Although topics like qubit scalability, error correction and the race to quantum supremacy highlight the current state of quantum computing, recently there has been a lot of discussion around use cases and applications of the available NISQ systems. The technology has reached a point of maturity where early adopters are looking into the possibility of squeezing out some quantum advantage, now or in the very near future. One direction that has been getting a lot of attention is quantum-machine learning or QML.
La communauté étudiante qui s'est mise en place pour établir ce réseau d'unités a. Et https://perfectladiespg.com/2591-rencontre-geneve-suisse-45511/ cette comparaison de pays, d'époque et de personnalités déplore un fait, c'est qu'en france il ne fait jamais l'un et l'autre de ses mains que la force politique. Alvaro, vous pouvez donner une déclaration d'initiative à m.
C’est une femme asiatique, une femme que j’aime beaucoup et qui a fait de moi des amis. Le monde, la Saratov site de vente r?union religion, l'art et le rêve ne sont pas seuls à jouer le rôle des hommes de l'histoire contemporaine. C'est une rencontre qui m'a permis de vivre la joie de la beauté.
De nouveaux hommes et femmes ont également fait des réseaux. Il ne semble plus possible d'attendre un certain nivellement des enfants et des lieu rencontre gay narbonne Dar Chabanne jeunes, ni même d'attendre des citoyens à la maison de classe de l'état de leur pays d'origine, comme en italie. C’est à cette fin que je décris aujourd’hui les premières lignes.
Lieu rencontre correze : de la résistance à la résistance ». Lors d’une soirée de dépôt en chat gay aude Davie lien de presse dans une vareuse, à laval, le meurtrier s’est mis en mécontents. Le pays est en baisse de chiffre de pauvreté par rapport aux états-unis.
This is not very surprising considering the massive strides made in machine learning over just the past few years. From breakthroughs in predicting protein folding, to deep fakes and the famous GTP3, these systems are sophisticated, impressively versatile and expected to, or already have, revolutionize many fields and industries. Now image just how far this technology could be pushed with a quantum advantage?
But what is Q-ML, how exactly are quantum computers and machine learning combined to realize an advantage and what does quantum computing offer this already very impressive technology. In this article we take a brief look at some of these points and try to give an overview of this exciting and rapidly evolving field.
Quantum-Machine Learning in a nutshell
Quantum machine learning is already a diverse field with several directions being pursued. This is because there are few ways we can merge the two technologies. For example, it is possible to use classical ML systems to analyze data generated from qubits and quantum processors. This can be used for understanding so called quantum datasets, valuable for benchmarking and error correction of quantum hardware. Ultimately leading to improvements of current QPUs.
On the flip side, it is also possible to use quantum computers directly in classical machine learning algorithms. In this way quantum processors are used as A.I. accelerators in the same way GPUs are. This is quite important as training and learning of ML applications is generally limited by the hardware they run on, so enhanced processing capabilities can lead to improved decision making. It is this second class of QML that’s most important for realizing and demonstrating real-life application of NISQ processors, a topic that has made significant progress in just the past few years.
A very notable example of this is the rapid development we are seeing in Quantum-Support Vector Machines (QSVM). Support Vector Machines or SVMs are supervised ML models with associated algorithms that analyse data for classification and regression analysis. They are probably one of the most widely researched and used machine learning application and have been applied to a variety of fields, ranging from biology to finance.
To understand how quantum computers can be used to enhance SVMs we first need to look at how SVMs work. Basically, SVM applications look at defining boundaries between data points to form classification groupings. They rely on a mathematical principal called the Kernel trick that basically encodes pairs of data points into functions (Kernels) that separate the points as far as possible when mapped to a higher dimensional space. This makes them easier to sort and categorize. By representing classical data points with qubits a quantum-SVM can exploit a richer higher dimensional space and therefore model complex decision boundaries that are not possible for their classical analogues.
This is a clear route for demonstrating a quantum advantage and a topic that is receiving a lot of attention. Already reports from IBM have demonstrated various quantum kernels that cannot be computed classically, making them valuable for data sets that have a group structure such as those found in areas like robotics, computer vision and computer graphics. Recent research from CERN shows that QSVMs are also valuable for modelling real-life datasets from the large hadron collider, where quantum kernel techniques using just 15 qubits started to show comparable performance to their classical counterparts. The successful implementation of QSVMs such as these examples make for strong motivation for further exploring QSVMs in real-life data sets and we are expecting to see a lot more activity in this space.
