Mines Repository (Colorado School of Mines)
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Quantum algorithms for exact and approximate optimization
No full textIncludes bibliographical references.2024 Spring.Following early breakthroughs for quantum algorithms in database search and integer factoring, over recent years, quantum computing hardware technologies and quantum algorithms have undergone rapid developments. Yet, the search for “killer” applications of quantum computing has remained elusive amidst the noisy intermediate scale quantum computing (NISQ) era, where noise from the environment provides a challenge for quantum algorithms requiring a fault-tolerant machine. Among prevalent applications such as quantum simulation and quantum chemistry, hard optimization tasks provide a promising class of problems where quantum algorithms hope to demonstrate advantage beyond quadratic speed-ups. This work investigates the application of heuristic quantum algorithms to combinatorial optimization, where we consider the class of binary constraint satisfaction problems. We introduce two new quantum algorithms for exact and approximate optimization, and demonstrate our methods with extensive quantum simulations that exhaust the capabilities of current supercomputers for classical simulation of quantum dynamics. In particular, we investigate a well-known problem known as MAX-3-XORSAT, in the complexity class, NP-hard. First, we introduce a new method called Spectrally Folded Quantum
Optimization (SFQO) which transforms the energy landscape of the problem, allowing approximate solutions to be readily obtained with guaranteed approximation ratios. Secondly, we introduce a new non-classical steering mechanism called Iterative Symphonic Tunneling for Satisfiability problems
(IST-SAT) which uses macroscopic quantum tunneling effects to guide the sufficiently good approximations towards the true global optima. The first work we present in this thesis, Spectrally Folded Quantum Optimization, investigates the ability of quantum algorithms to find approximate solutions to the MAX-3-XORSAT hypergraph problem class. We identify several distinct physical mechanisms associated to these problems, which make the task of finding exact solutions hard for all previously known classical and quantum algorithms. However, we find the same mechanisms that prevent quantum algorithms to find exact solutions, do not necessarily hold for approximate optimization. Spectrally folded quantum optimization implements a classical deformation of the constraint satisfaction problem energy landscape, which allows quantum algorithms to find constant fractions of the optimal solution with increasing problem size. We provide theoretical performance predictions for the algorithm, and benchmark our methods with extensive quantum simulations. Our results demonstrate that all of our numerical simulations agree with our predictions at the system sizes we can classically simulate using the best super-computing resources for classical simulation of quantum dynamics. This work suggests that quantum algorithms may be more powerful than previously thought for the task of approximate optimization. In the second work in this thesis, Iterative Symphonic Tunneling for Satisfiability problems, we present a non-classical steering mechanism for quantum optimization algorithms based on the use of high frequency oscillating “AC” drives. To demonstrate this mechanism we introduce an iterative quantum algorithm, IST-SAT, which does not require computing gradients or extensive fine-tuning. Using an initial classical or quantum algorithm to approximate the MAX-3-XORSAT problem, IST-SAT sets parameters in single-qubit oscillating drives according to the bits in the initial solution, which induces further macroscopic tunneling effects towards the true ground state(s) of the problem. IST-SAT converges to the ground state in an iterative manner, measured by the number of spin flips away from the ground state(s), also known as Hamming distance. We identify what it means to have a sufficient initial approximation for the IST-SAT, which defines a radius of convergence for the algorithm. The numerical results we obtain demonstrate that IST-SAT monotonically improves in performance, when provided initial states that are closer and closer to the ground state of the problem. When provided with an initial approximation at or above the radius of convergence, our results suggest that IST-SAT converges in polynomial time. Together, the results presented in this work obtain exponential speed-ups for obtaining approximate solutions, and polynomial speed-ups for exact problem solving, over the best known quantum and classical algorithms. While NP-hard optimization problems remain hard to solve exactly, by combining the methods in this work, we show that our algorithm can converge to the true ground state in polynomial time when provided a sufficiently good initial state. The novel mechanisms in this work thus pave new pathways for achieving quantum advantage on well-known hard problems, such as MAX-3-XORSAT. We expect the methods in this work to be amenable for experimental demonstrations on current or near-term quantum hardware, thus providing an exciting opportunity to demonstrate utility of quantum computers in the NISQ era
Torbernite
No full textPhotographed by Ron Wolf.Glassy green tabular crystals of torbernite on grey-blue-brown matrix (A0179-0078) and a close view of another part of the surface of glassy green sheet-like tabular crystals of torbernite
Witherite
No full textPhotographed by Ron Wolf.Pale yellow resinous witherite on matrix of small ghassy white and pink crystals
Defects, scattering, and mobility in complex thermoelectric materials
No full textIncludes bibliographical references.2024 Fall.Growing numbers of newly discovered materials hold the possibility of high thermoelectric performance.
