arXiv subjects

Varis Carey

Publications and source records attributed to Varis Carey.

3 recordsLinked to original sources

arXiv2025

Helios: A 98-qubit trapped-ion quantum computer

We report on Quantinuum Helios, a 98-qubit trapped-ion quantum processor based on the quantum charge-coupled device (QCCD) architecture. Helios features $^{137}$Ba$^{+}$ hyperfine qubits, all-to-all connectivity enabled by a rotatable ion storage ring connecting two quantum operation regions by a junction, speed improvements from parallelized operations, and a new software stack with real-time compilation of dynamic programs. Averaged over all operational zones in the system, we achieve average infidelities of $2.5(1)\times10^{-5}$ for single-qubit gates, $7.9(2)\times10^{-4}$ for two-qubit gates, and $4.8(6)\times10^{-4}$ for state preparation and measurement, none of which are fundamentally limited and likely able to be improved. These component infidelities are predictive of system-level performance in both random Clifford circuits and random circuit sampling, the latter demonstrating that Helios operates well beyond the reach of classical simulation and establishes a new frontier of fidelity and complexity for quantum computers.

quant-ph↗

arXiv2021

Accelerating Derivative-Free Optimization with Dimension Reduction and Hyperparameter Learning

We consider convex, black-box objective functions with additive or multiplicative noise with a high-dimensional parameter space and a data space of lower dimension, where gradients of the map exist, but may be inaccessible. We investigate Derivative-Free Optimization (DFO) in this setting and propose a novel method, Active STARS (ASTARS), based on STARS (Chen and Wild, 2015) and dimension reduction in parameter space via Active Subspace (AS) methods (Constantine, 2015). STARS hyperparmeters are inversely proportional to the known dimension of parameter space, resulting in heavy smoothing and small step sizes for large dimensions. When possible, ASTARS leverages a lower-dimensional AS, defining a set of directions in parameter space causing the majority of the variance in function values. ASTARS iterates are updated with steps only taken in the AS, reducing the value of the objective function more efficiently than STARS, which updates iterates in the full parameter space. Computational costs may be reduced further by learning ASTARS hyperparameters and the AS, reducing the total evaluations of the objective function and eliminating the requirement that the user specify hyperparameters, which may be unknown in our setting. We call this method Fully Automated ASTARS (FAASTARS). We show that STARS and ASTARS will both converge -- with a certain complexity -- even with inexact, estimated hyperparemters. We also find that FAASTARS converges with the use of estimated AS's and hyperparameters. We explore the effectiveness of ASTARS and FAASTARS in numerical examples which compare ASTARS and FAASTARS to STARS.

math.OC↗

arXiv2017

Moment Preserving Constrained Resampling with Applications to Particle-in-Cell Methods

In simulations of partial differential equations using particle-in-cell (PIC) methods, it is often advantageous to resample the particle distribution function to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. We introduce an algorithm for particle resampling called Moment Preserving Contrained Resampling (MPCR). The general algorithm partitions the system space into smaller subsets and is designed to conserve any number of particle and grid quantities with a high degree of accuracy (i.e. machine accuracy). The resampling scheme can be integrated into any PIC code. The advantages of MPCR, including performance, accuracy, and stability, are presented by examining several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The tests demonstrate that while the computational cost of MPCR is negligible compared to the nascent particle evolution in PIC methods, periodic particle resampling yields a significant improvement in the accuracy and stability of the results.

physics.plasm-ph↗