Quantum circuit compilation plays a fundamental role in optimizing quantum algorithms for execution on near-term quantum hardware. Traditional compilation approaches rely on heuristic or rule-based methods, which may not fully exploit the potential of advanced machine learning techniques. This study explores the synergy between reinforcement learning (RL) and supervised learning (SL) to enhance quantum circuit compilation efficiency. The proposed hybrid approach leverages RL for decision-making in circuit transformations while using SL to refine and accelerate learning through historical data. Experimental results demonstrate that this methodology achieves superior gate count reduction and depth optimization compared to conventional techniques, paving the way for more efficient quantum computing applications.

Keywords: Quantum circuit compilation, reinforcement learning, supervised learning, hybrid machine learning, quantum computing optimization.

Would you like to learn more about how reinforcement learning and supervised learning can improve quantum circuit compilation? This study was published on arXiv, an open-access repository for cutting-edge research in physics, mathematics, and computer science. You can read the full article at the following link: https://arxiv.org/pdf/2503.15394.

Ensuring the reliability of auxiliary services in electrical substations during power outages is critical for the stability and safety of power systems. Traditional backup solutions, such as diesel generators, often involve high operational costs and environmental concerns. This study presents a stochastic programming approach to optimally size hybrid backup systems that integrate renewable energy sources and battery storage to support substation auxiliary services during outages. The proposed model accounts for the uncertainties associated with renewable energy generation and load demand, aiming to minimize the total cost while ensuring reliability. A case study demonstrates the effectiveness of the approach, showing significant improvements in cost efficiency and sustainability compared to conventional backup systems.

Keywords: Substation auxiliary services, hybrid backup systems, stochastic programming, renewable energy integration, battery storage, reliability, cost optimization.

Would you like to learn more about optimizing hybrid backup systems for substation auxiliary services and how stochastic programming enhances their reliability and cost-effectiveness? This study was published in Electric Power Systems Research, a leading journal in power system engineering and optimization. You can read the full article at the following link: https://www.sciencedirect.com/science/article/abs/pii/S0378779624010770

This study analyzes the economic impact of floods in the Andean region of Colombia and their relationship with climate change by examining meteorological variables such as precipitation, air temperature, and relative humidity. Using an Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM), the research quantifies both the short-term and long-term effects of these variables on flood damage costs, employing monthly data from October 2006 to December 2023.

The results show a statistically significant relationship between meteorological conditions and flood damage costs. Specifically, precipitation has a positive and significant impact on flood costs in both the short and long run, whereas air temperature shows a negative impact. To enhance the analysis, climate change projections were incorporated using Global Climate Models (GCMs) and statistical downscaling techniques (SD). These projections indicate an increase in flood frequency and severity in the coming decades, reinforcing the urgency of climate adaptation and mitigation measures.

The study underscores the necessity of strengthening flood prevention policies, including investment in resilient infrastructure, improved early warning systems, and sustainable land-use planning. Additionally, the findings support broader climate policies aimed at reducing greenhouse gas emissions and deforestation, particularly in vulnerable areas like the Amazon.

Would you like to learn more about the impact of climate change on flood damage costs and how these effects can be projected into the future? This study was published in Sustainable Cities and Society, a high-impact journal in urban sustainability and resilience. You can read the full article at the following link: https://www.sciencedirect.com/science/article/abs/pii/S2210670725001866#d1e3829

As part of the event, distinctions were awarded to the most outstanding works presented. Esteban Leiva, a Master’s student in Industrial Engineering and a graduate of the Mathematics undergraduate program, received an honorable mention for the quality and rigor of his work. His contribution was evaluated alongside submissions from Ph.D. students from various international institutions.

This recognition highlights not only the student’s academic excellence but also the level of training and research fostered in postgraduate programs in the region.

For more information, visit: https://optimization-workshop.github.io/

The Optimization Workshop: Theory, Algorithms, and Applications was held in December 2024 in Bogotá, Colombia, at the Universidad de los Andes. The event brought together a diverse group of researchers and students from both local and international institutions, with the goal of strengthening collaboration in the field of mathematical optimization and promoting research in the region, particularly among early-career scholars.

One of the keynote speakers was Professor Andrés Medaglia, Director of the COPA research group, who delivered a talk titled “Leveraging Shortest-Path Structures for Engineering Solutions.”

The workshop was widely regarded as a success, providing a dynamic platform for the exchange of ideas and the development of new collaborations in optimization. This event marks a significant contribution toward building a stronger research community in the field, both in Colombia and across the region.

For more information, visit https://optimization-workshop.github.io/

COPA participated in the 2024 INFORMS/ALIO/ASOCIO International Conference, held from June 16th to 19th in Medellín, Colombia. This leading international event brings together top academics and practitioners in Operations Research and analytics from around the world to share cutting-edge research and developments.

The following (current and past) COPA members presented at the conference, listed in alphabetical order along with the titles of their presentations:

• Alfaima L. Solano-Blanco: "Sustainable crop planning for mixed cropping systems – An optimization approach"
• Andrés Gómez: "Robust support vector machines via conic optimization"
• Andrés L. Medaglia (Plenary): "A (Not So) Shortest Path: Models, Solutions, and Applications"
• Daniel Yamín: "Branch-Price-and-Cut for Infrastructure Maintenance Planning"
• Juan G. Villegas: "A Bilevel Vehicle Routing Problem for a Colombian Package Delivery Company"
• Julio Goez: "Assessing the Effect of Consolidation of Freight Transport: A Case Study in Norway"
• Luis Zuluaga: "Constraint Penalization for Non-convex Optimization Problems: Applications to Quantum Computing"
• Natalia Pacheco-Carvajal: "Automatic Classification of Academic Articles via Machine Learning"

You can find some pictures with their corresponding description below:

1. Top Left: Natalia Pacheco-Carvajal
2. Top Right: Michel Gendreau and Andrés L. Medaglia
3. Bottom Left: Andrés L. Medaglia and Juan G. Villegas
4. Bottom Center: Daniel Yamín
5. Bottom Right: Alfaima L. Solano-Blanco

For more information, visit https://meetings.informs.org/wordpress/2024international/ or contact us via email at This email address is being protected from spambots. You need JavaScript enabled to view it..