However, the requirement of the material to conduct electricity while maintaining a high Seebeck coefficient can be limited by native defects or poor electronic mobility which are difficult to predict \textit{a priori}.
The research goal of this dissertation is to investigate how the thermoelectric community can understand defects and scattering in complex thermoelectric materials.
Using recently discovered compound HgGeTe as a case study, we demonstrate manipulation of native and extrinsic defects to optimize thermoelectric performance. Finally, we explore the impact of carrier scattering on mobility in classic material SnTe using a custom-designed Nernst effect instrument we built.
To understand the impact of defects in complex thermoelectric materials, we adopt a joint computational-experimental approach focusing on HgGeTe as a case study material. We perform phase boundary mapping, defect calculations, and synthesis of native \& extrinsic doped samples to identify the highest performance in HgGeTe. We succeed in manipulating the carrier concentration of HgGeTe by half an order of magnitude \textit{via} manipulating concentrations of native defects. We follow our native doping study with an extrinsic doping study which examines the impact of 15 different dopants on the thermoelectric performance in HgGeTe.
Ultimately, we achieve the highest figure of merit to date in HgGeTe by doping with silver.
Our curiosity about the dominant source of scattering in HgGeTe leads us to construct an instrument to measure the Nernst effect, which can shed light on the dominant scattering mechanism in a material.
The Nernst coefficient, when combined with three other measured experimental parameters (Hall coefficient, electrical resistivity, and Seebeck coefficient) can be used to solve for material parameters that elucidate scattering mechanisms.
Nernst measurements are scant across the thermoelectric literature in comparison with Hall and Seebeck instruments. We describe our design and build a room temperature apparatus that automatically measures and calculates the Nernst coefficient in a material.
Finally, we apply our Nernst instrument to
explore carrier scattering in SnTe under various doping regimes. Undoped SnTe possesses high mobility, and by synthesizing samples doped with indium, iodine, or natively doped, we explore how mobility evolves in response to two orders of magnitude change in carrier concentration.
We demonstrate that the traditional single parabolic band model is insufficient to describe the scattering in SnTe, but applying the more appropriate two band Kane/parabolic model is a complex optimization problem. We provide an interpretation of the raw Nernst data, along with standard thermoelectric measurements to offer insight into the chemistry dependence of scattering in SnTe
Fluorite and arsenopyrite
No full textPhotographed by Ron Wolf.Glassy purple fluorite crystals on metallic bronze-colored arsenopyrite, Yaoganxian, Hunan Province, China
Scapolite var. wernerite (fluorescent)
No full textPhotographed by Ron Wolf.Soapy pitted grey-white wernerite (variety of scapolite), showing fluorescent properties of green-yellow color under ultraviolet light (A0179-0305); soapy pitted grey-white wernerite (variety of scapolite)(A0179-0306); Two views of pitted grey-white wernerite (variety of scapolite): top view shows specimen in plain light; bottom view shows fluorescent properties of green-yellow color under ultraviolet light (A0179-0307)
Social robot interaction design to mitigate risk in sensitive and adverse contexts
No full textIncludes bibliographical references.2024 Spring.To be successful and acceptable, social robots must demonstrate social competence, navigate sensitive situations, and react to adverse events. Designing robot behaviors for these interactions is challenging because poor robot responses risk harming humans’ dignity and well-being. This dissertation explores how social robots can be designed to effectively and appropriately respond to adverse or sensitive social interactions in positive ways that minimize risk to users’ well-being. Chapter 2 begins by exploring an instance in which social robots are already used in the wild for potentially sensitive interactions— the use of teleoperated socially assistive robots in education, therapy, and telehealth for children. This work demonstrates the advantages of human oversight in this domain by identifying users’ existing strategies to mitigate the social and emotional risks of child-robot interaction. It then presents design recommendations summarizing how roboticists can develop tools that support users’ ability to prepare for and adapt to unforeseen situations.
