Questions tagged [quantum-enhanced-machine-learning]
For questions about quantum algorithms tackling machine learning tasks (e.g. the HHL algorithm or questions about quantum neural networks). For questions about applying classical machine learning to quantum-information-related problems, use machine-learning instead.
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Is there any potential application of quantum computers in machine learning or AI?
A lot of people believe that quantum computers can prove to be a pivotal step in creating new machine learning and AI algorithms that can give a huge boost to the field. There have even been studies ...
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Introductory material for quantum machine learning
In the past few days, I have been trying to collect material (mostly research papers) related to Quantum machine learning and its applications, for a summer project. Here are a few which I found ...
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Quantum machine learning after Ewin Tang
Recently, a series of research papers have been released (this, this and this, also this) that provide classical algorithms with the same runtime as quantum machine learning algorithms for the same ...
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Will deep learning neural networks run on quantum computers?
Deep Learning (multiple layers of artificial neural networks used in supervised and unsupervised machine learning tasks) is an incredibly powerful tool for many of the most difficult machine learning ...
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What is the advantage of quantum machine learning over traditional machine learning?
Why exactly is machine learning on quantum computers different than classical machine learning? Is there a specific difference that allows quantum machine learning to outperform classical machine ...
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What are the benefits of using quantum machine learning?
I have been investigating uses for quantum machine learning, and have made a few working examples (variations of variational quantum classifiers using PennyLane). However, my issue now is its ...
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Applications of Quantum Principal Component Analysis
I have been reading Seth Lloyd's paper on Quantum Principal Component Analysis and while there is a short discussion that points to possible applications, I am having a hard time seeing the advantage ...
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HHL algorithm -- controlled-by-eigenvalues rotations
All the references in this question refer to Quantum algorithm for solving linear systems of equations (Harrow, Hassidim & Lloyd, 2009).
The question I have is about the step where they apply ...
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Are there any examples of anyone applying quantum algorithms to problems in computational biology?
As the title suggests, I'm searching for published examples of quantum algorithms being applied to problems in computational biology. Clearly the odds are high that practical examples don't exist (yet)...
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Comparing method of differentiation in variational quantum circuit
Training of variational circuits needs to calculate the derivative to be optimized. Several methods were proposed (1), the most famous ones being the finite difference and the parameter shift rule.
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Barren plateaus in quantum neural network training landscapes
Here the authors argue that the efforts of creating a scalable quantum neural network using a set of parameterized gates are deemed to fail for a large number of qubits. This is due to the fact that, ...
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Is VQA quicker than classical machine learning?
Variational Quantum Algorithm (VQA) is a kind of quantum algorithm corresponding to classical machine learning. Unlike the square speed up of Grover's algorithm, the circuit in VQA does not seem to ...
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Are there quantum algorithms demonstrating speedup computing classical neural networks (in 2021)?
It seems like there are a number of different speed-ups for different machine learning algorithms:
But has anyone created an algorithm showing speed-up for neural networks? A similar question was ...
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How is data encoded in a quantum neural network?
I am a newbie to quantum machine learning. I am trying to build a quantum neural network (QNN). What I studied so far about QNN is that input would be qubits and hidden layer parameter can be set ...
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Advantage of density matrix over vector to form quantum kernel
In Maria Schuld, Supervised quantum machine learning models are kernel methods, Section III.A, on page 6, the third paragraph from the bottom states
While from a quantum physics perspective it seems ...
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Quantum circuit for the ZZ feature map
Havlicek et al. propose a feature map for embedding $n$-dimensional classical data on $n$ qubits: $U_{\phi(x)}H^{\otimes n}$, where
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U_{\phi(x)} = \exp (i \sum_{S \subseteq [n]} \phi_S(x) \prod_{i \...
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Qiskit's classifier is not optimising the weights
I am using qiskit's VQC to build a classifier. Dimensionality of the data is 2 and number of classes are 4. The feature map I used is ZZFeatureMap and ansatz is the RealAplitudes. Then entanglement ...
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The possibility of an image classifier using quantum computer architecture?
Consider an exhaustive database of all contour images that can ever be created on a 16x16 grid. Out of the $2^{256}$ unique possibilities, could a quantum computer classify all the resulting images ...
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How to save a hybrid Tensorflow and Pennylane model?
I implemented a hybrid model with Keras and Pennylane that looks like this:
The quantum layer is basically a quantum circuit converted to a keras layer with the ...
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Error loading saved hybrid quantum (pennylane + tensorflow keras) model: Unknown layer: 'KerasLayer'
I'm creating a hybrid model consisting of classical convolutional layers and a quantum output using Tensorflow. I can save the model in either .h5 or .keras format, but when I load them with the code <...
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How to perform multi-class classification with qiskit's VQC?
I am following the tutorial given in qiskit's website Neural Network Classifier and Regressor. In the first part, classification, the third section refers to qiskit's VQC library. Everything works ...
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Regarding quantum support vector machine using qiskit [duplicate]
I would like to ask, how can I add my own .csv data file to run a quantum support vector machine using qiskit ? I don't want to use already existing datasets in sklearn, scikit-learn library.
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