Session Q01: Applied and Computational Physics

Potential of Nonlinear Phi-Bit Modes in Elastic Systems to Revolutionize Quantum-Analogue Computing

Phi-bits, akin to the quantum concept of qubits but in a classical mechanical framework, play a critical role in the development of quantum-analogue computing, and hence, understanding the nonlinear dynamics governing their control and interactions is crucial. These phi-bits, represented by acoustic waves within nonlinearly coupled arrays of waveguides, can exist in coherent superpositions of states. Adjusting external drivers' frequency, amplitude, and phase allows precise control over the phi-bit states. We have devised a discrete element model to analyze and predict the nonlinear response of phi-bits to external drivers, considering various types, strengths, and orders of nonlinearity stemming from intrinsic medium coupling among waveguides and external factors like signal generators, transducers, and ultrasonic couplant assemblies. Notable findings include the influence of nonlinearity type, strength, and order on the complex amplitudes within the coherent superposition of phi-bit states. This investigation serves as a groundwork for controlling design parameters in phi-bit creation, facilitating the preparation and manipulation of state superpositions crucial for developing phi-bit-based quantum analogue information processing platforms.   

Quantum Concepts in Classical Realms: Berry Phases and Elastic Bits in Granular Systems

The Berry phase, a concept of significant interest in quantum and classical mechanics, illuminates the dynamics of physical systems. Our current study explores this phenomenon within a classical granular network, employing an "elastic bit" that serves as a classical counterpart to the quantum bit. This approach establishes a connection between classical and quantum mechanics. By adjusting external forces, we generate an elastic bit within the granular network and map its behavior onto a Bloch sphere, akin to operating quantum logic gates. Varied manipulations of these external drivers yield a spectrum of Berry phases, from trivial (0) to nontrivial (π), unveiling the topological nature of the elastic bit. Crucially, this topological behavior is governed by external manipulations rather than the material or geometric properties of the medium. The nontrivial Berry phases, in particular, highlight energy localization within the granule vibrations, marking a significant insight into system dynamics. This research bridges the gap between the quantum and classical realms and paves the way for designing novel materials with unique properties.   

Stability Implications of Process Control Strategies Using Quantum Computations for Next-Generation Manufacturing

To enhance process efficiency, evolving industrial setups necessitate increased data collection and processing. This increase in processing demand, as observed in multiple other fields, including process systems engineering, has piqued interest in quantum computations. From a process control perspective, however, the utility of quantum computing remains unclear. We hypothesize that, by implementing existing controllers on quantum computers, we will gain insights into the usefulness of quantum computation for process control.

With this motivation, we investigate the concept of quantum computing from a process control perspective by controlling a simple linear process using a proportional controller. We send process measurements to a quantum Fourier-based addition algorithm to determine an appropriate control input using IBM's quantum experience SDK, Qiskit. We will then compare the algorithm to an implementation of the same control strategy in the presence of quantum noise. Multiple algorithms will subsequently be used to determine the impact of noise on different algorithm structures, along with a variety of factors that influence the impact of quantum noise on process stability guaranteed by the proportional control strategy.

Implementing Automation Algorithms on Quantum Computers

The need for enhanced autonomy systems for manufacturing raises the question of whether quantum computers could play any role in a future generation of decision-making systems.  At this time, it is not clear what quantum algorithms, if any, would be useful for autonomy; however, a key aspect of utility of algorithms for autonomy is that they be able to maintain safe operation.  In this talk, we will describe our initial work that seeks to begin to understand the safety implications of implementing control laws using quantum computation [1,2].  This will include discussing our work on the implementation of a single-input/single-output control law with a quantum simulator, and also searching a lookup table of control actions with a quantum algorithm.

[1] Rangan, K. K., Oyama, H., Assoumani, I. A., Durand, H., & Simon Ng, K. Y. (2023). Cyberphysical Systems and Energy: A Discussion with Reference to an Enhanced Geothermal Process.  Editor: Li, Mingheng.  AIP Publishing.

[2] Nieman, K., Kasturi Rangan, K., & Durand, H. (2022). Control implemented on quantum computers: Effects of noise, nondeterminism, and entanglement. Industrial & Engineering Chemistry Research, 61(28), 10133-10155.   

Quantum and Conventional Machine Learning Analysis of Synthesis-Structure Relationships in Transition Metal Dichalcogenide Thin Films

Vibrational modes in Raman spectra of transition metal dichalcogenides (TMDs) provide critical sample information on defects, sample thickness, and monolayer coverage. Identifying synthesis parameters that result in optimal values for these characteristics is nontrivial; however, supervised learning techniques provide a possible path towards recognizing patterns between growth conditions and characteristic features of Raman spectra acquired of the resulting samples. Leveraging data from over 300 growth trials, we use quantum as well as classical supervised learning algorithms to study the relationships between gas chalcogen precursor MOCVD synthesis parameters of MoS₂ thin films and features in Raman spectra characteristic of the thin films. We identify MOCVD growth parameters that minimize the A_1g and E_2g mode peak distance, corresponding to improved monolayer coverage. Models can be trained on data characterizing both the center and edge of samples to identify a growth recipe that minimizes the difference between the two spectra, improving the uniformity of the sample. The methodology of this machine learning investigation of synthesis–structure relationships can be applied to additional features of interest within Raman spectra, as well as to other TMDs, such as WS₂ and WSe₂.   

Computational Hemodynamic Investigations of Intracranial Aneurysm Pathophysiology in Identical Twins

Hemodynamic mechanisms of pathophysiology (i.e., generation and growth) of intracranial aneurysms (IA) in identical twins are still underdeveloped. To partially fill the knowledge gap and provide new insights for the aneurysm research community, we used an in-vitro validated computational fluid dynamics (CFD) method to distinguish hemodynamics (i.e., seven critical hemodynamic parameters) in three anatomical and five ablated neurovascular models from a rare pair of identical twins (i.e., Twin A and Twin B). CFD modeling results presented significant hemodynamic differences in the twins. However, they share the same genes, indicating that possible genetic mutation and environmental factors could influence neurovascular morphologies greatly, then further lead to various hemodynamic characteristics. After ablating IA sacs using a benchmarked pathway virtually, the regions of aneurysmal sac/bleb generation in the anterior cerebral artery (ACA) bifurcation register a locally high instantaneous wall shear stress of 52.9 and 70.1 Pa near the systolic peak in both Twin A and Twin B, respectively. A similar phenomenon can be discovered in performances of the other critical hemodynamic indicator, i.e., instantaneous wall shear stress gradient, with 571.1 Pa/mm for Twin A and 301.3 Pa/mm for Twin B due to aggressive blood impinging effects, leading to IA generation, respectively. Additionally, the fenestrated complex approaching the first-order ACA bifurcations in twin A could be a potential factor in assisting IA growth and rupture, via. causing abnormal instantaneous wall shear stress of 116.3 Pa, the associated gradient of 832.5 Pa/mm, and oscillatory shear index of 0.49. The bleb sac in twin B has high growth and rupture risks as the IA sac suffers relatively low instantaneous wall shear stress and high oscillatory shear index. Additionally, IA generation and growth can change blood flow rates in each connected artery in the cerebral artery system, then influence associated tissues and organs.