ (0.75) [Z1 Z4]\n", - "+ (0.75) [Z2 Z5]\n", - "+ (0.75) [Z3 Z5]\n", - "Mixer Hamiltonian:\n", - " (1) [X0]\n", - "+ (1) [X1]\n", - "+ (1) [X2]\n", - "+ (1) [X3]\n", - "+ (1) [X4]\n", - "+ (1) [X5]\n" - ] - } - ], - "source": [ - "import pennylane as qml\n", - "from pennylane import numpy as np\n", - "\n", - "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", - "# constrained=True results in greater circuit depth but potentially better solutions\n", - "\n", - "print(\"Cost Hamiltonian:\\n\", cost_h)\n", - "print(\"Mixer Hamiltonian:\\n\", mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin by setting up a single QAOA layer" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The full QAOA circuit is then given by:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 1\n", - "wires = n_nodes\n", - "\n", - "\n", - "def circuit(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note We have chosen to use a single QAOA layer. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are overall two controllable parameters: the first one is $\\gamma_i$ of the cost Hamiltonian and the one is $\\alpha_i$ of the mixer Hamiltonian:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.72511958],\n", - " [0.57312068]], requires_grad=True)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(1967)\n", - "params = np.random.uniform(size=[2, n_layers])\n", - "params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\"braket.local.qubit\", wires=wires)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", - "def cost_function(params, **kwargs):\n", - " circuit(params)\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the algorithm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.GradientDescentOptimizer()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 1.2873831170401528\n", - "Completed iteration 1, cost function: 1.0283347453164076\n", - "Completed iteration 2, cost function: 0.6850453114142451\n", - "Completed iteration 3, cost function: 0.26816570914692445\n", - "Completed iteration 4, cost function: -0.18397174104834577\n", - "Completed iteration 5, cost function: -0.6134512514217036\n", - "CPU times: user 9.66 s, sys: 3.35 ms, total: 9.66 s\n", - "Wall time: 9.66 s\n" - ] - } - ], - "source": [ - "%%time\n", - "print(\"Initial cost:\", cost_function(params))\n", - "\n", - "for i in range(5):\n", - " params = optimizer.step(cost_function, params)\n", - " cost_eval = cost_function(params)\n", - " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Investigating the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", - "\n", - "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "shots = 10_000\n", - "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", - "\n", - "\n", - "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", - "def samples(params):\n", - " circuit(params)\n", - " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Samples can now be generated and converted into probabilities:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import Counter\n", - "\n", - "s = samples(params).T\n", - "s = (1 - s.numpy()) / 2\n", - "s = map(tuple, s)\n", - "\n", - "counts = Counter(s)\n", - "indx = np.ndindex(*[2] * wires)\n", - "\n", - "probs = {p: counts.get(p, 0) / shots for p in indx}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now plot the probability distribution over all possible samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "plt.style.use(\"seaborn-v0_8-colorblind\")\n", - "labels = [\"{0:{fill}6b}\".format(i, fill='0') for i in range(len(probs))]\n", - "\n", - "plt.bar(range(2**wires), probs.values())\n", - "plt.xticks([i for i in range(len(probs))], labels, rotation=\"vertical\", size=12)\n", - "plt.yticks(size=12)\n", - "\n", - "plt.xlabel(\"Sample\", size=20)\n", - "plt.ylabel(\"Probability\", size=20)\n", - "\n", - "fig = plt.gcf()\n", - "fig.set_size_inches(16, 8)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sub = g.subgraph([0, 2, 3, 4])\n", - "nx.draw(g, pos=positions, with_labels=True)\n", - "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scaling up QAOA for larger graphs with hybrid jobs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a 18 node graph! We will use [Amazon Braket Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) to scale up the classical resources, and run the entire algorithm asynchronously. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", - "from braket.tracking import Tracker\n", - "\n", - "task_tracker = Tracker().start() # track Braket tasks costs" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import networkx as nx\n", - "\n", - "nodes = wires = 18\n", - "edges = 60\n", - "seed = 1967\n", - "\n", - "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", - "positions = nx.spring_layout(g, seed=seed)\n", - "\n", - "nx.draw(g, with_labels=True, pos=positions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A 18 node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization or adjoint differentiation. