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Will deep learning neural networks run on quantum computers?
This is very much an open question, but yes, there is a considerable amount of work that is being done on this front.
Some clarifications
It is, first of all, to be noted that there are two major ...
glS♦
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How Mature is the Tensorflow Quantum Library
First we should take a step back. Is there any machine learning done a quantum computer that cannot be efficiently simulated on a classical computer? The answer currently (2020) is no. In this ...
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Quantum Circuit To Compute Any Inner Product
We can use the SWAP test to determine the inner product of 2 states $|\phi\rangle$ and $|\psi\rangle$. The circuit is shown below
The state of the system at the beginning of the protocol is $|0\...
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Will deep learning neural networks run on quantum computers?
Yes, all classical algorithms can be run on quantum computers, moreover any classical algorithm involving searching can get a $\sqrt{\text{original time}}$ boost by the use of grovers algorithm. An ...
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Can quantum computing provide advantages related to Hardware-Neural Networks?
Again, this is still an open question.
There are two lines of work that come to mind when you talk of "hardware-based neural networks" which try/claim to use photonics as a mean to speed-up ...
glS♦
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6
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How is data encoded in a quantum neural network?
There are many possible ways to encode data into a quantum neural network (QNN). In one of the first papers to suggest the use of variational circuits to classify data [1], the authors suggest the ...
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5
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PennyLane operations - Kerr, Displacement and Squeeze
Short, sort-of right answer: you can't
This is in essence due to the superconducting qubits that e.g. IBM use being, well, qubits, while continuous variable (CV) operations don't act on qubits. Well, ...
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Is it possible to speed up the generation of the weighting matrix using a quantum algorithm?
Taking the density matrix $$\rho=W+\frac{I_d}{d}=\frac 1M \sum_{m=1}^M\left|x^{\left(m\right)}\rangle\langle x^{\left(m\right)}\right|,$$ many of the details are all contained in the following ...
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4
votes
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Barren plateaus in quantum neural network training landscapes
First: The paper references [37] for Levy's Lemma, but you will find no mention of "Levy's Lemma" in [37]. You will find it called "Levy's Inequality", which is called Levy's Lemma in this, which is ...
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Devising "structured initial guesses" for random parametrized quantum circuits to avoid getting stuck in a flat plateau
I am not an expert but I read a few papers and here is what I have found. Similarly to NN, people found strategies to avoid this issue with the gradients.
Basically, for some problems, you can use ...
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3
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Quantum algorithm for linear systems of equations (HHL09): Step 1 - Number of qubits needed
Calculation of the inverse of an $N\times N$ matrix can be done by applying HHL with $N$ different $\vec{b}_i$ (specifically, HHL is applied $N$ times, once for each computational basis vector used as ...
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3
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How to encode MNIST data set on a quantum circuit to study supervised learning with QNN?
First, they reduce the size from 28*28 to 4*4 images (by downsampling), then convert into binary values for pixels by just comparing to a value. Then, they encode the data in a quantum uniform ...
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Will deep learning neural networks run on quantum computers?
All of the answers here seem to be ignoring a fundamental practical limitation:
Deep Learning specifically works best with big data. MNIST is 60000 images, ImageNet is 14 Million images.
Meanwhile,...
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How did the authors manage to simulate and get the error estimate for a neural network with greater than 7840 qubits?
As of now we can properly simulate only ~50 qubits.
You are talking about a full quantum simulation of a vector containing $2^{50}$ elements.
In quantum neural networks and quantum annealing, we ...
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What are some of the interesting problems whose solutions have been proposed using quantum neural networks?
What are some other proposed applications of quantum neural networks?
Absolutely any application of classical neural networks can be an application of quantum neural networks. There's a lot of ...
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2
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Will deep learning neural networks run on quantum computers?
Here is a latest development from Xanadu, a photonic quantum circuit which mimics a neural network. This is an example of a neural network running on a quantum computer.
This photonic circuit ...
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Can quantum annealing be used for training convolutional neural networks?
I will assume you are asking about D-Wave's quantum annealer.
If there is a part of the learning process that can fit the QUBO (Quadratic Unconstrained Binary Optimization) formulation, then yes.
...
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Why is Farhi and Neven's architecture described in "Classification with Quantum Neural Network on near term processors" called a Neural Network?
On page 2, the authors of the paper write "We continue to use the
word 'neural' to describe our network since the term has been adopted by the machine
learning community recognizing that the ...
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Quantum NN vs Quantum-Inspired NN
A QNN is a "quantum implementation of a NN" that actually runs on a quantum device.
Look for example at this paper by Tacchino et al.
A QINN instead is a complex model that runs on traditional ...
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How is back-propagation done in "Transfer learning in hybrid classical-quantum neural networks"
How is back-propagation done through the classical weights feeding into the quantum unitaries?
In this particular case, the gradient of the quantum variational circuit is computed using the parameter-...
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Is this Quantum Neural Network overfitting?
First, it is useful to realize that your question is about statistical learning and not quantum computing. Your question better fits the Cross Validated stack.
But since your code contains a tiny bit ...
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Hybrid Quantum LSTM in Qiskit
Ok, so my recomendation is to use CircuitQNN and use the probs as output. That will solve the issue of speed and convergence at the same time.
I used embedding_dim = 8, hidden_dim = 6 and 3 qubits
1
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What are "unbounded loss functions" and "unbounded operators"?
In this context, I think that the authors simply refer to a bounded function
A linear operator is called bounded when it it has finite operator norm. This is equivalent to saying that the linear ...
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How could I choose cost function in Qiskit TwoLayerQNN?
Quantum neural networks in Qiskit Machine Learning do not store weights, they only provide a framework for computations of forward and backward passes. They are not aware of any algorithms that can be ...
1
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Initial assumption of the unitary that allows us to estimate the label function
As far as I understand from the paper, eq. (13) gives $U_l$ as a product of two qubit unitaries, independently of $l(z)$. Then the authors present two cases, subset parity and subset majority, and ...
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Question About Measuring an Operator For Quantum Neural Network Paper
$\newcommand{\ket}[1]{|{#1}\rangle}$
$\newcommand{\bra}[1]{\langle{#1}|}$
Applying $H$ to the auxiliary qubit results in:
$\frac{1}{2}(\ket{z,1}(\ket{0}+\ket{1}) + iU\ket{z,1}(\ket{0}-\ket{1}))$
$=...
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vote
Accepted
Is there a quantum neural network "hello world" for character recognition (convolutional neural networks)?
I don't think a hello world really exists here. You can have different points of view or goals here. I will give references.
The first one is speeding up parts of the algorithm with a quantum version ...
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