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Your code looks fine - I am afraid you just were out of luck - Bogota appears to have some problems today (24th Oct 2021 as of 11 PM EST). Try switching to a different backend, like ibmq-manila or ibmq-santiago. You can check a list of all backends available to you here. On a related note, I would sincerely recommend also playing with simpler circuits at ...


3

The relation between "Ising" and binary variables is following $$ x_i = \frac{1 + s_i}{2}, $$ where $s_i$ is a spin and $x_i$ is a binary variable. Clearly setting $s_i = -1$ leads to $x_i = 0$ and if $s_i = 1$ we get $x_i$ = 1. So, this simple linear transform changes spins to binary variables and conversely. Quadratic terms in QUBO objective ...


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Adapting the answer above, if anyone needs implementation to this question in Qiskit. from itertools import product import numpy as np from qiskit.quantum_info import Operator from qiskit.circuit import library def make_square(matrix): # Pad with 0 to make square matrix if matrix.shape[0] != matrix.shape[1]: if matrix.shape[0] > matrix....


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You can select a basis of unitary matrices with respect to which you can decompose your matrix. For example, if your matrix $A$ is $2^n\times 2^n$, then you can select the Pauli basis $$ \sigma_y,\qquad y\in\{0,1,2,3\}^n $$ You can find the decomposition very easily. Notice that if $$ A=\sum_yA_y\sigma_y $$ then calculating $$ \text{Tr}(A\sigma_x)=A_x2^n $$ ...


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The paper doesn't address very much the "fully classical" approach to their problems, so I don't think they are making a judgment one way or another about quantum advantage with VQA. But they are part of a growing body of literature arguing very wisely that the complexity of classical optimization cannot be ignored, when assessing the complexity of ...


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