65 citations · 594 across the 65 of their papers we have counts for
66 papers
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…
Geometry-Induced Domain-Wall Pinning and Asymmetry in Nominally One-Dimensional Rydberg Arrays
Elijah Pelofske, Frank Barrows, Cristiano Nisoli
We perform experiments on QuEra's neutral-Rydberg-atom Aquila quantum computer, using quasi-adiabatic evolution on 1-dimensional models. We use hardware fine-tuning to balance the…
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…
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…
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…
Partition Function Estimation Using Analog Quantum Processors
Thinh Le, Elijah Pelofske
We evaluate using programmable superconducting flux qubit D-Wave quantum annealers to approximate the partition function of Ising models. We propose the use of two distinct quantum…
Probing Antiferromagnetic Hysteresis on Programmable Quantum Annealers
Elijah Pelofske, Pratik Sathe, Cristiano Nisoli +1
Using programmable analog quantum annealing processors, we implement a sampling-based magnetic hysteresis protocol to probe the counterintuitive notion of magnetic memory in antife…
Boltzmann Sampling of Frustrated J1 - J2 Ising Models with Programmable Quantum Annealers
Elijah Pelofske
One of the surprising, and potentially very useful, capabilities of analog quantum computers, such as D-Wave quantum annealers, is sampling from the Boltzmann, or Gibbs, distributi…
Depth One Quantum Alternating Operator Ansatz as an Approximate Gibbs Distribution Sampler
Elijah Pelofske
This study numerically investigates the thermal sampling properties of QAOA, the Quantum Alternating Operator Ansatz which was generalized from the original Quantum Approximate Opt…
Erasing Classical Memory with Quantum Fluctuations: Shannon Information Entropy of Reverse Quantum Annealing
Elijah Pelofske, Cristiano Nisoli
Quantum annealers can provide non-local optimization by tunneling between states in a process that ideally eliminates memory of the initial configuration. We study the crossover be…
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…
Magnetic Memory and Hysteresis from Quantum Transitions: Theory and Experiments on Quantum Annealers
Frank Barrows, Elijah Pelofske, Pratik Sathe +2
Quantum annealing leverages quantum tunneling for non-local searches, thereby minimizing memory effects that typically arise from metastabilities. Nonetheless, recent work has demo…
Magnetic Hysteresis Experiments Performed on Quantum Annealers
Elijah Pelofske, Frank Barrows, Pratik Sathe +1
While quantum annealers have emerged as versatile and controllable platforms for experimenting on correlated spin systems, the important phenomenology of magnetic memory and hyster…
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…
Digital Zero-Noise Extrapolation with Quantum Circuit Unoptimization
Elijah Pelofske, Vincent Russo
Quantum circuit unoptimization is an algorithm that transforms a quantum circuit into a different circuit that uses more gate operations while maintaining the same unitary transfor…
Efficient Online Quantum Circuit Learning with No Upfront Training
Tom O'Leary, Piotr Czarnik, Elijah Pelofske +3
We propose a surrogate-based method for optimizing parameterized quantum circuits which is designed to operate with few calls to a quantum computer. We employ a computationally ine…
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…
Classical Combinatorial Optimization Scaling for Random Ising Models on 2D Heavy-Hex Graphs
Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz
Motivated by near term quantum computing hardware limitations, combinatorial optimization problems that can be addressed by current quantum algorithms and noisy hardware with littl…
Increasing the Hardness of Posiform Planting Using Random QUBOs for Programmable Quantum Annealer Benchmarking
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Posiform planting is a method for constructing QUBO problems with a single unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investiga…
Biased Degenerate Ground-State Sampling of Small Ising Models with Converged QAOA
Elijah Pelofske
The Quantum Alternating Operator Ansatz, a generalization of the Quantum Approximate Optimization Algorithm (QAOA), is a quantum algorithm used for approximately solving combinator…
Automated Software Vulnerability Static Code Analysis Using Generative Pre-Trained Transformer Models
Elijah Pelofske, Vincent Urias, Lorie M. Liebrock
Generative Pre-Trained Transformer models have been shown to be surprisingly effective at a variety of natural language processing tasks -- including generating computer code. We e…
An Efficient All-to-All GCD Algorithm for Low Entropy RSA Key Factorization
Elijah Pelofske
RSA is an incredibly successful and useful asymmetric encryption algorithm. One of the types of implementation flaws in RSA is low entropy of the key generation, specifically the p…