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", - "\n", - "Let's first load a new device:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from braket.devices import Devices\n", - "\n", - "device_arn = Devices.Amazon.SV1\n", - "# device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\" # alternatively use the device ARN" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "dev = qml.device(\"lightning.qubit\", wires=wires)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "cost_h, mixer_h = qml.qaoa.maxcut(g)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def qaoa_layer(gamma, alpha):\n", - " qml.qaoa.cost_layer(gamma, cost_h)\n", - " qml.qaoa.mixer_layer(alpha, mixer_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "n_layers = 2\n", - "\n", - "\n", - "@qml.qnode(dev)\n", - "def cost_function(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", - " return qml.expval(cost_h)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1967)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = qml.AdagradOptimizer(stepsize=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", - "\n", - "\n", - "We create a hybrid job by annotating our main function with `@hybrid_job`. \n", - "This allows us to choose a target QPU for priority queueing, and additional arguments such as the type of classical instances to use. \n", - "In this example, we use an \"ml.c5.xlarge\" instance. \n", - "\n", - "Note that creating hybrid jobs is only supported on Python 3.10. For other versions, you may use [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "from braket.jobs import hybrid_job, InstanceConfig\n", - "from braket.jobs.metrics import log_metric\n", - "\n", - "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", - "\n", - "\n", - "@hybrid_job(device=\"local:pennylane/lightning.qubit\", instance_config=large_instance)\n", - "def qaoa_training(iterations, n_layers=2):\n", - " task_tracker = Tracker().start() # track Braket tasks costs\n", - "\n", - " dev = qml.device(\"lightning.qubit\", wires=wires)\n", - "\n", - " @qml.qnode(dev)\n", - " def cost_function(params, **kwargs):\n", - " for i in range(wires): # Prepare an equal superposition over all qubits\n", - " qml.Hadamard(wires=i)\n", - "\n", - " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", - " return qml.expval(cost_h)\n", - "\n", - " params = 0.01 * np.random.uniform(size=[2, n_layers])\n", - "\n", - " for i in range(iterations):\n", - " params, cost = optimizer.step_and_cost(cost_function, params)\n", - "\n", - " # Record the value of the cost function with each iteration\n", - " log_metric(metric_name=\"cost\", value=cost, iteration_number=i)\n", - "\n", - " # Additionally, keep track of cost in USD for Braket tasks\n", - " braket_task_cost = float(\n", - " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", - " )\n", - " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", - "\n", - " return {\n", - " \"parameters\": params,\n", - " \"final_cost\": cost_function(params),\n", - " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the next cell, we create the hybrid job by calling the function as usual. The function arguments are logged as hyperparamters for the hybrid job. \n", - "\n", - "
\n", - "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qaoa-training-1697141494101')\n" - ] - } - ], - "source": [ - "job = qaoa_training(iterations=5, n_layers=2)\n", - "print(job)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The hybrid job will be scheduled to run and will appear in the \"QUEUED\" state. \n", - "If the target is a QPU, the hybrid job will be queued with other hybrid jobs. \n", - "If the target device is not a QPU, the hybrid job should start immediately. \n", - "\n", - "Note that since the algorithm code is run in a containerized environment, it takes approximately 1 minute to start running your algorithm. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'QUEUED'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job.state()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the state is \"COMPLETED\", we retrieve the results with " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 337 ms, sys: 18 ms, total: 355 ms\n", - "Wall time: 8min 54s\n" - ] - }, - { - "data": { - "text/plain": [ - "{'parameters': tensor([[-0.01460419, 0.00094966],\n", - " [ 0.0187241 , 0.00412996]], requires_grad=True),\n", - " 'final_cost': array(-30.03947181),\n", - " 'braket_tasks_cost': Decimal('0')}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "\n", - "job.result()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results included the three values from the return statement of our function.\n", - "\n", - "Additionally, we can retrieve the metrics recorded during the training with:" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iteration_numbertimestampcostbraket_cost
