181 citations · 287 across the 19 of their papers we have counts for
20 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…
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…
Hierarchical divide and conquer quantum approach to combinatorial optimization problems with tunable reduction
Mathias Schmid, Naeimeh Mohseni, Michael J. Hartmann
Combinatorial optimization is considered a promising class of problems in which quantum computers can show significant advantages. However, problems of practical relevance typicall…
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…
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…
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…
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…
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…
Transfer learning in predicting quantum many-body dynamics: from physical observables to entanglement entropy
Philipp Schmidt, Florian Marquardt, Naeimeh Mohseni
Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compellin…
Demonstrating Quantum Scaling Advantage in Approximate Optimization for Energy Coalition Formation with 100+ Agents
Naeimeh Mohseni, Thomas Morstyn, Corey O'Meara +3
The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a…
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…
Deep learning of many-body observables and quantum information scrambling
Naeimeh Mohseni, Junheng Shi, Tim Byrnes +1
Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibited remarkable power in modeling quantum many-body systems.…
Ising machines as hardware solvers of combinatorial optimization problems
Naeimeh Mohseni, Peter L. McMahon, Tim Byrnes
Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because…
Deep recurrent networks predicting the gap evolution in adiabatic quantum computing
Naeimeh Mohseni, Carlos Navarrete-Benlloch, Tim Byrnes +1
In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a function of the parameter varied during the adiabatic sweep is crucial in order to optimize…
Deep Learning of Quantum Many-Body Dynamics via Random Driving
Naeimeh Mohseni, Thomas Fösel, Lingzhen Guo +2
Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamic…
Floquet theory for temporal correlations and spectra in time-periodic open quantum systems: Application to squeezed parametric oscillation beyond the rotating-wave approximation
C. Navarrete-Benlloch, R. Garcés, N. Mohseni +1
Open quantum systems can display periodic dynamics at the classical level either due to external periodic modulations or to self-pulsing phenomena typically following a Hopf bifurc…
Error suppression in adiabatic quantum computing with qubit ensembles
Naeimeh Mohseni, Marek Narozniak, Alexey N. Pyrkov +3
Incorporating protection against quantum errors into adiabatic quantum computing (AQC) is an important task due to the inevitable presence of decoherence. Here we investigate an er…
Deterministic generation of hybrid high-N00N states with Rydberg ions trapped in microwave cavities
Naeimeh Mohseni, Shahpoor Saeidian, Jonathan P. Dowling +1
Trapped ions are among the most promising platforms for quantum technologies. They are at the heart of the most precise clocks and sensors developed to date, which exploit the quan…