Update README

README.md

@@ -1,27 +1,28 @@
-# QUBO.jl <img src="https://raw.githubusercontent.com/JuliaLang/julia/master/doc/src/assets/julia.ico" height="26"/> :left_right_arrow:	 :atom_symbol:	
+# QUBO.jl Documentation
 
 
 ## Introduction
 
 `QUBO.jl` is an all-in-one package for working with QUBO formulations in [JuMP](https://github.com/jump-dev/JuMP.jl) and interfacing with QUBO solvers. This project aggregates three complementary packages: [`ToQUBO.jl`](https://github.com/psrenergy/ToQUBO.jl), [`QUBODrivers.jl`](https://github.com/psrenergy/QUBODrivers.jl) and [`QUBOTools.jl`](https://github.com/psrenergy/QUBOTools.jl).
 
-## What is a QUBO?
+## QUBO?
 
 QUBO is an acronym for *Quadratic Unconstrained Binary Optimization*. So every QUBO problem is comprised of:
-- an objective function at most quadratic
+- a linear or quadratic objective function
 - no constraints
 - binary variables
 
-We can represent a QUBO problem as follows.
+We can represent such problem as follows:
 
 ```math
 \begin{array}{rl}
    \min          & \mathbf{x}' Q\,\mathbf{x} \\
-   \mathrm{s.t.} & \mathbf{x} \in \mathbb{B}^{n}
+   \textrm{s.t.} & \mathbf{x} \in \mathbb{B}^{n}
 \end{array}
 ```
 
-QUBOs are suited for representing non-convex global optimization problems. With that said, the significant advances in computing systems and algorithms specialized for sampling QUBOs have contributed to their popularity.
+QUBOs are suited for representing non-convex global optimization problems.
+With that said, the significant advances in computing systems and algorithms specialized for sampling QUBOs have contributed to their popularity.
 
 Some of the paradigms that stand out for running QUBOs are quantum gate-based optimization algorithms (QAOA and VQE), quantum annealers and hardware-accelerated platforms (Coherent Ising Machines and Simulated Bifurcation Machines).
 
@@ -46,10 +47,8 @@ julia> ]add https://github.com/psrenergy/QUBO.jl#master
 ### Example
 
 ```julia
-using QUBO
 using JuMP
-ToQUBO = QUBO.ToQUBO
-Anneal = QUBO.QUBODrivers
+using QUBO
 
 model = Model(() -> ToQUBO.Optimizer(ExactSampler.Optimizer))
 
@@ -60,14 +59,14 @@ model = Model(() -> ToQUBO.Optimizer(ExactSampler.Optimizer))
 optimize!(model)
 
 for i = 1:result_count(model)
-    xᵢ = value.(x, result = i)
-    yᵢ = objective_value(model, result = i)
-    println("f($xᵢ) = $yᵢ")
+    xi = value.(x, result = i)
+    yi = objective_value(model, result = i)
+
+    println("f($xi) = $yi")
 end
 
 ```
 
-
 <div align="center">
     <h2>QUBO.jl Packages</h2>
     <a href="https://github.com/psrenergy/ToQUBO.jl">