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" - ], - "text/plain": [ - " iteration_number timestamp cost braket_cost\n", - "0 0.0 3.394283e+09 -29.992536 0.0\n", - "1 1.0 3.394284e+09 -27.007548 0.0\n", - "2 2.0 3.394284e+09 -29.993231 0.0\n", - "3 3.0 3.394284e+09 -30.002011 0.0\n", - "4 4.0 3.394284e+09 -30.009532 0.0" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics = job.metrics()\n", - "df = pd.DataFrame(metrics)\n", - "df = df.groupby(\"iteration_number\").sum().reset_index()\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The metrics are plotted below. " - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'cost function')" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting the convergence of the loss function metric\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "\n", - "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"cost\", marker=\"o\")\n", - "plt.xlim(1, 5)\n", - "plt.ylabel(\"cost function\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example shows us that a 18-qubit QAOA problem can be trained using the \"lightning.qubit\" with adjoint gradients by using hybrid jobs. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "What's next? See if you can analyze the trained QAOA circuit for the 18-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", - "Estimated cost to run this example: 0.000 USD\n" - ] - } - ], - "source": [ - "job_cost = job.result()[\"braket_tasks_cost\"]\n", - "\n", - "print(\n", - " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", - ")\n", - "print(f\"Estimated cost to run this example: {job_cost :.3f} USD\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" - } - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph optimization with QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One application area where near-term quantum hardware is expected to shine is in graph optimization. Graph-based problems are interesting to explore because they have both strong links to practical use-cases (such as logistics and social networks) and are also often hard to solve." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Graphs are composed of a collection of interconnected nodes. For example, here is a six-node graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "n_nodes = 6\n", + "p = 0.5 # probability of an edge\n", + "seed = 1967\n", + "\n", + "g = nx.erdos_renyi_graph(n_nodes, p=p, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions, node_size=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many practical use-cases can be mapped to a graph structure. In a social network, the nodes of a graph can represent users and the edges can represent connections between the users.\n", + "\n", + "We often need to solve optimization problems to identify important properties of the graph. These problems can include:\n", + "\n", + "- finding large clusters of fully connected nodes (known as [maximum clique](https://en.wikipedia.org/wiki/Clique_problem))\n", + "- finding a minimum number of nodes that connect to every edge in the graph (known as [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover))\n", + "- finding a partition of the nodes into two subsets so that the greatest number of edges are intersected (known as [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut))\n", + "\n", + "This tutorial shows how a quantum algorithm called QAOA can be run using PennyLane and Braket to solve graph-based optimization problems. We begin with a small 6-node graph and then push the limits to run a 20-node graph using parallel executions on SV1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note This notebook requires PennyLane version 0.17 or above.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## QAOA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The quantum approximate optimization algorithm (QAOA) is an algorithm designed for near-term hardware. It can find approximate solutions to combinatorial optimization problems such as graph-based problems.\n", + "\n", + "QAOA is covered in more depth in the [QAOA_braket](../../hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb) notebook as well as in PennyLane [tutorials](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). The following is a short summary to refresh the key concepts.\n", + "\n", + "\n", + "QAOA begins by associating the optimization problem with a cost Hamiltonian $H_C$ and choosing a mixer Hamiltonian $H_{M}$. It proceeds by repetitively applying multiple layers of the unitaries $\\exp{(-i \\gamma_i H_C)}$ and $\\exp{(-i \\alpha_i H_M)}$ with controllable parameters $\\gamma_i$ and $\\alpha_i$, as shown in the diagram below." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The algorithm then measures the cost Hamiltonian $H_C$. By varying the controllable parameters $\\gamma_i$ and $\\alpha_i$, the expectation value of the cost Hamiltonian is minimized. Applying the optimized unitaries prepares a quantum state that contains information about the optimal configuration for the problem. Sampling from the state will give a candidate solution.\n", + "\n", + "
\n", + "Summary If you are less familiar with QAOA and quantum algorithms, the key takeaway message is that the algorithm involves an optimization of the controllable parameters $\\gamma_i$ and $\\alpha_i$ that the quantum circuit depends on. This can be tackled naturally using the PennyLane/Braket pipeline.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fixing the problem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's consider the graph above and aim to find the maximum clique, i.e., the largest set of nodes that are fully connected.