768 citations · 2.6k across the 25 of their papers we have counts for
23 papers · 1 filter
Optimal testing of fermionic and bosonic Gaussian states
Vishnu Iyer, Bryan O'Gorman, Ojas Parekh +2
We study the problem of property testing quantum states: given a pure state that either (A) belongs to some class of pure states, or (B) is -far in tra…
Self-consistent Quantum Iteratively Sparsified Hamiltonian method (SQuISH): A new algorithm for efficient Hamiltonian simulation and compression
Diana B. Chamaki, Stuart Hadfield, Katherine Klymko +2
It is crucial to reduce the resources required to run quantum algorithms and simulate physical systems on quantum computers due to coherence time limitations. With regards to Hamil…
Fermionic tomography and learning
Bryan O'Gorman
Shadow tomography via classical shadows is a state-of-the-art approach for estimating properties of a quantum state. We present a simplified, combinatorial analysis of a recently p…
Response to "Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers"
Joonho Lee, David R. Reichman, Ryan Babbush +4
A recent preprint by Mazzola and Carleo numerically investigates exponential challenges that can arise for the QC-QMC algorithm introduced in our work, "Unbiasing fermionic quantum…
The Parameterized Complexity of Quantum Verification
Srinivasan Arunachalam, Sergey Bravyi, Chinmay Nirkhe +1
We initiate the study of parameterized complexity of problems in terms of the number of non-Clifford gates in the problem description. We show that for the problem o…
Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
William J. Huggins, Bryan A. O'Gorman, Nicholas C. Rubin +3
Many-electron problems pose some of the greatest challenges in computational science, with important applications across many fields of modern science. Fermionic quantum Monte Carl…
Electronic Structure in a Fixed Basis is QMA-complete
Bryan O'Gorman, Sandy Irani, James Whitfield +1
Finding the ground state energy of electrons subject to an external electric field is a fundamental problem in computational chemistry. We prove that this electronic-structure prob…
Quantum-accelerated constraint programming
Kyle E. C. Booth, Bryan O'Gorman, Jeffrey Marshall +2
Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that…
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley +83
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algor…
Planning for Compilation of a Quantum Algorithm for Graph Coloring
Minh Do, Zhihui Wang, Bryan O'Gorman +3
The problem of compiling general quantum algorithms for implementation on near-term quantum processors has been introduced to the AI community. Previous work demonstrated that temp…
A Non-Orthogonal Variational Quantum Eigensolver
William J. Huggins, Joonho Lee, Unpil Baek +2
Variational algorithms for strongly correlated chemical and materials systems are one of the most promising applications of near-term quantum computers. We present an extension to…
Discontinuous Galerkin discretization for quantum simulation of chemistry
Jarrod R. McClean, Fabian M. Faulstich, Qinyi Zhu +5
Methods for electronic structure based on Gaussian and molecular orbital discretizations offer a well established, compact representation that forms much of the foundation of corre…
Generalized swap networks for near-term quantum computing
Bryan O'Gorman, William J. Huggins, Eleanor G. Rieffel +1
The practical use of many types of near-term quantum computers requires accounting for their limited connectivity. One way of overcoming limited connectivity is to insert swaps in…
From Ansätze to Z-gates: a NASA View of Quantum Computing
Eleanor G. Rieffel, Stuart Hadfield, Tad Hogg +12
For the last few years, the NASA Quantum Artificial Intelligence Laboratory (QuAIL) has been performing research to assess the potential impact of quantum computers on challenging…
Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management
Tobias Stollenwerk, Bryan O'Gorman, Davide Venturelli +6
We present the mapping of a class of simplified air traffic management (ATM) problems (strategic conflict resolution) to quadratic unconstrained boolean optimization (QUBO) problem…
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Stuart Hadfield, Zhihui Wang, Bryan O'Gorman +3
The next few years will be exciting as prototype universal quantum processors emerge, enabling implementation of a wider variety of algorithms. Of particular interest are quantum h…
Readiness of Quantum Optimization Machines for Industrial Applications
Alejandro Perdomo-Ortiz, Alexander Feldman, Asier Ozaeta +9
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current…
A NASA Perspective on Quantum Computing: Opportunities and Challenges
Rupak Biswas, Zhang Jiang, Kostya Kechezhi +10
In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, howeve…
Determination and correction of persistent biases in quantum annealers
Alejandro Perdomo-Ortiz, Bryan O'Gorman, Joseph Fluegemann +2
Calibration of quantum computing technologies is essential to the effective utilization of their quantum resources. Specifically, the performance of quantum annealers is likely to…
A case study in programming a quantum annealer for hard operational planning problems
Eleanor G. Rieffel, Davide Venturelli, Bryan O'Gorman +3
We report on a case study in programming an early quantum annealer to attack optimization problems related to operational planning. While a number of studies have looked at the per…
Bayesian Network Structure Learning Using Quantum Annealing
Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush +2
We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a…
Resource Efficient Gadgets for Compiling Adiabatic Quantum Optimization Problems
Ryan Babbush, Bryan O'Gorman, Alán Aspuru-Guzik
We develop a resource efficient method by which the ground-state of an arbitrary k-local, optimization Hamiltonian can be encoded as the ground-state of a (k-1)-local optimization…
Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization
Ryan Babbush, Alejandro Perdomo-Ortiz, Bryan O'Gorman +2
Optimization problems associated with the interaction of linked particles are at the heart of polymer science, protein folding and other important problems in the physical sciences…