---
# System prepended metadata

title: Integrating quantum computation with chemical bonding logic to solve the mean-variance portfolio optimization problem using Qiskit Dynamic Solvers.

---

# Integrating quantum computation with chemical bonding logic to solve the mean-variance portfolio optimization problem using Qiskit Dynamic Solvers. 

*Methdology, code and example computation.*

**I-Methdology**

Integrating quantum computation with chemical bonding logic to solve the mean-variance portfolio optimization problem using Qiskit Dynamic Solvers requires a multi-step approach. Here is an outline of the process:

1. **Encode the chemical bonding problem into qubits**: First, you need to represent the chemical bonding problem as a quantum problem. You can do this using quantum Hamiltonians, which describe the energy of a molecular system. The Schrödinger equation and the Lennard-Jones potential can be used to construct the Hamiltonian. The problem can then be translated into a quantum Ising model, which can be solved using quantum algorithms.

2. **Solve the chemical bonding problem using a quantum algorithm**: Next, you will need to implement a quantum algorithm to solve the molecular bonding problem. The Variational Quantum Eigensolver (VQE) is an algorithm that can be used to find the ground state energy of a given Hamiltonian. In Qiskit, you can use the `VQE` class from the `qiskit.algorithms` module to implement this algorithm.

3. **Map the chemical bonding problem to the mean-variance portfolio optimization problem**: After solving the molecular bonding problem, you will need to map the solution to the mean-variance portfolio optimization problem. This can be done by treating the molecular bond strengths as asset returns and the electron densities as asset covariances. By representing the problem in this manner, you can apply the mean-variance portfolio optimization framework to the chemical bonding problem.

4. **Solve the mean-variance portfolio optimization problem using Qiskit Dynamic Solvers**: Finally, you will need to use Qiskit Dynamic Solvers to solve the mean-variance portfolio optimization problem. The `QuadraticProgram` class from the `qiskit.optimization` module can be used to represent the optimization problem, and the `MinimumEigenOptimizer` class from the `qiskit.optimization.algorithms` module can be used to solve the problem using quantum algorithms.

**II-Code**

Here is a high-level implementation of the process using Qiskit:

```Python
from qiskit import Aer
from qiskit.algorithms import VQE
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.algorithms import MinimumEigenOptimizer
from qiskit_nature.problems.second_quantization.electronic import ElectronicStructureProblem
from qiskit_nature.mappers.second_quantization import ParityMapper
from qiskit_nature.converters.second_quantization.qubit_converter import QubitConverter

# Encode the chemical bonding problem into qubits
problem = ElectronicStructureProblem(driver)  # driver represents the molecule's information
second_q_ops = problem.second_q_ops()
main_op = second_q_ops[0]

# Solve the chemical bonding problem using VQE
mapper = ParityMapper()
converter = QubitConverter(mapper=mapper, two_qubit_reduction=True)
qubit_op = converter.convert(main_op)
optimizer = ...  # Define a classical optimizer, e.g., COBYLA, SLSQP, etc.
ansatz = ...  # Define a suitable ansatz, e.g., TwoLocal, UCCSD, etc.
backend = Aer.get_backend('statevector_simulator')
vqe = VQE(ansatz, optimizer, quantum_instance=backend)
result = vqe.compute_minimum_eigenvalue(qubit_op)

# Map the chemical bonding problem to the mean-variance portfolio optimization problem
# This step requires custom implementation to map the molecular bond strengths and electron densities
# to asset returns and covariances

# Solve the mean-variance portfolio optimization problem using Qiskit Dynamic Solvers
qp = QuadraticProgram()
# Define the quadratic program using the mapped data from the chemical bonding problem
meo = MinimumEigenOptimizer(min_eigen_solver=vqe)
result = meo.solve(qp)
```

Please note that the code above is a high-level outline, and you will need to adapt it to your specific problem and provide additional implementation details.

****

Integrating quantum computation and chemical bonding logic into mean-variance portfolio optimization using Qiskit Dynamic Solvers can be understood better by breaking it down into specific steps.

