2 citations · 5 across the 10 of their papers we have counts for
11 papers · 1 filter
Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
Amana Liaqat, Ahmed Darwish, Stephen DiAdamo +6
The Minimum Birkhoff Decomposition (MBD) seeks a sparse weighted sum of matchings and is a challenging optimization problem with applications in network scheduling and energy tradi…
Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale
Mackenson Polché, Varun Puram, Aditi Lal +6
Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In t…
Constrained Quantum Optimization at Utility Scale: Application to the Knapsack Problem
Naeimeh Mohseni, Julien-Pierre Houle, Ibrahim Shehzad +3
Constrained combinatorial optimization problems are challenging for quantum computing, particularly at utility-relevant scales and on near-term hardware. At the same time, these pr…
Boosting Sparsity in Graph Decompositions with QAOA Sampling
George Pennington, Naeimeh Mohseni, Oscar Wallis +5
We study the problem of decomposing a graph into a weighted sum of a small number of matchings, a task that arises in network resource allocation problems such as peer-to-peer ener…
Enhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applications
Philip Anton Hernicht, Alona Sakhnenko, Corey O'Meara +2
Quantum machine learning (QML) presents potential for early industrial adoption, yet limited access to quantum hardware remains a significant bottleneck for deployment of QML solut…
Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems
David Bucher, Daniel Porawski, Maximilian Janetschek +4
This paper proposes a novel combination of constraint encoding methods for the Quantum Approximate Optimization Ansatz (QAOA). Real-world optimization problems typically consist of…
Grid Cost Allocation in Peer-to-Peer Electricity Markets: Benchmarking Classical and Quantum Optimization Approaches
David Bucher, Daniel Porawski, Benedikt Wimmer +4
This paper presents a novel optimization approach for allocating grid operation costs in Peer-to-Peer (P2P) electricity markets using Quantum Computing (QC). We develop a Quadratic…
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map…
Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids
Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara +3
Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling…
Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games
Jonas Nüßlein, Daniëlle Schuman, David Bucher +5
The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so…
Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation
Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara +2
Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy gene…