1 citations · 1 across the 14 of their papers we have counts for
16 papers
Learning Trotter Orderings for Heisenberg Hamiltonians with a Ranking Transformer
Shamminuj Aktar, Reuben Tate, Stephan Eidenbenz
Trotterization approximates quantum time evolution by sequentially applying Hamiltonian terms. Because noncommuting terms introduce ordering-dependent errors, selecting an optimal…
The Quantum Hamiltonian Analysis Toolkit: Lowering the Barrier to Quantum Computing with Hamiltonians
Brendan K. Krueger, Stephan Eidenbenz, Shamminuj Aktar +8
We present the Quantum Hamiltonian Analysis Toolkit (QHAT), a newly developed application that provides a user-friendly interface for studying Hamiltonians and performing Hamiltoni…
Quantum Hypergraph Partitioning
Cameron Ibrahim, Bao G. Bach, Jad Salem +4
Quantum optimization algorithms are inherently probabilistic, yet they are most often used to search for a single high-quality solution. In this paper, we instead study hypergraph…
SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis
Sean Feeney, Pooja Rao, Andreas Klappenecker +5
In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in quantum circuit analysis built o…
Structure-Aware Transformers for Learning Near-Optimal Trotter Orderings with System-Size Generalization in 1D Heisenberg Hamiltonians
Shamminuj Aktar, Reuben Tate, Stephan Eidenbenz
Trotterization is a standard approach for simulating quantum time evolution on quantum computers, where the Hamiltonian is split into local terms and each term is applied in sequen…
An Analysis of Commutation-Based Trotter Ordering Strategies on Heisenberg-Style Hamiltonians
Reuben Tate, Shamminuj Aktar, Stephan Eidenbenz
Trotterization is a technique that allows one to approximate a time evolution of a Hamiltonian by repeatedly evolving the individual terms of the Hamiltonian one-at-a-time for smal…
Learning Cut Distributions with Quantum Optimization
Bao Bach, Cameron Ibrahim, Reuben Tate +3
Many combinatorial optimization problems admit a maximin fairness variant, where the aim is to find a distribution over possible solutions which maximizes an expected worst-case ou…
MPS-JuliQAOA: User-friendly, Scalable MPS-based Simulation for Quantum Optimization
Sean Feeney, Reuben Tate, John Golden +1
We present the MPS-JuliQAOA simulator, a user-friendly, open-source tool to simulate the Quantum Approximate Optimization Algorithm (QAOA) of any optimization problem that can be e…
Solving General QUBOs with Warm-Start QAOA via a Reduction to Max-Cut
Bikrant Bhattacharyya, Michael Capriotti, Reuben Tate
The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm that finds approximate solutions to problems in combinatorial optimization, especially those that can b…
Enhancing Quantum Expectation Values via Exponential Error Suppression and CVaR Optimization
Touheed Anwar Atif, Reuben Blake Tate, Stephan Eidenbenz
Precise quantum expectation values are crucial for quantum algorithm development, but noise in real-world systems can degrade these estimations. While quantum error correction is r…
Snapshot-QAOA: Extending QAOA to Quantum Hamiltonian Simulation
Reuben Tate, Quinn Langfitt, Elijah Pelofske +4
We present Snapshot-QAOA, a variation of the Quantum Approximate Optimization Algorithm (QAOA) that finds approximate minimum energy eigenstates of a large set of quantum Hamiltoni…
Phantom Edges in the Problem Hamiltonian: A Method for Increasing Performance and Graph Visibility for QAOA
Quinn Langfitt, Reuben Tate, Stephan Eidenbenz
The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm that can be used to approximately solve combinatorial optimization problems. However, a maj…
Expected Maximin Fairness in Max-Cut and other Combinatorial Optimization Problems
Jad Salem, Reuben Tate, Stephan Eidenbenz
Maximin fairness is the ideal that the worst-off group (or individual) should be treated as well as possible. Literature on maximin fairness in various decision-making settings has…
Warm-Started QAOA with Aligned Mixers Converges Slowly Near the Poles of the Bloch Sphere
Reuben Tate, Stephan Eidenbenz
In order to boost the performance of the Quantum Approximate Optimization Algorithm (QAOA) to solve problems in combinatorial optimization, researchers have leveraged the solutions…
The Better Solution Probability Metric: Optimizing QAOA to Outperform its Warm-Start Solution
Sean Feeney, Reuben Tate, Stephan Eidenbenz
This paper presents a numerical simulation investigation of the Warm-Start Quantum Approximate Optimization Algorithm (QAOA) as proposed by Tate et al. [1], focusing on its applica…
Theoretical Approximation Ratios for Warm-Started QAOA on 3-Regular Max-Cut Instances at Depth
Reuben Tate, Stephan Eidenbenz
We generalize Farhi et al.'s 0.6924-approximation result technique of the Max-Cut Quantum Approximate Optimization Algorithm (QAOA) on 3-regular graphs to obtain provable lower bou…