8 votes
Accepted

Computing expectation value of product of observables in PennyLane

PennyLane supports measurements of tensor products of observable via the @ operator, like so: ...
Josh Izaac's user avatar
5 votes
Accepted

Does TensorFlow Quantum tfq.convert_to_tensor work on custom gates?

I'm the engineer who looks after TensorFlow Quantum. Serializing custom gates is not supported. There is an active issue on the GitHub here: https://github.com/tensorflow/quantum/issues/354 . A quick ...
Michael's user avatar
  • 393
4 votes

Computing expectation value of product of observables in PennyLane

I think the following should work: ...
KAJ226's user avatar
  • 13.8k
4 votes

Categories and types of quantum inspired algorithms

My reply is by no means an answer to your question. However, I still would like to drop in my 2 cents. The notion of "quantum-inspired" algorithms has no formal definition and has a rather ...
MonteNero's user avatar
  • 2,369
3 votes

Does this quantum algorithm to check for a permuting function make sense?

One problem with this approach is that in quantum state space, permuting the inputs looks a lot like permuting most of the inputs but then doing something that isn't permutation with the remainder of ...
forky40's user avatar
  • 6,358
3 votes

Why does the parameter circuit include both the positive and negative shift terms?

Note that we are not interested in differentiating the gate $U(\theta)$, but some expectation-value-based function $E(\theta)$, which "contains the gate twice", if you will: $$E(\theta)=\...
David Wierichs's user avatar
3 votes

How do I calculate the amount of qubits required for Image Classification with a Quantum Convolutional Neural Network?

For images it depends on which quantum embedding method you choose. Let's take as an example MNIST dataset, as Leeseok Kim suggested, since it's simple and has small, grayscale images. Let's downsize ...
Marek Kowalik's user avatar
3 votes
Accepted

Label function for a QNN designed to classify bit strings

Yes, $l(z)$ is the true label, and so $l$ is the target function that you want to learn in order for the machine learning model to be correct. The set of functions $l$ that the model is capable of ...
forky40's user avatar
  • 6,358
3 votes

Understanding the definition of quantum neural network of Abbas et al. 2020

Typically when you use these kinds of variational circuits to do classification, your goal is to use the circuit to classify some input data $x\in\mathbb{R}^d$ with a decision function of the form $$\...
forky40's user avatar
  • 6,358
3 votes

Understanding the definition of quantum neural network of Abbas et al. 2020

Why are all the states 0 ? Oversimplified, there are three main components to any quantum circuit: the input, the quantum function, and the output. QML research will usually fall into two buckets. In ...
ryanhill1's user avatar
  • 2,473
3 votes
Accepted

Data encoding in the quantum perceptron model

Why that $|1\rangle^{\otimes N}$? The reason for requiring the final state to be $|1\rangle^{\otimes N}$, is that in this way you can easily propagate the information on an ancilla (i.e. additional) ...
Stefano Mangini's user avatar
2 votes
Accepted

How to upload our dataset using pytorch when it is not present in torchvision?

Yes, since the dataset does not exist in torchvision, trying to load it through torchvision will produce this error. Instead, save your SWELL-KW dataset as a .csv file, read it in, and convert the ...
ryanhill1's user avatar
  • 2,473
2 votes

What is a "repeat until success quantum circuit" in quantum neural networks?

A non-linear process involves a measurement. The idea of a repeat until success sequence is that when you do the measurement, one of the results will be the one you want. If you get it, great! You've ...
DaftWullie's user avatar
  • 56.9k
2 votes
Accepted

How do I calculate the amount of qubits required for Image Classification with a Quantum Convolutional Neural Network?

It usually depends on what types of data you're using and how many qubits you want to start your QCNN with. Suppose you want to encode MNIST image data as an input of your QCNN. There are several ways ...
Leeseok Kim's user avatar
2 votes

Graph Limits in Quantum Computing

Because the adjacency matrix of undirected graphs are symmetric about the diagonal, these graphs are hermitian, and you are correct to suppose that quantum computing can be a natural vehicle for ...
Mark Spinelli's user avatar
1 vote
Accepted

Quantum neural networks and quantum kernels deal with nonlinearities

The quoted statement is a bit vague taken out of context, but a few comments on what they might have meant: Any quantum operation (meaning any physical evolution that is compatible with quantum ...
glS's user avatar
  • 23.9k
1 vote

What are necessary and sufficient conditions for the output of a parametrized unitary $U(\theta)$ to be smooth?

I'm still not sure I fully understand what you're asking but here's my take. Let $\rho$ be the input to my circuit, at the end of the circuit I receive an output $\rho_U = U \rho U^\dagger$. Now ...
Rammus's user avatar
  • 5,435
1 vote

How to add noise mode in Sampler (SamplerQNN) in qiskit for quantum neural network?

To add noise in Aer it's done via the backend_options supplied to the primitive. Here is an example using the Aer Estimator primitive but the technique is the same ...
Steve Wood's user avatar
  • 1,333
1 vote
Accepted

Unitary operations in a Quantum Neural Network

Following the remark of @glS, we can reformulate the problem using the Baker-Campbell-Hausdorf formula and we manage to derive the right results. Here are some details for the calculations: \begin{...
Do a Phase Flip's user avatar
1 vote
Accepted

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 ...
Markus Heinrich's user avatar

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