Alejandro Montanez's repositories
Quantum-Supply-Chain-Manager
The quantum supply chain manager is a quantum solution for logistics problems. We use the power of quantum machine learning for product [backorder](https://www.investopedia.com/terms/b/backorder.asp) prediction and quantum optimization for finding the best route to pick those products with high demand to store them in strategic warehouses. With the first technique, our clients can be prepared for increasing the production of their products when they are in high demand. Once we have established a set of products needed during a period of time, we use our second solution, the vehicle routing problem (VRP) solution to find the optimal route for picking these products. This reduces considerably costs associated with logistics, transportation, backorders, and overstocking for our clients. In summary, these solutions will improve business in terms of client satisfaction, backorder and shipping transportation costs.
Quantum-Counselor-for-Portfolio-Investment
The Quantum Counselor for portfolio investment is a tool with two main objectives: forecasting the trend of assets price and optimizing portfolio returns both using quantum computing techniques. For the case of the forecasting method, we use a hybrid method that combines a deep learning model of classical LSTM layers with quantum layers. For the case of portfolio optimization, the quantum algorithms of QAOA and VQE are used to solve the problem and will be compared with CPLEX, a classical solver. Both tools are deeply connected because the forecasted price of the different assets is used for the cost function construction.
Qiskit_Fall_Fest_Mexico_2022
Tutorial sobre optimización de portafolios usando computación cuántica.
clever-portfolio-optimization-encoding
Enhancing portfolio optimization solutions: wisely encoding constrained combinatorial optimization problems on quantum devices
BinPackingProblemNewApproach
A heuristic method for inequality constrained optimization problems
Generating-randomly-perturbed-density-operators
This code presents a general method for producing randomly perturbed density operators subject to different sets of constraints. The perturbed density operators are a specified “distance” away from the state described by the original density operator. This approach is applied to a bipartite system of qubits and used to examine the sensitivity of various entanglement measures on the perturbation magnitude. The constraint sets used include constant energy, constant entropy, and both constant energy and entropy. The method is then applied to produce perturbed random quantum states that correspond with those obtained experimentally for Bell states on the IBM quantum device ibmq manila. The results show that the methodology can be used to simulate the outcome of real quantum devices where noise, which is important both in theory and simulation, is present.
Neepy
The Non-Equilibrium Evolution PYthon based library (*Neepy*) is a programming tool designed to simulate the evolution of quantum systems out of equilibrium. This library presents a fast prototyping of the evolution of quantum systems based on commonly used evolution equations such as the von Neumann, the Lindblad, or the SEAQT equations of motion.
Quantum-Powered-Planner-For-EV-Charging-Networks
QUANTUM-POWERED PLANNER FOR EV CHARGING NETWORKS: Greening the road ahead: Charging demand location model for sustainable electric vehicle adoption.
Bin-Packing-Problem
Solution of the Bin-Packing problem using QAOA and Qiskit optimization library
CohortProject_2022
Open collaboration of the Quantum Bootcamp 2022
collateral-optimization
Collateral Optimization using MILP and QUBO formulations.
dimod
A shared API for QUBO/Ising samplers.
Hackathon2022
CDL Quantum Hackathon 2022
ibm-quantum-challenge-2024
For IBM Quantum Challenge 2024 (5-14 June 2024)
openqaoa
Multi-backend SDK for quantum optimisation
openqaoa-website
A website for OpenQAOA build with MkDocs
OracleOfAssets
The oracle of assets is a tool for investors that identifies a subgroup of assets that minimize the risk to lose money. Using neutral atoms, we can identify such a subgroup using an algorithm known as Maximum Independent Set (MIS). The subgroup of assets will not share a correlation between them, therefore if the price in the market of one of the assets decreases it won't affect the price of the others. Our algorithm will ensure that the subgroup will have the greater variety of assets possible for an investor to invest in.
pennylane
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
qiskit-app-benchmarks
Qiskit Application Benchmarks
Qiskit-Fall-Fest-Latinoamerica-2023
Qiskit and OpenQAOA tutorial
qml
Introductions to key concepts in quantum machine learning, as well as tutorials and implementations from cutting-edge QML research.
QuEra-braket-examples
Public repository for examples created by QuEra.
stable-diffusion
A latent text-to-image diffusion model
tequila
Rapid development of novel quantum algorithms