\n", + "\n", + "To solve this using QAOA in PennyLane and Braket, we first calculate the cost Hamiltonian $H_C$ and corresponding mixer Hamiltonian $H_M$" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost Hamiltonian:\n", + " (-0.5) [Z1]\n", + "+ (-0.5) [Z5]\n", + "+ (0.25) [Z0]\n", + "+ (0.25) [Z4]\n", + "+ (0.25) [Z2]\n", + "+ (0.25) [Z3]\n", + "+ (0.75) [Z0 Z1]\n", + "+ (0.75) [Z1 Z4]\n", + "+ (0.75) [Z2 Z5]\n", + "+ (0.75) [Z3 Z5]\n", + "Mixer Hamiltonian:\n", + " (1) [X0]\n", + "+ (1) [X1]\n", + "+ (1) [X2]\n", + "+ (1) [X3]\n", + "+ (1) [X4]\n", + "+ (1) [X5]\n" + ] + } + ], + "source": [ + "import pennylane as qml\n", + "from pennylane import numpy as np\n", + "\n", + "cost_h, mixer_h = qml.qaoa.max_clique(g, constrained=False)\n", + "# constrained=True results in greater circuit depth but potentially better solutions\n", + "\n", + "print(\"Cost Hamiltonian:\\n\", cost_h)\n", + "print(\"Mixer Hamiltonian:\\n\", mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We begin by setting up a single QAOA layer" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This layer contains the controllable parameters $\\gamma_i$ and $\\alpha_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The full QAOA circuit is then given by:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 1\n", + "wires = n_nodes\n", + "\n", + "\n", + "def circuit(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Note We have chosen to use a single QAOA layer. The choice of depth is a tradeoff between improved solutions (for greater depth) and increasing runtime.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are overall two controllable parameters: the first one is $\\gamma_i$ of the cost Hamiltonian and the one is $\\alpha_i$ of the mixer Hamiltonian:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.72511958],\n", + " [0.57312068]], requires_grad=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(1967)\n", + "params = np.random.uniform(size=[2, n_layers])\n", + "params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this part of the tutorial, we will use the local Braket simulator (see the [introduction tutorial](./0_Getting_started.ipynb) for further details):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"braket.local.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final step is to define the cost function. In QAOA, the output cost function is given by the expectation value of the cost Hamiltonian $H_C$, i.e.," + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def cost_function(params, **kwargs):\n", + " circuit(params)\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have set up the cost function, we just need to pick an optimizer and run the standard optimization loop." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.GradientDescentOptimizer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 1.2873831170401528\n", + "Completed iteration 1, cost function: 1.0283347453164076\n", + "Completed iteration 2, cost function: 0.6850453114142451\n", + "Completed iteration 3, cost function: 0.26816570914692445\n", + "Completed iteration 4, cost function: -0.18397174104834577\n", + "Completed iteration 5, cost function: -0.6134512514217036\n", + "CPU times: user 9.66 s, sys: 3.35 ms, total: 9.66 s\n", + "Wall time: 9.66 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print(\"Initial cost:\", cost_function(params))\n", + "\n", + "for i in range(5):\n", + " params = optimizer.step(cost_function, params)\n", + " cost_eval = cost_function(params)\n", + " print(f\"Completed iteration {i + 1}, cost function:\", cost_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Investigating the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How do we know how well the algorithm has performed? To do this, we can sample from the circuit using the optimized parameters. This will give us binary samples that allow us to select which nodes of the graph to use as part of our clique, e.g., either by simply selecting the most common sample or selecting the sample with the lowest corresponding energy.\n", + "\n", + "Let's take some samples and see which ones occur most frequently. To start, we'll create a QNode designed for sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "shots = 10_000\n", + "dev = qml.device(\"braket.local.qubit\", wires=wires, shots=shots)\n", + "\n", + "\n", + "@qml.qnode(dev, diff_method=\"parameter-shift\")\n", + "def samples(params):\n", + " circuit(params)\n", + " return np.array([qml.sample(qml.PauliZ(i)) for i in range(wires)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Samples can now be generated and converted into probabilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "\n", + "s = samples(params).T\n", + "s = (1 - s.numpy()) / 2\n", + "s = map(tuple, s)\n", + "\n", + "counts = Counter(s)\n", + "indx = np.ndindex(*[2] * wires)\n", + "\n", + "probs = {p: counts.get(p, 0) / shots for p in indx}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot the probability distribution over all possible samples:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "labels = [\"{0:{fill}6b}\".format(i, fill=\"0\") for i in range(len(probs))]\n", + "\n", + "plt.bar(range(2**wires), probs.values())\n", + "plt.xticks([i for i in range(len(probs))], labels, rotation=\"vertical\", size=12)\n", + "plt.yticks(size=12)\n", + "\n", + "plt.xlabel(\"Sample\", size=20)\n", + "plt.ylabel(\"Probability\", size=20)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(16, 8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot, it is clear that the sample ``101110`` has the greatest probability. Since each qubit corresponds to a node, this sample selects the nodes ``[0, 2, 3, 4]`` to form a subgraph. Let's check if this is a clique, i.e., if all of the nodes are connected:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sub = g.subgraph([0, 2, 3, 4])\n", + "nx.draw(g, pos=positions, with_labels=True)\n", + "nx.draw(sub, pos=positions, node_color=\"r\", edge_color=\"r\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, this is a clique! Moreover, it is the *largest* clique in this six-node graph. QAOA, using PennyLane and Braket, has helped us to solve the maximum clique problem!