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- All Subjects: quantum physics
- Creators: Dasarathy, Gautam
Description
High-performance III-V semiconductors based on ternary alloys and superlattice systems are fabricated, studied, and compared for infrared optoelectronic applications. InAsBi is a ternary alloy near the GaSb lattice constant that is not as thoroughly investigated as other III-V alloys and that is challenging to produce as Bi has a tendency to surface segregate and form droplets during growth rather than incorporate. A growth window is identified within which high-quality droplet-free bulk InAsBi is produced and Bi mole fractions up to 6.4% are obtained. Photoluminescence with high internal quantum efficiency is observed from InAs/InAsBi quantum wells. The high structural and optical quality of the InAsBi materials examined demonstrates that bulk, quantum well, and superlattice structures utilizing InAsBi are an important design option for efficient infrared coverage.
Another important infrared material system is InAsSb and the strain-balanced InAs/InAsSb superlattice on GaSb. Detailed examination of X-ray diffraction, photoluminescence, and spectroscopic ellipsometry data provides the temperature and composition dependent bandgap of bulk InAsSb. The unintentional incorporation of approximately 1% Sb into the InAs layers of the superlattice is measured and found to significantly impact the analysis of the InAs/InAsSb band alignment. In the analysis of the absorption spectra, the ground state absorption coefficient and transition strength of the superlattice are proportional to the square of the electron-hole wavefunction overlap; wavefunction overlap is therefore a major design parameter in terms of optimizing absorption in these materials. Furthermore in addition to improvements through design optimization, the optical quality of the materials studied is found to be positively enhanced with the use of Bi as a surfactant during molecular beam epitaxy growth.
A software tool is developed that calculates and optimizes the miniband structure of semiconductor superlattices, including bismide-based designs. The software has the capability to limit results to designs that can be produced with high structural and optical quality, and optimized designs in terms of maximizing absorption are identified for several infrared superlattice systems at the GaSb lattice constant. The accuracy of the software predictions are tested with the design and growth of an optimized mid-wave infrared InAs/InAsSb superlattice which exhibits superior optical and absorption properties.
Another important infrared material system is InAsSb and the strain-balanced InAs/InAsSb superlattice on GaSb. Detailed examination of X-ray diffraction, photoluminescence, and spectroscopic ellipsometry data provides the temperature and composition dependent bandgap of bulk InAsSb. The unintentional incorporation of approximately 1% Sb into the InAs layers of the superlattice is measured and found to significantly impact the analysis of the InAs/InAsSb band alignment. In the analysis of the absorption spectra, the ground state absorption coefficient and transition strength of the superlattice are proportional to the square of the electron-hole wavefunction overlap; wavefunction overlap is therefore a major design parameter in terms of optimizing absorption in these materials. Furthermore in addition to improvements through design optimization, the optical quality of the materials studied is found to be positively enhanced with the use of Bi as a surfactant during molecular beam epitaxy growth.
A software tool is developed that calculates and optimizes the miniband structure of semiconductor superlattices, including bismide-based designs. The software has the capability to limit results to designs that can be produced with high structural and optical quality, and optimized designs in terms of maximizing absorption are identified for several infrared superlattice systems at the GaSb lattice constant. The accuracy of the software predictions are tested with the design and growth of an optimized mid-wave infrared InAs/InAsSb superlattice which exhibits superior optical and absorption properties.
ContributorsWebster, Preston Thomas (Author) / Johnson, Shane R (Thesis advisor) / Zhang, Yong-Hang (Committee member) / Menéndez, Jose (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2016
Description
Quantum computing is becoming more accessible through modern noisy intermediate scale quantum (NISQ) devices. These devices require substantial error correction and scaling before they become capable of fulfilling many of the promises that quantum computing algorithms make. This work investigates the current state of NISQ devices by implementing multiple classical computing scenarios with a quantum analog to observe how current quantum technology can be leveraged to achieve different tasks. First, quantum homomorphic encryption (QHE) is applied to the quantum teleportation protocol to show that this form of algorithm security is possible to implement with modern quantum computing simulators. QHE is capable of completely obscuring a teleported state with a liner increase in the number of qubit gates O(n). Additionally, the circuit depth increases minimally by only a constant factor O(c) when using only stabilizer circuits. Quantum machine learning (QML) is another potential application of NISQ technology that can be used to modify classical AI. QML is investigated using quantum hybrid neural networks for the classification of spoken commands on live audio data. Additionally, an edge computing scenario is examined to profile the interactions between a quantum simulator acting as a cloud server and an embedded processor board at the network edge. It is not practical to embed NISQ processors at a network edge, so this paradigm is important to study for practical quantum computing systems. The quantum hybrid neural network (QNN) learned to classify audio with equivalent accuracy (~94%) to a classical recurrent neural network. Introducing quantum simulation slows the systems responsiveness because it takes significantly longer to process quantum simulations than a classical neural network. This work shows that it is viable to implement classical computing techniques with quantum algorithms, but that current NISQ processing is sub-optimal when compared to classical methods.
ContributorsYarter, Maxwell (Author) / Spanias, Andreas (Thesis advisor) / Arenz, Christian (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023
Description
Quantum computing holds the potential to revolutionize various industries by solving problems that classical computers cannot solve efficiently. However, building quantum computers is still in its infancy, and simulators are currently the best available option to explore the potential of quantum computing. Therefore, developing comprehensive benchmarking suites for quantum computing simulators is essential to evaluate their performance and guide the development of future quantum algorithms and hardware. This study presents a systematic evaluation of quantum computing simulators’ performance using a benchmarking suite. The benchmarking suite is designed to meet the industry-standard performance benchmarks established by the Defense Advanced Research Projects Agency (DARPA) and includes standardized test data and comparison metrics that encompass a wide range of applications, deep neural network models, and optimization techniques. The thesis is divided into two parts to cover basic quantum algorithms and variational quantum algorithms for practical machine-learning tasks. In the first part, the run time and memory performance of quantum computing simulators are analyzed using basic quantum algorithms. The performance is evaluated using standardized test data and comparison metrics that cover fundamental quantum algorithms, including Quantum Fourier Transform (QFT), Inverse Quantum Fourier Transform (IQFT), Quantum Adder, and Variational Quantum Eigensolver (VQE). The analysis provides valuable insights into the simulators’ strengths and weaknesses and highlights the need for further development to enhance their performance. In the second part, benchmarks are developed using variational quantum algorithms for practical machine learning tasks such as image classification, natural language processing, and recommendation. The benchmarks address several unique challenges posed by benchmarking quantum machine learning (QML), including the effect of optimizations on time-to-solution, the stochastic nature of training, the inclusion of hybrid quantum-classical layers, and the diversity of software and hardware systems. The findings offer valuable insights into the simulators’ ability to solve practical machine-learning tasks and pinpoint areas for future research and enhancement. In conclusion, this study provides a rigorous evaluation of quantum computing simulators’ performance using a benchmarking suite that meets industry-standard performance benchmarks.
ContributorsSathyakumar, Rajesh (Author) / Spanias, Andreas (Thesis advisor) / Sen, Arunabha (Thesis advisor) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023