Publications (53)
A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms
Jeffrey Larson, Matt Menickelly, Jiahao Shi
A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quant…
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
Stephen Hudson, Jeffrey Larson, John-Luke Navarro +1
Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recogni…
Portable, heterogeneous ensemble workflows at scale using libEnsemble
Stephen Hudson, Jeffrey Larson, John-Luke Navarro +1
libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator--simulator--al…
Origins and optimization of entanglement in plasmonically coupled quantum dots
Matthew Otten, Jeffrey Larson, Misun Min +3
A system of two or more quantum dots interacting with a dissipative plasmonic nanostructure is investigated in detail by using a cavity quantum electrodynamics approach with a mode…
Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions
Mickael Binois, Jeffrey Larson
We develop and analyze a method for stochastic simulation optimization based on Gaussian process models within a trust-region framework. We focus on settings where the variance of…
Structure-Aware Methods for Expensive Derivative-Free Nonsmooth Composite Optimization
Jeffrey Larson, Matt Menickelly
We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some k…
Multistart Algorithm for Identifying All Optima of Nonconvex Stochastic Functions
Prateek Jaiswal, Jeffrey Larson
We propose a multistart algorithm to identify all local minima of a constrained, nonconvex stochastic optimization problem. The algorithm uniformly samples points in the domain and…
Multi-Angle QAOA Does Not Always Need All Its Angles
Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin +3
Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle…
Birkhoff interpolation models for optimization with some available derivatives
Jeffrey Larson, Matt Menickelly, Evan Toler
We consider interpolation-based derivative-free optimization in settings where only some derivatives are available. Such situations arise in scientific computing applications invol…
An Interacting System and Environment with Prior Correlations Plus Local Operations Can Mimic Uncorrelated Evolution
Daniel Dilley, Alvin Gonzales, Jeffrey Larson +1
Initial system-environment correlations can induce reduced dynamics that depart from the standard completely positive (CP) description. We study when such dynamics can be reproduce…
Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks
Amir Davatgari, Taner Cokyasar, Anirudh Subramanyam +2
Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. So…
Platoon formation maximization through centralized routing and departure time coordination
Vadim Sokolov, Jeffrey Larson, Todd Munson +2
Platooning allows vehicles to travel with small intervehicle distance in a coordinated fashion thanks to vehicle-to-vehicle connectivity. When applied at a larger scale, platooning…
Roadblocks and Opportunities in Quantum Algorithms -- Insights from the National Quantum Initiative Joint Algorithms Workshop, May 20--22, 2024
Eliot Kapit, Peter Love, Jeffrey Larson +6
The National Quantum Initiative Joint Algorithms Workshop brought together researchers across academia, national laboratories, and industry to assess the current landscape of quant…
Derivative-Free Optimization of a Rapid-Cycling Synchrotron
Jeffrey S. Eldred, Jeffrey Larson, Misha Padidar +2
We develop and solve a constrained optimization model to identify an integrable optics rapid-cycling synchrotron lattice design that performs well in several capacities. Our model…
An Empirical Quantile Estimation Approach to Nonlinear Optimization Problems with Chance Constraints
Fengqiao Luo, Jeffrey Larson
We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance const…
Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson +2
Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unwei…
Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions
Jeffrey Larson, Matt Menickelly, Baoyu Zhou
We propose a manifold sampling algorithm for minimizing a nonsmooth composition , where we assume is nonsmooth and may be inexpensively computed in closed form and…
A Repeated Route-then-Schedule Approach to Coordinated Vehicle Platooning: Algorithms, Valid Inequalities and Computation
Fengqiao Luo, Jeffrey Larson
Platooning of vehicles is a promising approach for reducing fuel consumption, increasing vehicle safety, and using road space more efficiently. We consider the important but diffic…
Characterizing Error Mitigation by Symmetry Verification in QAOA
Ashish Kakkar, Jeffrey Larson, Alexey Galda +1
Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been propose…
A Method for Convex Black-Box Integer Global Optimization
Jeffrey Larson, Sven Leyffer, Prashant Palkar +1
We study the problem of minimizing a convex function on a nonempty, finite subset of the integer lattice when the function cannot be evaluated at noninteger points. We propose a ne…
Extreme-Scale EV Charging Infrastructure Planning for Last-Mile Delivery Using High-Performance Parallel Computing
Waquar Kaleem, Taner Cokyasar, Jeffrey Larson +3
This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide whe…
Variational quantum state preparation for quantum-enhanced metrology in noisy systems
Juan C. Zuñiga Castro, Jeffrey Larson, Sri Hari Krishna Narayanan +3
We investigate optimized quantum state preparation for quantum metrology applications in noisy environments. Using the QFI-Opt package, we simulate a low-depth variational quantum…
Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors
Brad L. Boyce, Mitchell A. Wood, Krishna Garikipati +16
The paper proposes a probabilistic framework that treats material behavior as an ensemble of competing mechanisms, using fatigue crack propagation as an example to show how mechani…
Comparison of Multiobjective Optimization Methods for the LCLS-II Photoinjector
Nicole Neveu, Tyler H. Chang, Paris Franz +2
Particle accelerators are among some of the largest science experiments in the world and can consist of thousands of components with a wide variety of input ranges. These systems c…
Routing Entanglement in Complex Quantum Networks Using GHZ States
Xin-An Chen, Caitao Zhan, Joaquin Chung +1
