In particular, if a The format of the date(s) is year of creation, changes over a release cycle we require that all user facing changes include information was not being passed, which calls the transpiler many times during the execution of the QSVC/QSVR algorithm. Can it learn to play quantum games better than a classical agent and human minds? Fixes in Ad Hoc dataset. workflow for writing and compiling release notes. sure they're included with the code in your PR. user with limited internal knowledge of the project is upgrading from the main branches so these should be installed from source too following the instructions at It can also be used for predicting the result using the trained model or calculating the score of the trained model faster using Qiskit Runtime. tox. You signed in with another tab or window. file in docs_build\html and you can navigate from there. We appreciate all kinds of help, so thank you! Therefore, a number of jobs required for such computations is significantly reduced. in each category. It takes the Computation of gradients with respect to input data in the backward method of NeuralNetwork is now optional. Eventually, the trained generator can be used for state preparation e.g. Another way to run the test suite is to use tar -zcvf artifacts/tutorials.tar.gz --exclude=./artifacts . This is, the equivalent of calling the ```` to do something to your, QuantumCircuit. it has been tagged:: At release time reno report is used to generate the release notes for the This caused to re-use the same forward pass value across all iteration. a parametrized quantum circuit) in the framework of Approximate Q-Learning and Deep Q-Learning [5]. QuantumKernel transpiles all circuits before execution. Ensure all code, including unit tests, has the copyright header. Get classical data samples from the generator. User parameters are an array of Parameter objects corresponding to parameterized quantum gates in the feature map circuit the user wishes to tune. This is consistent with the PyTorch naming convention and the weight property used to get access to the computed weights. To generate the rst file you Support for running with Python 3.6 has been removed. Fixes an issue in QuantumKernel where evaluating a quantum kernel for data with dimension d>2 raised an error. they'll have to update their code. Well occasionally send you account related emails. never changed. PR numbers from the git log (assuming a squash merge) and checks if that PR had So for example: If the year of creation is the same as last year changed then only Please see the Installing Qiskit Machine Learning from The weights property in TorchConnector is deprecated in favor of the weight property which is PyTorch compatible. You'll want to delete all the sections you aren't using and to see the changes formatted output is as expected. This support is introduced in TrainableModel, so all sub-classes can be saved and loaded. By default, PyTorch layers expose weight properties to get access to the computed weights. that are not in the en-us dictionary and get flagged as being misspelled, despite being correct, the same location. # Any modifications or derivative works of this code must retain this, # copyright notice, and modified files need to carry a notice indicating. follow the guidelines below. They may inspected and turned on, if required, by getting or setting a new property input_gradients in the NeuralNetwork class. when we migrated to use black for code formatting). message summary line from the git log for the release to the changelog. Now both NeuralNetworkClassifier and VQC support sparse arrays and convert them dense arrays in the implementation. generator_circuit: a QuantumCircuit implementing the generator. You may, # obtain a copy of this license in the LICENSE.txt file in the root directory. to ignore when using the git blame command. If you want to generate the full Machine Learning release Classification models (i.e.

The same is true for the classes VQC and VQR as they inherit from NeuralNetworkClassifier and NeuralNetworkRegressor respectively. If it makes sense for your change that you have added new tests that test skip

the following steps: The stable/* branches should only receive changes in the form of bug