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaling up QAOA for larger graphs with hybrid jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen how we can use PennyLane on Braket to solve graph optimization problems with QAOA. However, we have so far restricted to a simple six-node graph and used the local Braket device. Let's now be more ambitious and try to solve an optimization problem on a 18 node graph! We will use [Amazon Braket Hybrid Jobs](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) to scale up the classical resources, and run the entire algorithm asynchronously. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n", + "from braket.tracking import Tracker\n", + "\n", + "task_tracker = Tracker().start() # track Braket tasks costs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "nodes = wires = 18\n", + "edges = 60\n", + "seed = 1967\n", + "\n", + "g = nx.gnm_random_graph(nodes, edges, seed=seed)\n", + "positions = nx.spring_layout(g, seed=seed)\n", + "\n", + "nx.draw(g, with_labels=True, pos=positions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A 18 node graph (which maps to the same number of qubits) definitely puts us in a regime where the local simulator will be slow to execute. As we have discussed in the [parallelization tutorial](../1_Parallelized_optimization_of_quantum_circuits/1_Parallelized_optimization_of_quantum_circuits.ipynb), this slowness will be compounded when it comes to training the circuit, with each optimization step resulting in multiple device executions due to calculation of the gradient. Thankfully, the remote SV1 simulator is highly suited to speeding up gradient calculations through parallelization or adjoint differentiation. We now show that this makes training the circuit for QAOA solvable within a reasonable time.\n", + "\n", + "Let's first load a new device:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from braket.devices import Devices\n", + "\n", + "device_arn = Devices.Amazon.SV1\n", + "# device_arn = \"arn:aws:braket:::device/quantum-simulator/amazon/sv1\" # alternatively use the device ARN" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "dev = qml.device(\"lightning.qubit\", wires=wires)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now just need to set up the QAOA circuit and optimization problem in the same way as before. However, we will switch to a new optimization problem to keep things interesting: aiming to solve maximum cut, with the objective of partitioning the graph's nodes into two groups so that the greatest number of edges are shared between the groups (see the image below). This problem is NP-hard, so we expect it to be tough as we increase the number of graph nodes." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cost_h, mixer_h = qml.qaoa.maxcut(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def qaoa_layer(gamma, alpha):\n", + " qml.qaoa.cost_layer(gamma, cost_h)\n", + " qml.qaoa.mixer_layer(alpha, mixer_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "n_layers = 2\n", + "\n", + "\n", + "@qml.qnode(dev)\n", + "def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1967)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of [optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) are available in PennyLane. Let's choose ``AdagradOptimizer``:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = qml.AdagradOptimizer(stepsize=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're now set up to train the circuit! Note, if you are training this circuit yourself, you may want to increase the number of iterations in the optimization loop and also investigate changing the number of QAOA layers.\n", + "\n", + "\n", + "We create a hybrid job by annotating our main function with `@hybrid_job`. \n", + "This allows us to choose a target QPU for priority queueing, and additional arguments such as the type of classical instances to use. \n", + "In this example, we use an \"ml.c5.xlarge\" instance. \n", + "\n", + "Note that creating hybrid jobs is only supported on Python 3.10. For other versions, you may use [scripts](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs.html) or a [custom container image](https://docs.aws.amazon.com/braket/latest/developerguide/braket-jobs-byoc.