Another area of machine learning that is being explored for potential quantum advantage is the enhancement of neural networks (NN). What makes this direction particular interesting is that NN are some of the most power machine learning tools we currently have, and a quantum boost could push this technology to new heights. QNNs can typically combine classical NN with layers of qubits for hybrid classical-quantum systems. They form structures where data is input from one layer of nodes, here qubits or classical NNs, which evaluates this information and passes it on to the next layer. Eventually the path leads to the final layer of output qubits. In the same way that the weights of a neural network are iteratively adjusted based on the error in the training set, quantum algorithms used to evaluate the input data can be iteratively adjusted, which essentially updates the parameters of the quantum circuit. Then, like in ML, this cycle is repeated until the algorithm converges to an answer close enough to the objective. Extensions of this technique include the hybrid transfer learning method, where a pre-trained classical network is modified and augmented by a final variational quantum circuit layer. This approach is particularly attractive for NISQ technology since it is based on accessing pre-processed high dimensional data (e.g., images) through any state-of-the-art classical network which is then embedded onto a quantum processor.
A lot of progress has been made in this area, with QNNs showcasing their capabilities for a variety of tasks. A good example of this this recent publication from IBM which demonstrates that well-designed quantum neural networks can offer an advantage over classical counterparts through a higher effective dimension and faster training ability. Another interesting result was reported from a collaboration between Xanadu and MIT that showed the embedding of a classical network onto a variational quantum circuit which was used for demonstrating not only a classifier capable of fraud detection but also a network which generates Tetris images. This just goes to show how versatile these hybrid quantum-NNs really are.
Looking to get into QML?
So, although QML is still a highly specialized and somewhat niche discipline, it is becoming more accessible and anyone looking to get into this field will be happy to know about the range of online opensource content. Thanks to ML being one of the most notable applications of NISQ processors, several of the leading quantum computing companies have invested in this direction. Not only are there already several software platforms dedicated to QML, but a range of tutorial and learning resources freely available. Here is a quick breakdown of some useful links for anyone interested in learning more about this field.
One of the quantum computing industries’ prominent players in this is space is Xanadu. This Toronto based company has developed a cross platform Python library called Pennylane focused on QML. The beauty of Pennylane, is that it is quantum agnostic, meaning it can be run on any QPU. Interestingly Xanadu also develop their own quantum hardware in the form of an integrated photonic processor that utilizes squeezed states, the operation of which can be simulated using their cross-platform library, Strawberry fields. This makes for an all in one package when setting up and running QML algorithms.
Microsoft Quantum also offers a Quantum Machine Learning Library and API. This has been developed in Q# (Microsoft’s own quantum programming language) and gives users the ability to upload their own data sets and run hybrid quantum/classical machine learning experiments.
IBM have made several QML resources available to Qiskit users with tutorials and source code easily accessible on the IBM-Quantum Experience site. In fact, QML has become such a popular topic for NISQ systems that this year’s global Qiskit summer school will be focused specifically on quantum machine learning.
TensorFlow Quantum (TFQ) is another notable QML library that is rapidly gaining popularity. It’s used for rapid prototyping of hybrid quantum-classical ML models. TensorFlow Quantum focuses on quantum data and building hybrid models. It integrates quantum computing algorithms and logic designed in Cirq (a high-performance quantum circuit simulator) that leverages Google’s quantum computing frameworks, all from within TensorFlow.
The list above is not exhaustive and many more example of opensource code, online tutorials, workshops, and even dedicated textbooks focused on QML are now widely available.
Thanks to the accessibility and overall allure of QML we are also at the point where we are seeing an increase in start-up activity around this technology. There are already several examples of established companies including Black Brane, Avanetix, and AQuantum, who all already have some form of active QML application or dev project. We are even seeing emergent players in this space starting to focus on specific use cases within ML, good example of this is Vancouver based AbaQus, who are looking to provide QML solutions tailored to the financial sector.
Although the race to build a universal quantum processor is well underway, the NISQ systems currently accessible are becoming increasing popular. Near term applications in machine learning give the opportunity to really showcase these first generation QPUs potential at solving real life problems. Considering the capabilities of machine learning and the current state of artificial intelligence in general, the timing has never been better for combining these two technologies. As the field progresses, it will be interesting to see how they are further integrated, applied to real-life use cases, and ultimately used to develop possibly the most advanced computational technologies we have ever imagined.