are well-tested versions of the software. cover the changes and any new function. Cannot retrieve contributors at this time, Introduced a new feature foo, that adds support for doing something to. models that extend the NeuralNetworkClassifier class like VQC) can now handle categorical target data in methods like fit() and score(). Fixes a bug where VQC could not be instantiated unless either feature_map or ansatz were provided (#217). The basic You can run make spell locally to check spelling though you would need to So, every time when the circuit is executed it is already transpiled and overall time of the forward pass is reduced. objective_function: objective function for quantum generator optimization, params (numpy.ndarray): generator parameters, gradient_object (Gradient): the gradient object to be used to, gradient_function: gradient function that takes the current, parameter values and returns partial derivatives of the loss. You should update your model instantiations by replacing l1 with absolute_error and l2 with squared_error. These agents are rewarded for performing good actions, and their goal is to find an optimal policy (behaviour) to maximize these rewards, thus solving the envirnoment (i.e. You add individual items under each category and they will be grouped This works through a combination of the git log and the pull Line length must be limited to no more than 100 characters. Once you've made a code change, it is important to verify that your change Therefore, VQC will now determine the number of classes from the shape of the target array. automatically by release when the release notes are compiled. Users may also use a new function assign_user_parameters to assign real values to some or all of the user parameters in the feature map. A Quantum Agent in a Quantum World structure of a release note is restructured text in yaml lists under category Sparse arrays can be easily observed when labels are encoded via OneHotEncoder from SciKit-Learn. Both NeuralNetworkClassifier and NeuralNetworkRegressor extend this class. Terra CONTRIBUTING To make sure we don't forget a release note or if the details of user facing run all unit tests., Quantum Reinforcement Learning (Qiskit Hackathon Europe),,,, This fixes the exception that occurs when no optimizer argument is passed to NeuralNetworkClassifier and NeuralNetworkRegressor. docs/_build/html/artifacts/documentation.tar.gz, ${{ !cancelled() && matrix.python-version != '3.10' }}, Machine Learning Unit Tests under Python ${{ matrix.python-version }}, ${{ contains(github.event.pull_request.labels. with something like. Now NeuralNetworkClassifier extends ClassifierMixin and NeuralNetworkRegressor extends RegressorMixin from Scikit-Learn and rely on their methods for score calculation.

Fix a bug where TorchConnector failed on backward pass computation due to empty parameters for inputs or weights.

The main idea of our project is to create and train a Quantum Agent, from now on named Elliot, developed entirely with Qiskit, capable of using quantum computing to speed up its own learning process and possibly outperform its classical counterpart [4]. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. ```` calls to ````. We implemented Quantum Reinforcement Learning la Grover [2], where a Grover-like procedure is used to amplify the amplitudes corresponding to good actions. and skip the test if not installed. *.name, 'run_slow') }}" == "true" ]; then. The valid values passed to the loss argument of the TrainableModel constructor were partially deprecated (i.e. Source Fixes an issue where VQC would fail with warm_start=True. You should first install the python development libraries by running builds. Machine Learning optional installs for

It can be used by using the foo function, The ``qiskit.QuantumCircuit`` module has a new method ``foo()``. needed so that users will understand what has changed, why it changed, and how You will find an index.html Implementation of a fully quantum model, where Elliot, the quantum agent, tries to learn how to play Quantum TicTacToe. Fixed a bug in QuantumKernel where for statevector simulator all circuits were constructed and transpiled at once, leading to high memory usage. shots (int): Number of shots for hardware or qasm execution. becomes part of the online API Documentation. privacy statement. look something like:: You can also look at other release notes for other examples. would go about doing that. dict: generator loss(float) and updated parameters (array). The global effective dimension (or just effective dimension) can be used as a measure of the expressibility of the model, and accepts multiple parameter sets. PEP8 style guidelines. # This code is licensed under the Apache License, Version 2.0. repository's release notes file, Building The release notes are part of the standard qiskit-machine-learning documentation This also adds an ability to pass sample weights as an optional parameter to the score methods. Fixed an ValueError when n=3 is passed to ad_hoc_data.

quantum_instance (QuantumInstance): used to run the generator circuit. Now, if a hybrid PyTorch model is being trained on GPU, TorchConnector correctly detaches tensors, moves them to CPU, evaluate forward and backward passes and places resulting tensors to the same device they came from. to the change. current_point (np.ndarray): Current values for the variational parameters. you'll want to create a new file for each pull request that has user facing

*.name, 'run_slow') }}, python tools/ -file out.txt -output ./ci-artifact-data/ml.dep, ${{ matrix.os }}-${{ matrix.python-version }}, Machine Learning Unit Tests without torch/sparse under Python ${{ matrix.python-version }}, if [ "${{ github.event_name }}" == "schedule" ] || [ "${{ contains(github.event.pull_request.labels. In general you want the release notes to include as much detail as When the time for a new release has come, we will: The release automation processes will be triggered by the new tag and perform Addition of a new KernelLoss subclass, SVCLoss. docs/_build/html/artifacts/tutorials.tar.gz, version=$(pip show qiskit-machine-learning | awk -F. '/^Version:/ { print substr($1,10), $2-1 }' OFS=. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. # TODO Improve access to maxiter, say via options getter, to avoid private member access, # and since not all optimizers have that exact naming figure something better as well to, # allow the checking below to not have to warn if it has something else and max iterations, "Please set the the optimizer maxiter argument to 1 ", "and discriminator are updated in an alternating fashion. this change too, to provide the necessary context.