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from braket.jobs import hybrid_job, InstanceConfig\n", + "from braket.jobs.metrics import log_metric\n", + "\n", + "large_instance = InstanceConfig(instanceType=\"ml.c5.xlarge\")\n", + "\n", + "\n", + "@hybrid_job(device=\"local:pennylane/lightning.qubit\", instance_config=large_instance)\n", + "def qaoa_training(iterations, n_layers=2):\n", + " task_tracker = Tracker().start() # track Braket tasks costs\n", + "\n", + " dev = qml.device(\"lightning.qubit\", wires=wires)\n", + "\n", + " @qml.qnode(dev)\n", + " def cost_function(params, **kwargs):\n", + " for i in range(wires): # Prepare an equal superposition over all qubits\n", + " qml.Hadamard(wires=i)\n", + "\n", + " qml.layer(qaoa_layer, n_layers, params[0], params[1])\n", + " return qml.expval(cost_h)\n", + "\n", + " params = 0.01 * np.random.uniform(size=[2, n_layers])\n", + "\n", + " for i in range(iterations):\n", + " params, cost = optimizer.step_and_cost(cost_function, params)\n", + "\n", + " # Record the value of the cost function with each iteration\n", + " log_metric(metric_name=\"cost\", value=cost, iteration_number=i)\n", + "\n", + " # Additionally, keep track of cost in USD for Braket tasks\n", + " braket_task_cost = float(\n", + " task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost()\n", + " )\n", + " log_metric(metric_name=\"braket_cost\", value=braket_task_cost, iteration_number=i)\n", + "\n", + " return {\n", + " \"parameters\": params,\n", + " \"final_cost\": cost_function(params),\n", + " \"braket_tasks_cost\": task_tracker.qpu_tasks_cost() + task_tracker.simulator_tasks_cost(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we create the hybrid job by calling the function as usual. The function arguments are logged as hyperparamters for the hybrid job. \n", + "\n", + "
\n", + "Caution: Running the following cell will take a long time and will result in usage fees charged to your AWS account. Only uncomment the cell if you are comfortable with the potential wait-time and costs. We recommend monitoring the Billing & Cost Management Dashboard on the AWS console and being aware that hybrid jobs involving a large number of qubits can be costly.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AwsQuantumJob('arn':'arn:aws:braket:us-west-1:961591465522:job/qaoa-training-1697141494101')\n" + ] + } + ], + "source": [ + "job = qaoa_training(iterations=5, n_layers=2)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hybrid job will be scheduled to run and will appear in the \"QUEUED\" state. \n", + "If the target is a QPU, the hybrid job will be queued with other hybrid jobs. \n", + "If the target device is not a QPU, the hybrid job should start immediately. \n", + "\n", + "Note that since the algorithm code is run in a containerized environment, it takes approximately 1 minute to start running your algorithm. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'QUEUED'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the state is \"COMPLETED\", we retrieve the results with " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 337 ms, sys: 18 ms, total: 355 ms\n", + "Wall time: 8min 54s\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "data": { + "text/plain": [ + "{'parameters': tensor([[-0.01460419, 0.00094966],\n", + " [ 0.0187241 , 0.00412996]], requires_grad=True),\n", + " 'final_cost': array(-30.03947181),\n", + " 'braket_tasks_cost': Decimal('0')}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "job.result()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results included the three values from the return statement of our function.\n", + "\n", + "Additionally, we can retrieve the metrics recorded during the training with:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " iteration_number timestamp cost braket_cost\n", + "0 0.0 3.394283e+09 -29.992536 0.0\n", + "1 1.0 3.394284e+09 -27.007548 0.0\n", + "2 2.0 3.394284e+09 -29.993231 0.0\n", + "3 3.0 3.394284e+09 -30.002011 0.0\n", + "4 4.0 3.394284e+09 -30.009532 0.0" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics = job.metrics()\n", + "df = pd.DataFrame(metrics)\n", + "df = df.groupby(\"iteration_number\").sum().reset_index()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metrics are plotted below. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'cost function')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the convergence of the loss function metric\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "df.sort_values(by=[\"iteration_number\"]).plot(x=\"iteration_number\", y=\"cost\", marker=\"o\")\n", + "plt.xlim(1, 5)\n", + "plt.ylabel(\"cost function\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example shows us that a 18-qubit QAOA problem can be trained using the \"lightning.qubit\" with adjoint gradients by using hybrid jobs. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "What's next? See if you can analyze the trained QAOA circuit for the 18-node graph by adapting the earlier analysis. Also, check out the followup tutorial on quantum chemistry.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n", + "Estimated cost to run this example: 0.000 USD\n" + ] + } + ], + "source": [ + "job_cost = job.result()[\"braket_tasks_cost\"]\n", + "\n", + "print(\n", + " \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n", + ")\n", + "print(f\"Estimated cost to run this example: {job_cost :.3f} USD\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "590fab68195cf107911461461f81d5c472d3d6127f579badfcfad30f03e5cab2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } \ No newline at end of file