85 citations · 105 across the 26 of their papers we have counts for
28 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…
Effects of Trotter Error, Digitization Error, and Initial State Overlap on Tapered Quantum Phase Estimation for Minimum Eigenvalue Computation
Elijah Pelofske, Stephan Eidenbenz
We numerically implement full quantum circuits of optimal tapered Quantum Phase Estimation (in the form of an implementable approximate bandwidth-limited DPSS taper) for the purpos…
Entanglement Negativity in Noisy Quantum Volume Sampling
Elijah Pelofske, Stephan Eidenbenz
The Quantum Volume protocol uses scrambling random circuits to benchmark NISQ computers. Quantum Volume is generally well-regarded as a benchmark for small, noisy, quantum computer…
A Validation Framework for Quantum Simulation of Spin Dynamics against Inelastic Neutron Scattering and Classical Simulation
Gilles Buchs, Elaine Wong, Anshumitra Baul +16
Establishing quantum simulations as reliable tools for materials research requires quantitative validation against experiments and, where available, classical many-body calculation…
Learning Low-Energy Subspace Overlaps in Many-Body Systems with Measurement-Based and Coherent Quantum Strategies
Shamminuj Aktar, Rishabh Bhardwaj, Tanmoy Bhattacharya +1
Predicting the overlap of quantum states with specified low-energy subspaces is a key diagnostic for quantum many-body dynamics, with direct applications in state preparation, subs…
VQE as Initial State Preparation for QPE on Heisenberg Spin-Glass Hamiltonians
Elijah Pelofske, Stephan Eidenbenz
Quantum Phase Estimation (QPE) is the quantum algorithmic workhorse for computing ground state energies of quantum Hamiltonians with quantum computers. Ground state energy calculat…
Setting angles in quantum approximate optimization at utility-scale
Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh +16
The quantum approximate optimization algorithm (QAOA) is a powerful heuristic that seeks to solve combinatorial optimization problems using quantum hardware and classical optimizat…
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…
Numerical Experiments with Parameter Setting of Trotterized Quantum Phase Estimation for Quantum Hamiltonian Ground State Computation
Elijah Pelofske, Stephan Eidenbenz
We numerically investigate quantum circuit elementary-gate level instantiations of the standard Quantum Phase Estimation (QPE) algorithm for the task of computing the ground-state…
Quantum Homotopy Algorithm for Solving Nonlinear PDEs and Flow Problems
Sachin S. Bharadwaj, Balasubramanya Nadiga, Stephan Eidenbenz +1
Quantum algorithms to integrate nonlinear PDEs governing flow problems are challenging to discover but critical to enhancing the practical usefulness of quantum computing. We prese…
Quantum Data Learning of Topological-to-Ferromagnetic Phase Transitions in the 2+1D Toric Code Loop Gas Model
Shamminuj Aktar, Rishabh Bhardwaj, Andreas Bärtschi +2
Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states, bypassing the need for any identification of the classical observabl…
Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
Elijah Pelofske, Marek Rams, Andreas Bärtschi +4
The emergent practical applicability of the Quantum Approximate Optimization Algorithm (QAOA) for approximate combinatorial optimization is a subject of considerable interest. One…
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…
Variational Quantum Simulations of a Two-Dimensional Frustrated Transverse-Field Ising Model on a Trapped-Ion Quantum Computer
Ammar Kirmani, Elijah Pelofske, Andreas Bärtschi +2
Quantum computers are an ideal platform to study the ground state properties of strongly correlated systems due to the limitation of classical computing techniques particularly for…
Quantum Approximate Multi-Objective Optimization
Ayse Kotil, Elijah Pelofske, Stephanie Riedmüller +4
The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, i.e., the set of all Pareto optimal…
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…