5 citations · 10 across the 8 of their papers we have counts for
11 papers
A Feasible Direction Method with Active-Set Identification for Derivative-Free Optimization
Jeffrey Larson, Matt Menickelly, Lara Zebiane
We propose a model-based trust region method for derivative-free optimization with simulation-based inequality constraints. We are motivated by settings of simulation optimization…
AI-Accelerated Gyrokinetic Predictions of Turbulent Transport for Stellarator Design Optimization and Experimental Planning
R. Michael Churchill, Matt Landreman, Jong Youl Choi +10
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellara…
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…
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…
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…
A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly +1
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS lever…
State Dependent Optimization with Quantum Circuit Cutting
Xinpeng Li, Ji Liu, Jeffrey M. Larson +4
Quantum circuits can be reduced through optimization to better fit the constraints of quantum hardware. One such method, initial-state dependent optimization (ISDO), reduces gate c…
Efficient Frequency Allocation for Superconducting Quantum Processors Using Improved Optimization Techniques
Zewen Zhang, Pranav Gokhale, Jeffrey M. Larson
Building on previous research on frequency allocation optimization for superconducting circuit quantum processors, this work incorporates several new techniques to improve overall…
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…
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…
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…