Classical Optimizers for Noisy Intermediate-Scale Quantum Devices

We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They are also used for calibration tasks, hyperparameter tuning, in machine learning, etc. We analyze the efficiency and effectiveness of different optimizers in a VQE case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, and will likely remain necessary even for future fault tolerant circuits

Classical Optimizers for Noisy Intermediate-Scale Quantum Devices

We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They are also used for calibration tasks, hyperparameter tuning, in machine learning, etc. We analyze the efficiency and effectiveness of different optimizers in a VQE case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, and will likely remain necessary even for future fault tolerant circuits.Comment: 11 pages, 17 figure

The Variability and Optimization of Mental Toughness

This thesis examined within-person variability and potential optimizers of mental toughness through a literature review and four central studies, three of which have been published and a fourth currently under review. Study I focused on within-person mental toughness and potential optimizers in a single elite Masters athlete across a series of endurance events (3,000 mile Race Across America, Hawaii Ironman Triathlon World Championship qualifier and a sub-3 hour marathon) over a five month period and beginning six weeks after a bike wreck resulted in eight fractures and an increased emphasis on the mental aspects of the events. Notable variability and potential optimizers were both identified via an autoethnographic approach. The second study expanded upon the first by investigating the presence of within-person variability and potential optimizers in a group of 13 elite Masters athletes. In addition to the larger group of participants, Study II also identified within-person mental toughness variability, utilizing the Mental Toughness Index (Gucciardi, 2015) to specifically track the potential variability over a 30-day period. The exploratory case study design also included collection of qualitative data regarding the potential optimizers and lead to the development of three primary higher order themes of mental toughness optimizers: Thrive, Prepare and Activate. Study III examined the influence of sleep on mental toughness, a potential optimizer identified previously within the Thrive and Prepare higher order themes. Within-person variability in mental toughness was again demonstrated and while sleep was not shown to be related to mental toughness in all participants as hypothesized, it was an influencer of mental toughness in the majority of participants. In addition, Study III provided insights into additional buoys of mental toughness utilized by participants when sleep was limited. Study IV then investigated whether self-talk (identified previously as a potential optimizer under the Prepare and Activate themes) influenced mental toughness and performance. The influence of self-talk on mental toughness and performance was demonstrated. The thesis concludes with a discussion about the findings, their implications for additional settings and applications, and future research opportunities

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

Summary: 1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data