1. **Transform the problem into a QUBO problem**

Quadratic Unconstrained Binary Optimization (QUBO) is a mathematical problem that involves minimizing a quadratic function, with binary variables representing portfolio allocation. The objective function is given by:

```
Minimize: f(x) = ∑(w_i * x_i)^2 - λ * ∑(r_i * x_i)
```

Here, `x_i` is a binary variable representing the allocation of the i-th asset, `w_i` is its weight, and `r_i` is its return rate. The parameter λ is the risk-aversion factor.

2. **Transform the QUBO problem into an Ising Hamiltonian**

An Ising Hamiltonian is a mathematical representation of a quantum system, which can be solved using quantum computing algorithms. The objective function of the QUBO problem can be transformed into an Ising Hamiltonian using the following equation:

```
H = ∑(w_i * σ_i^z)^2 - λ * ∑(r_i * σ_i^z)
```

Here, `σ_i^z` is the Pauli-Z operator acting on the i-th qubit.

3. **Solve using SamplingVQE or QAOA algorithms**

SamplingVQE and Quantum Approximate Optimization Algorithm (QAOA) are quantum algorithms that can solve the Ising Hamiltonian problem. These algorithms help in finding the optimal qubit states that minimize the energy of the Hamiltonian.

4. **Integrate chemical bonding logic**

Chemical bonding logic can be integrated by representing the particles' bonding structure using qubits. Each qubit represents the presence or absence of a bond between particles, and the Ising Hamiltonian can be adapted to incorporate this bonding information.

5. **Extract insights and make informed investment decisions**

Analyze the resulting qubit states to understand the bonding mechanism of particles and the factors that influence their stability and reactivity. Use these insights to make informed investment decisions and optimize portfolio allocation.

It is important to note that the mathematical accuracy of this approach is dependent on the accuracy and efficiency of the quantum algorithms used. As quantum computing technology evolves, better algorithms and more accurate solutions will become available.

****

**III-Example**

A simple example to help you understand how a quantum algorithm can be used to solve the mean-variance portfolio optimization problem. For this example, we will use Qiskit, a popular quantum computing library for Python.

```Python
import numpy as np
from qiskit import BasicAer
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import QAOA
from qiskit.optimization.algorithms import MinimumEigenOptimizer
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.applications.ising import portfolio

# Define problem-specific parameters
n = 4  # number of assets
q = 0.5  # risk factor
budget = n // 2  # budget
mu = np.array([0.7313, 0.9893, 0.2725, 0.8750])  # expected returns
sigma = np.array([
    [0.7312, 0.9891, 0.2725, 0.8750],
    [0.9891, 1.7382, 0.8236, 1.2396],
    [0.2725, 0.8236, 0.7500, 0.7329],
    [0.8750, 1.2396, 0.7329, 1.8639],
])  # covariance matrix

# Create the portfolio optimization problem in QUBO format
qubo = portfolio.get_operator(mu, sigma, q, budget)

# Define the QAOA algorithm to find the optimal solution
qaoa = QAOA(quantum_instance=QuantumInstance(BasicAer.get_backend('statevector_simulator')))

# Create QuadraticProgram and optimize it using MinimumEigenOptimizer
qp = QuadraticProgram()
qp.from_ising(qubo[0], qubo[1], linear=True)
optimizer = MinimumEigenOptimizer(qaoa)
result = optimizer.solve(qp)

# Analyze the result
optimal_solution = result.x
optimal_value = result.fval
print("Optimal Solution:", optimal_solution)
print("Optimal Value:", optimal_value)
```

This code snippet sets up a simple portfolio optimization problem with 4 assets, a risk factor of `q=0.5`, and a budget constraint. It then converts the problem into a QUBO representation using the `get_operator` function from the Qiskit's portfolio module.

The QAOA algorithm is used to find the optimal solution, and the MinimumEigenOptimizer is applied to the QUBO problem. The optimal solution and the optimal value are printed as the output.

Keep in mind that this is a highly simplified example with only 4 assets and limited data. In practice, you would use a larger set of assets, accurate historical data, and more advanced quantum algorithms to improve the accuracy and efficiency of the solution.

