181 citations · 334 across the 27 of their papers we have counts for
29 papers
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…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
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…
A Lie Theoretic Framework for Controlling Open Quantum Systems
Corey O'Meara
This thesis focuses on the Lie-theoretic foundations of controlled open quantum systems. We describe Markovian open quantum system evolutions by Lie semigroups, whose corresponding…
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…
Quantum Computing in the Computational Landscape of Power Electronics: Vision and Reality
Nikolaos G. Paterakis, Petros Karamanakos, Corey O'Meara +1
Quantum computing is rapidly emerging as a promising technology for solving complex optimization problems that arise in various engineering fields. Therefore, it holds significant…
Quantum Optimization Benchmarking Library - The Intractable Decathlon
Thorsten Koch, David E. Bernal Neira, Ying Chen +24
Through recent progress in hardware development, quantum computers have advanced to the point where benchmarking of (heuristic) quantum algorithms at scale is within reach. Particu…
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…
Reducing QUBO Density by Factoring Out Semi-Symmetries
Jonas Nüßlein, Leo Sünkel, Jonas Stein +6
Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing are prominent approaches for solving combinatorial optimization problems, such as those formulated as Quadra…
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…
A Machine Learning Approach to Boost the Vehicle-2-Grid Scheduling
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EV…
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…
Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection
David Bucher, Daniel Porawski, Benedikt Wimmer +5
Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identi…
Quantum Optimization for the Future Energy Grid: Summary and Quantum Utility Prospects
Jonas Blenninger, David Bucher, Giorgio Cortiana +6
In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aim…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Incentivising Demand Side Response through Discount Scheduling using Hybrid Quantum Optimization
David Bucher, Jonas Nüßlein, Corey O'Meara +5
Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand a…
Energy risk analysis with Dynamic Amplitude Estimation and Piecewise Approximate Quantum Compiling
Kumar J. B. Ghosh, Kavitha Yogaraj, Gabriele Agliardi +6
We generalize the Approximate Quantum Compiling algorithm into a new method for CNOT-depth reduction, which is apt to process wide target quantum circuits. Combining this method wi…
Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing
Gabriele Agliardi, Corey O'Meara, Kavitha Yogaraj +8
Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely ri…
Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
Raúl Berganza Gómez, Corey O'Meara, Giorgio Cortiana +2
The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent y…