Chapters 3 and 4 evaluate interaction design for autonomous robots in adverse interactions involving norm violations, such as unethical commands or hate speech. Chapter 3 explores how people appraise these interactions and investigates why they may prefer a robot to intervene or abdicate from responding to adverse events. Chapter 4 furthers this work through an empirical evaluation of robots’ use of human-like linguistic politeness cues to address unethical commands. It presents a framework delineating how robots could use human-like cues to effectively and appropriately address adverse interactions while avoiding negative perceptions. This work also reemphasizes broader concerns about the extent to which robots should be able to perceive and react to such scenarios. Overall, this dissertation makes empirical and design contributions to the field of HRI that inform how social robots can preserve humans’ dignity and well-being in adverse interactions. It argues that these contexts require roboticists to recognize factors outside of individual human-robot interactions— including the experiences of secondary stakeholders and bystanders, existing sociocultural norms of collaboration and conflict, and the potential for ill use of robots’ capabilities
Cavansite
No full textPhotographed by Ron Wolf.Radiating clusters of blue cavansite on smaller white mineral clusters, Poonah district, Maharashtra, India
Unmanned aerial vehicle and laboratory-based hyperspectral imaging to unravel modern and ancient hydrothermal systems
No full textIncludes bibliographical references.2024 Fall.Understanding hydrothermal activity is crucial for mineral exploration and geothermal energy development, as it significantly influences the formation of mineral deposits and geothermal reservoirs. Modern geothermal systems serve as present-day analogs of epithermal ore-forming systems, providing insights into hydrothermal mineralization processes and fluid circulation patterns in the upper crust. This study utilizes hyperspectral imaging to detect and map alteration minerals and their spatial distribution in two distinct geological settings: the Castle Mountain low-sulfidation epithermal gold deposit and the Coso Geothermal Field. At the Castle Mountain deposit, a UAV-based hyperspectral survey was conducted to map alteration minerals. Ground truthing was performed using laboratory-based hyperspectral analysis on ground control samples from the surveyed area. Multiple mineral identification algorithms, including USGS PRISM MICA, Minimum Wavelength Mapping (MWL), and Spectral Angle Mapper (SAM) were utilized and compared to map surface alteration at the Castle Mountain deposit. Our findings indicate that the UAV-based hyperspectral scanning effectively identifies alteration minerals, reflecting the classic zonation in epithermal systems. The USGS PRISM MICA algorithm outperformed others by providing the most accurate results with the least complexity. In the Coso Geothermal Field, five reverse circulation chip drill holes were analyzed using laboratory-based short-wave infrared (SWIR) scanning and SEM-based automated mineralogy. The Illite Spectral Maturity (ISM) index and the position of the 2200 nm absorption feature were utilized to differentiate between illite, smectite, and their mixtures, enabling the construction of thermal gradients for the studied drill holes. Our results confirm the dominance of diorites and granodiorites with occasional granite intrusions. The unsystematic distribution of alteration minerals suggests multiple hydrothermal events. The ISM results correlated well with previous XRD studies. Smectite and illite-smectite mixtures were found at greater depths than their thermal stability, suggesting heating events within the geothermal system. This study validates the effectiveness of hyperspectral imaging for mapping alteration. The proposed methodologies offer a faster, non-invasive approach for mineral identification while enhancing data processing efficiency and allowing detailed mineralogical and lithological analyses. Which in turn improved our understanding of this complex geothermal system