Row 5285

Row ID: 5285 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 5285 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I have been trying to implement [this paper](https://arxiv.org/abs/1903.05076) on identity block initialisation strategy for barren plateau mitigation but I don't really understand how one would apply it to a parameterised circuit with many parameters and specifically \*how the initialisation of the parameters is obtained\*.

If I have understood it correctly, the idea is to use these identity blocks to obtain a list of parameters that then could be used as a starting point in training the circuit. Now, say I have an ansatz consisting of 2 parameters (a circuit of 2 qubits consisting of a RX gate on the first qubit and a CRZ controlled by the first and acting on the second qubit). My only reasonable understanding of the implementation is that I define a single trainable parameter y\_1, say for the RX gate, and choose a random value for the CRZ gate to then undo the circuit by applying the gates in reverse with the negative values of the CRZ parameter and some random value for the RX parameter? That would then be one block, the rest would be have parameters chosen randomly without any trainable parameters, and then being undone by their adjoint to create the second block. The circuit would then be trained using Qiskit's NeuralNetworkRegressor.fit() function and the value for the trainable parameter is then stored in a list before moving on to the next gate and repeating this procedure to store that parameter value in the list. The list would then be used as the initial\_point in the NeuralNetworkRegressor.fit().

In summary: I don't understand how one can obtain a set of initial values for the parameters of the gates of the circuit using this strategy. Does it involve training the circuit on a single parameter as I explained above, or am I completely lost?

Field	Value
text	I have been trying to implement [this paper](https://arxiv.org/abs/1903.05076) on identity block initialisation strategy for barren plateau mitigation but I don't really understand how one would apply it to a parameterised circuit with many parameters and specifically \how the initialisation of the parameters is obtained\. If I have understood it correctly, the idea is to use these identity blocks to obtain a list of parameters that then could be used as a starting point in training the circu…
label	r/quantumcomputing
dataType	post
communityName	r/QuantumComputing
datetime	2024-04-28
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Raw Record

{
  "text": "I have been trying to implement [this paper](https://arxiv.org/abs/1903.05076) on identity block initialisation strategy for barren plateau mitigation but I don't really understand how one would apply it to a parameterised circuit with many parameters and specifically \\*how the initialisation of the parameters is obtained\\*.\n\nIf I have understood it correctly, the idea is to use these identity blocks to obtain a list of parameters that then could be used as a starting point in training the circuit. Now, say I have an ansatz consisting of 2 parameters (a circuit of 2 qubits consisting of a RX gate on the first qubit and a CRZ controlled by the first and acting on the second qubit). My only reasonable understanding of the implementation is that I define a single trainable parameter y\\_1, say for the RX gate, and choose a random value for the CRZ gate to then undo the circuit by applying the gates in reverse with the negative values of the CRZ parameter and some random value for the RX parameter? That would then be one block, the rest would be have parameters chosen randomly without any trainable parameters, and then being undone by their adjoint to create the second block. The circuit would then be trained using Qiskit's NeuralNetworkRegressor.fit() function and the value for the trainable parameter is then stored in a list before moving on to the next gate and repeating this procedure to store that parameter value in the list. The list would then be used as the initial\\_point in the NeuralNetworkRegressor.fit().\n\nIn summary: I don't understand how one can obtain a set of initial values for the parameters of the gates of the circuit using this strategy. Does it involve training the circuit on a single parameter as I explained above, or am I completely lost?",
  "label": "r/quantumcomputing",
  "dataType": "post",
  "communityName": "r/QuantumComputing",
  "datetime": "2024-04-28",
  "username_encoded": "Z0FBQUFBQm5LakwyWHluaDN1N0JkR25lLVBTM1F6M3ByVDMwR0hyOWNKa0w2NXoxSEdrOEtQS3hseWJndkFPeVFvaVQybXBWclp1cG90Y0tOd1NqaFYwZXd5SlhoNU5JT3c9PQ==",
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}

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Entry Information

Entry ID: 5285
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000