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tl;dr: You need to compute the average of the parity of the observed bitstrings, with an understanding that the circuit was executed with some appended measurement gates like $V_{measure} U(\theta)$. Check out footnote 3 for an example implementation in Cirq. The core idea of how to do this is fairly simple, but I'm going to provide a series of ...


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You can find some information on another approach to portfolio optimization on quantum computer in this article: Quantum computational finance: quantum algorithm for portfolio optimization. The authors deal with minimizing risk descibed by function $w^T\Sigma w$, where $w$ is vector of asset weights and $\Sigma$ is a covariance matrix. The minimization is ...


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The objective of the portfolio optimization problem is to trade off expected return ($\mu^T x$) with the risk taken ($x^T \Sigma $x). This could be achieved by introducing a constraint on the risk, e.g. $x^T \Sigma x \leq R$, for an acceptable risk level $R$ and then maximize the return under this constraint. However, this is not a QUBO, i.e., it cannot be ...


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If we have prepared an arbitrary anzats/trial two-qubit state: $$\psi = a |00\rangle + b |01\rangle+ c |10\rangle+ d |11\rangle$$ And we want to calculate the expectation value of individual Pauli terms of this Hamiltonian that is the two qubit case of the used one in the VQE Cirq example: $$ \langle H \rangle = \alpha_1 \langle Z_1 Z_2 \rangle + \alpha_2 ...


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