Since it's a very new field, the first questions we addressed are the fundamentals: how can we translate the building blocks of Classical RL efficiently in a Quantum setting? A single file online tests

you'll want to run the test suite locally. line from the git log will be used for each changelog category tagged. In addition to the general guidelines above there are specific details for a RL algorithm that uses quantum computation as an internal resource, and train it on classical environments. Ensure that you have reno We used the Grover approach to train the agent, and we built a custom Qiskit implementation of a Quantum TicTacToe environment. However, this To check what the rendered html output of the release notes will look Driven by the promising idea of a quantum speed-up, the development of Quantum Reinforcement Learning (QRL) algorithms has begun in the last few years [1, 2, 3]. In general the output from reno that we'll get is a rst Learn more about bidirectional Unicode characters. This class follows Scikit-Learn principles and makes the quantum machine learning compatible with classical models. can have as many entries in it as needed, but to avoid potential conflicts For example, the end result should Quantum Reinforcement Learning with a Variational approach**. from the root of the repository. discriminator (torch.nn.Module): discriminator network to compute the sample labels. Implement calculation of probability distributions for QBM. This is not longer the case. num_qubits: k numbers of qubits to determine representation resolution, i.e. Reinforcement Learning (RL) [1] is a machine learning paradigm dealing with autonomous intelligent agents, which learn wihtout supervision by direct interaction with an external environment. Loss function for training the generator's parameters. This is due to this averaging factor. That means if a model is loaded from a file and is used, for instance, for inference, the same quantum instance and a corresponding backend will be used even if a cloud backend was used. For the Hackathon, our team decided to focus on Quantum Reinforcement Learning [1, 2, 3], a super interesting field of research which tries to bring together Classical Reinforcement Learning and Quantum Computing. Added a dedicated handling of ComposedOp as a operator in OpflowQNN.

a Changelog: label on it. tar -zcvf artifacts/documentation.tar.gz --exclude=./artifacts . is that if your code contribution has user facing changes that you will write in QAE. You can run make test which will The callback function takes in two parameters: the weights for the objective function and the computed objective value. Addition of a QuantumKernelTrainer object which may be used by kernel-based machine learning algorithms to perform optimization of some QuantumKernel parameters before training the model. In the qiskit-machine-learning repository we maintain a list of commits for git blame Encoder type is determined by the one_hot argument supplied when instantiating the model. These Machine Learning repository clone and then init_params: 1D numpy array or list, Initialization for, optimizer: optimizer to be used for the training of the generator, gradient_function: A Gradient object, or a function returning partial, derivatives of the loss function w.r.t. Computed averaged objective values are passed to a user specified callback if any.

Machine Learning also has some other optional dependents see The development cycle for qiskit-machine-learning is informed by release plans in the Published article. to your account. The quantum generator is a parametrized quantum circuit which can be trained with the, :class:`~qiskit_machine_learning.algorithms.QGAN` algorithm, to generate a quantum state which approximates the probability, distribution of given training data. what was changed, why it was changed, and how users can either use or adapt ${{ github.repository }}-${{ github.ref }}-${{ github.head_ref }}, echo -e "\033[31;1;4mConcurrency Group\033[0m", ./.github/actions/install-main-dependencies, ${{ !startsWith(github.ref, 'refs/heads/stable') && !startsWith(github.base_ref, 'stable/') }}, ./.github/actions/install-machine-learning, pip install jupyter sphinx_rtd_theme qiskit-terra[visualization] 'torchvision<0.10.0', echo "earliest_version: 0.1.0" >> releasenotes/config.yaml. It returned nan values on some iterations. Initially, this cache was based on an identifier (a call of id() function) of the weights array. Note: Machine Learning depends on Terra, and has optional dependence on Aer and IBM Q Provider, so Machine Learning uses Pylint and This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. like for the current state of the repo you can run: tox -edocs which will Source, Create a new tag with the version number and push it to github, Create a stable branch for the new minor version from the release tag change the functionality but just change the code formatting (for example, However please note Machine Learning does not have any This can be done one time A base class TrainableModel is introduced for machine learning models. VQC is now instantiated with the default feature_map and/or ansatz. If positional arguments are passed into QSVR or QSVC and these classes are printed, an exception is raised. by default. section of the Qiskit documentation.

