Timeline for Creating an ansatz for variational quantum algorithms?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| May 31, 2019 at 20:41 | comment | added | Kevin Sung | @EnriqueSegura Sorry, I don't quite understand your question. Would you mind providing some more detail? | |
| Apr 14, 2019 at 19:26 | comment | added | Enrique Segura | How can we extract the hyperplane corresponding to the network of this system that one is taking the gradient at every step to find the hyperparameters that satisfy the loss function condition? | |
| Mar 2, 2019 at 17:56 | vote | accept | Jack Ceroni | ||
| Feb 28, 2019 at 17:33 | comment | added | Kevin Sung | Classically, each spin can be +1 or -1. There are $2^n$ possible spin configurations, where $n$ is the number of spins. Each configuration is assigned a scalar energy value according to the energy function. When mapping this problem to a quantum problem, the energy function is replaced with a Hamiltonian, and the values of the energy function correspond to eigenvalues of the Hamiltonian. It turns out that replacing each spin variable with a Pauli Z operator accomplishes exactly this mapping, with computational basis vectors being the eigenvectors. | |
| Feb 28, 2019 at 3:29 | comment | added | Jack Ceroni | Thank you for your answer! I was just wondering, about the initial Hamiltonian, why we are able to chose a Z-rotation for both terms? In the tutorial, we start off with the Ising model, which has spin configurations of +1/-1, but why are we able to accurately map this to a problem that can be run on a quantum computer by replacing the +1/-1 spin values with Z-rotations? | |
| Feb 28, 2019 at 1:54 | history | edited | Kevin Sung | CC BY-SA 4.0 |
added 42 characters in body
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| Feb 28, 2019 at 1:45 | review | First posts | |||
| Feb 28, 2019 at 9:25 | |||||
| Feb 28, 2019 at 1:44 | history | answered | Kevin Sung | CC BY-SA 4.0 |