50 citations · 253 across the 20 of their papers we have counts for
20 papers
Block encoding the 3D heterogeneous Poisson equation with application to fracture flow
Austin Pechan, John Golden, Daniel O'Malley
Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real…
Trainability Barriers in Low-Depth QAOA Landscapes
Joel Rajakumar, John Golden, Andreas Bärtschi +1
The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifyin…
JuliQAOA: Fast, Flexible QAOA Simulation
John Golden, Andreas Bärtschi, Daniel O'Malley +2
We introduce JuliQAOA, a simulation package specifically built for the Quantum Alternating Operator Ansatz (QAOA). JuliQAOA does not require a circuit-level description of QAOA pro…
Hierarchical Multigrid Ansatz for Variational Quantum Algorithms
Christo Meriwether Keller, Stephan Eidenbenz, Andreas Bärtschi +3
Quantum computing is an emerging topic in engineering that promises to enhance supercomputing using fundamental physics. In the near term, the best candidate algorithms for achievi…
Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs
Elijah Pelofske, Andreas Bärtschi, Lukasz Cincio +2
We show through numerical simulation that the Quantum Approximate Optimization Algorithm (QAOA) for higher-order, random-coefficient, heavy-hex compatible spin glass Ising models h…
Addressing Quantum's "Fine Print": State Preparation and Information Extraction for Quantum Algorithms and Geologic Fracture Networks
Jessie M. Henderson, John Kath, John K. Golden +2
Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain…
Lower bounds on the number of rounds of the quantum approximate optimization algorithm required for guaranteed approximation ratios
Naphan Benchasattabuse, Andreas Bärtschi, Luis Pedro García-Pintos +3
The quantum approximate optimization algorithm, also known in its generalization as the quantum alternating operator ansatz, (QAOA) is a heuristic hybrid quantum-classical algorith…
High-Round QAOA for MAX -SAT on Trapped Ion NISQ Devices
Elijah Pelofske, Andreas Bärtschi, John Golden +1
The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-quantum algorithm that aims to sample the optimal solution(s) of discrete combinatorial optimization problems.…
The Quantum Alternating Operator Ansatz for Satisfiability Problems
John Golden, Andreas Bärtschi, Daniel O'Malley +1
We comparatively study, through large-scale numerical simulation, the performance across a large set of Quantum Alternating Operator Ansatz (QAOA) implementations for finding appro…
Quantum Algorithms for Geologic Fracture Networks
Jessie M. Henderson, Marianna Podzorova, M. Cerezo +4
Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it…
Quantum Computing and Preconditioners for Hydrological Linear Systems
John Golden, Daniel O'Malley, Hari Viswanathan
Modeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can…
A near-term quantum algorithm for solving linear systems of equations based on the Woodbury identity
Daniel O'Malley, Jessie M. Henderson, Elijah Pelofske +5
Quantum algorithms for solving linear systems of equations have generated excitement because of the potential speed-ups involved and the importance of solving linear equations in m…
Numerical Evidence for Exponential Speed-up of QAOA over Unstructured Search for Approximate Constrained Optimization
John Golden, Andreas Bärtschi, Stephan Eidenbenz +1
Despite much recent work, the true promise and limitations of the Quantum Alternating Operator Ansatz (QAOA) are unclear. A critical question regarding QAOA is to what extent its p…
Sampling on NISQ Devices: "Who's the Fairest One of All?"
Elijah Pelofske, John Golden, Andreas Bärtschi +2
Modern NISQ devices are subject to a variety of biases and sources of noise that degrade the solution quality of computations carried out on these devices. A natural question that…
Threshold-Based Quantum Optimization
John Golden, Andreas Bärtschi, Daniel O'Malley +1
We propose and study Th-QAOA (pronounced Threshold QAOA), a variation of the Quantum Alternating Operator Ansatz (QAOA) that replaces the standard phase separator operator, which e…
Fair Sampling Error Analysis on NISQ Devices
John Golden, Andreas Bärtschi, Daniel O'Malley +1
We study the status of fair sampling on Noisy Intermediate Scale Quantum (NISQ) devices, in particular the IBM Q family of backends. Using the recently introduced Grover Mixer-QAOA…
Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations
Daniel O'Malley, John K. Golden
Homomorphic encryption has been an area of study in classical computing for decades. The fundamental goal of homomorphic encryption is to enable (untrusted) Oscar to perform a comp…
Reverse Annealing for Nonnegative/Binary Matrix Factorization
John Golden, Daniel O'Malley
It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm perfo…
Pre- and post-processing in quantum-computational hydrologic inverse analysis
John K. Golden, Daniel O'Malley
It was recently shown that certain subsurface hydrological inverse problems -- here framed as determining the composition of an aquifer from pressure readings -- can be solved on a…
Learning to regularize with a variational autoencoder for hydrologic inverse analysis
Daniel O'Malley, John K. Golden, Velimir V. Vesselinov
Inverse problems often involve matching observational data using a physical model that takes a large number of parameters as input. These problems tend to be under-constrained and…