does not break any existing tests and that any new tests that you've added This guarantees that it is at least possible for every class to be predicted with a non-zero probability. from errors and warnings. Quantum kernel - missing indices in regression tasks, Write a new tutorial or update the existing tutorials to cover new primitives-based QNNs, Implement a QNN based on the Estimator primitive, Implement a QNN based on the Sampler primitive, Enhance optimizer support as per recent VQE/QAOA change, Exception while evaluating QuantumKernel on a QASM simulator when duplicate entries in the dataset, Deprecate and move ad_hoc dataset to another place. By clicking Sign up for GitHub, you agree to our terms of service and

[2] Quantum Reinforcement Learning (2018, Dong et al.). Implement PoC of convolutional neural networks. this. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. TorchConnector now returns the correct input gradient dimensions during the backward pass in hybrid nn training. You should update your calls. ), wget$version -O /tmp/, # copy stable tutorials to main tutorials, cp -R /tmp/qiskit-machine-learning-stable-$version/docs/tutorials/* docs/tutorials, ${{ matrix.python-version == 3.8 && !startsWith(github.ref, 'refs/heads/stable') && !startsWith(github.base_ref, 'stable/') }}, tutorials-stable${{ matrix.python-version }}, pip install -U coverage coveralls diff-cover, sort -f -u /tmp/u37/ml.dep /tmp/u38/ml.dep /tmp/u39/ml.dep /tmp/u310/ml.dep /tmp/m38/ml.dep /tmp/m310/ml.dep /tmp/w38/ml.dep /tmp/w310/ml.dep ||. Then open that yaml file in a text editor and write the release note. ``QuantumCircuit`` objects. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Note that implementations 1 and 3 come with python scripts, while implementation 2 consists of Jupyter Notebooks. It was originally deprecated in the Qiskit Terra 0.18.0 release. Once that is run it will create a new yaml file in releasenotes/notes. there is a .pylintdict custom word list file, in the root of the Machine Learning repo, Better batch size default setting for QuantumKernel, Expand CircuitQNN to allow for custom measurments. For each iteration an optimizer invokes the callback and passes current weights and computed value of the objective function. x (numpy.ndarray): sample label (equivalent to discriminator output), weights (numpy.ndarray): probability for measuring the sample. What does it mean to take a quantum action? To accomplish this we use the params (list | dict): parameters which should be used to run the generator. function or class has been modified during the PR, please update the We tested this approach against the FrozenLake environment, provided by OpenAI gym. This will become the prefix for the release note As a consequence qiskit_machine_learning.circuit.library.RawFeatureVector.bind_parameters() is also fixed. As for the first phase, we all collaborated to the algorithm design and theory, although Paolo is the major contributor of the Grover-like algorithm while Stefano is the major contributor to the Parametric Quantum Circuit (PQC) code. More in detail, in this repository you find three main codes: Grover-like amplitude enhancing bounds: k min/max data values [[min_1,max_1],,[min_k,max_k]]. fix update the code formatting. This means overall performance when RawFeatureVector is used stays the same. Fix a bug, where qiskit_machine_learning.circuit.library.RawFeatureVector.copy() didnt copy all internal settings which could lead to issues with the copied circuit. np.ndarray: array of partial derivatives of the loss, Perform one training step w.r.t to the generator's parameters. contributing to Qiskit Machine Learning. from the root of the Machine Learning repository clone. The current categories for each label are as follows: When making any end user facing changes in a contribution we have to make sure A base class ObjectiveFunction is introduced for objective functions optimized by machine learning models. The previously deprecated BaseBackend class has been removed. Class CrossEntropySigmoidLoss is deprecated and marked for removal.