23
votes
Accepted
Introductory material for quantum machine learning
The Nielsen and Chuang of Quantum Machine Learning is this extensive review called "Quantum Machine Learning" published in Nature in 2017. The arXiv version is here and has been updated as recently as ...
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17
votes
Accepted
Quantum machine learning after Ewin Tang
I am not an expert in the field but there are a few points that I am aware of:
There are proofs that certain quantum machine learning algorithms cannot be efficiently simulated on a classical ...
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15
votes
Accepted
Comparing method of differentiation in variational quantum circuit
Both finite differences and the parameter-shift rule can be used to compute quantum gradients on quantum hardware. However, there are several reasons that lead to the parameter-shift rule being ...
- 839
14
votes
Is there any potential application of quantum computers in machine learning or AI?
I will only answer to the part of the question regarding how quantum mechanics can be useful to analyse classical data with machine-learning-like techniques.
There are also works related to "quantum ...
glS♦
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14
votes
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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11
votes
Introductory material for quantum machine learning
Here's a list of other resources to learn about quantum machine learning:
An introduction to quantum machine learning
The quest for a Quantum Neural Network
Quantum Machine Learning: What Quantum ...
9
votes
What is the advantage of quantum machine learning over traditional machine learning?
As so often, and especially in young research areas, the answer depends quite a lot on how you break down the question. Let me try a few examples:
Does quantum mechanics change what is theoretically ...
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8
votes
Accepted
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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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:
...
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7
votes
Accepted
Is a "kernel" just the quantum equivalent of classical SVMs?
Consider a simple implementation of a Support Vector Machine (SVM) that finds a hyperplane (defined by its normal vector $w$) that maximally separates vectors $\{v_1, \dots, v_m\}$ according to their ...
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6
votes
Is there any potential application of quantum computers in machine learning or AI?
There are arguments that our brains are quantum mechanical, and arguments against, so that's a hotly debated topic. Fisher at UCSB has some speculative thinking about how brains might still use ...
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6
votes
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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6
votes
Accepted
What does the quantum part of the quantum support vector machine actually do?
The basic idea of how the quantum feature map works is that you're using a quantum computer to map each input datapoint $x$ from your training domain $\mathcal{X}$ into a quantum state $|\phi(x)\...
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6
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Claimed "potential revenue" from machine learning in 2023?
"IonQ is claiming to have a potential application in machine learning by 2023. What applications could they have in mind?"
None.
The plot you showed has no units on the y-axis. It doesn't ...
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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 ...
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5
votes
Is there any potential application of quantum computers in machine learning or AI?
Much of the work done so far with quantum computers has been focused on solving combinatorial optimization problems. Both D-Wave style Quantum Annealers and the more recent Gate Model machines from ...
5
votes
Introductory material for quantum machine learning
The most recent quantum machine learning textbook is
Schuld and Petruccione (2018). Supervised Learning with Quantum Computers
while a nice companion to Nielsen and Chuang for introductory quantum ...
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5
votes
Accepted
Embedding classical information into norm of a quantum state
I don't understand their notion of a $2^n$ dimensional complex
vector. If each of the components of their classical data array has
two floating point numbers, wouldn't encoding that into a $n$-...
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5
votes
Accepted
HHL algorithm -- controlled-by-eigenvalues rotations
If $\tilde{\lambda_{k}} < C$, the controlled rotation becomes non-physical since you have coeffecient greater than 1 on your $|1\rangle$ state.
As a result $C < \lambda_{min}$ is a safer choice,...
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5
votes
Accepted
SWAP Test as a Projective Measurement
There are two different ancillas floating around, one used in $|\psi\rangle$ and another to conduct the swap test later on:
In the above picture with subsystems explicitly labeled we have
\begin{...
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5
votes
Accepted
Why are 3, rather than 2 gates used in quantum variational circuits?
At the start of the circuit you're right that you only need two parameters. This is actually easy to show if you decompose into a sequence of rotations starting with a Z rotation, because Z rotations ...
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5
votes
Accepted
Sympy suddently does not work together with TFQ
Just to add a little more context to your answer: TensorFlow-Quantum 0.4.0 has an explicit version dependency on sympy==1.5.0 in the ...
- 393
5
votes
How to create a quantum circuit with 800+ features using PennyLane
You are asking numpy to initialize an array with dimension greater than $2^{896} \approx 5 \times 10^{269}$. For reference, it is thought that there are maybe $10^{82}$ or so atoms in the observable ...
- 6,108
4
votes
Accepted
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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4
votes
Accepted
Are there any examples of anyone applying quantum algorithms to problems in computational biology?
I was not able to find references specifically in quantum biology.
I found however a review called Quantum Assisted biomolecular modeling.
You may find it interesting but this is from 2010. The ...
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4
votes
Introductory material for quantum machine learning
A lot of focus in quantum machine learning in the near term revolves around variational quantum algorithms (you'll also see them called variational quantum circuits or parameterized quantum circuits), ...
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4
votes
Accepted
What is the advantage of quantum machine learning over traditional machine learning?
Potentially, the same advantage that quantum computing can provide over classical computing. By "quantum machine learning", in the way you seem to be using the term here, people usually ...
glS♦
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4
votes
Computing expectation value of product of observables in PennyLane
I think the following should work:
...
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4
votes
Accepted
Quantum SVM with large feature set
Practically, it can be (quite often) a limitation of number of qubits/hardware, but also it is a hyperparameter to play with. So it may be that using more qubits gives you better results or worse.
...
- 4,644
4
votes
Accepted
How is quantum machine learning reversible?
There are a couple of ways reversibility might be coming into play in this context. The first is that the measurement at the end of the circuit will be typically be an irreversible step. For example ...
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