Distributing entanglement to distant parties in a network is a central task in quantum information processing and quantum networking. The sensitivity of entangled states to loss ne…
Switching Time Optimization for Binary Quantum Optimal Control
Xinyu Fei, Lucas T. Brady, Jeffrey Larson +2
Quantum optimal control is a technique for controlling the evolution of a quantum system and has been applied to a wide range of problems in quantum physics. We study a binary quan…
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
S. Ashwin Renganathan, Jeffrey Larson, Stefan M. Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracl…
CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
Wei Tang, Teague Tomesh, Martin Suchara +2
Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles t…
Exploiting In-Constraint Energy in Constrained Variational Quantum Optimization
Tianyi Hao, Ruslan Shaydulin, Marco Pistoia +1
A central challenge of applying near-term quantum optimization algorithms to industrially relevant problems is the need to incorporate complex constraints. In general, such constra…
Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network
Anirudh Subramanyam, Taner Cokyasar, Jeffrey Larson +1
Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional in…
Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti +26
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tac…
Certified randomness using a trapped-ion quantum processor
Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula +29
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a…
QContext: Context-Aware Decomposition for Quantum Gates
Ji Liu, Max Bowman, Pranav Gokhale +4
In this paper we propose QContext, a new compiler structure that incorporates context-aware and topology-aware decompositions. Because of circuit equivalence rules and resynthesis,…
Binary Control Pulse Optimization for Quantum Systems
Xinyu Fei, Lucas T. Brady, Jeffrey Larson +2
Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important…
Spin-Boson Mapping of the Quantum Approximate Optimization Algorithm
Sami Boulebnane, Abid Khan, Minzhao Liu +4
The Quantum Approximate Optimization Algorithm (QAOA) achieves monotonically improving performance with circuit depth , yet the study of the high-depth regime has been obstructe…
Hardware-Conscious Optimization of the Quantum Toffoli Gate
Max Aksel Bowman, Pranav Gokhale, Jeffrey Larson +2
While quantum computing holds great potential in combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by…
Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization
S. Ashwin Renganathan, Jeffrey Larson, Stefan Wild
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle. We are interested in the decision that maximizes the oracle at a fini…
Derivative-free optimization methods
Jeffrey Larson, Matt Menickelly, Stefan M. Wild
In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of…
Variational quantum state preparation within an entangle-rotate circuit framework for quantum-enhanced metrology in noisy systems
Juan C. Zuñiga Castro, Jeffrey Larson, Matt Menickelly +4
We investigate the generation of quantum states for precision metrology in noisy two-level systems. These states are obtained by optimizing a variational quantum circuit to maximiz…
Exploring Non-Multiplicativity in the Geometric Measure of Entanglement
Daniel Dilley, Jerry Chang, Jeffrey Larson +1
The geometric measure of entanglement (GME) quantifies how close a multi-partite quantum state is to the set of separable states under the Hilbert-Schmidt inner product. The GME ca…
Large-Scale Dynamic Ridesharing with Iterative Assignment
Akhil Vakayil, Felipe de Souza, Taner Cokyasar +2
Transportation network companies (TNCs) have become a highly utilized transportation mode over the past years. At their emergence, TNCs were serving ride requests one by one. Howev…
Multistart Methods for Quantum Approximate Optimization
Ruslan Shaydulin, Ilya Safro, Jeffrey Larson
Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quant…
Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints
Christian Kirches, Jeffrey Larson, Sven Leyffer +1
We propose an algorithm for solving bound-constrained mathematical programs with complementarity constraints on the variables. Each iteration of the algorithm involves solving a li…
Integrating ytopt and libEnsemble to Autotune OpenMC
Xingfu Wu, John R. Tramm, Jeffrey Larson +7
ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that…
Challenges with Differentiable Quantum Dynamics
Sri Hari Krishna Narayanan, Michael Perlin, Robert Lewis-Swan +4
Differentiable quantum dynamics require automatic differentiation of a complex-valued initial value problem, which numerically integrates a system of ordinary differential equation…
End-to-End Protocol for High-Quality QAOA Parameters with Few Shots
Tianyi Hao, Zichang He, Ruslan Shaydulin +2
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classic…
A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery
Taner Cokyasar, Anirudh Subramanyam, Jeffrey Larson +2
Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing…
Modeling Approaches for Addressing Simple Unrelaxable Constraints with Unconstrained Optimization Methods
Misha Padidar, Jeffrey Larson, Stefan M. Wild
We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated.…
Comparing optimization strategies for force field parameterization
Fatih G. Sen, Badri Narayanan, Jeffrey Larson +7
Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability…
Binary Quantum Control Optimization with Uncertain Hamiltonians
Xinyu Fei, Lucas T. Brady, Jeffrey Larson +2
Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneou…
LEAP: Scaling Numerical Optimization Based Synthesis Using an Incremental Approach
Ethan Smith, Marc G. Davis, Jeffrey Larson +3
While showing great promise, circuit synthesis techniques that combine numerical optimization with search over circuit structures face scalability challenges due to a large number…
InterQnet: A Heterogeneous Full-Stack Approach to Co-designing Scalable Quantum Networks
Joaquin Chung, Daniel Dilley, Ely Eastman +21
Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology ma…
Classical Pre-optimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications
J. Wayne Mullinax, Panagiotis G. Anastasiou, Jeffrey Larson +2
The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic…