Theses on the topic of quantum computing
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Currently, all thesis topics are already assigned. New theses will be announced by mid-2024.
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Stochastic gradient line bayesian optimization (sglbo) for quantum computing (b.sc.).
Current algorithms for quantum computers are based on so-called variational approaches. In this process, quantum gates - the programmable building blocks of quantum computers - are parameterized with variables and numerically optimized using conventional computers. To do this, gradient-based methods are often applied. However, noise poses a particular challenge when it comes to optimization due to the probabilistic nature of quantum physics, as well as the significant measurement noise of today’s error-prone hardware. Optimization methods for quantum algorithms must therefore be able to deal with noise.
This is where machine learning optimization methods come into play. A highly promising optimization approach is Stochastic Gradient Line Bayesian Optimization (SGLBO). This uses a machine learning method (Bayesian Optimization) to control the optimization step by step. A recent publication demonstrated how this can give quantum algorithms an advantage over other optimization methods. This bachelor thesis will examine to what extent quantum neural networks can be optimized using the SGLBO method. Quantum neural networks function like artificial neural networks, but they are run on a quantum computer. In the first part of the thesis, the SGLBO method will be implemented and tested in Python. This will be followed by a comparison with other previously-implemented optimization methods under different noise influences. Finally, the thesis will assess how well the optimization works on IBM's real quantum computing hardware. The thesis provides an exciting opportunity to address current optimization challenges using quantum computing and to make an important contribution in this field. Prior knowledge of numerical optimization is a great advantage, as well as a general interest in the topics of quantum computing and machine learning.
Quantum Kernel Methods for Solving Differential Equations (B.Sc, M.Sc)
In the DEGRAD-EL3-Q project, we are investigating how quantum computing methods can be used to analyze the lifetime of electrolyzers. The project is part of the lead project H2Giga and aims at advancing the industrial manufacturing process of electrolyzers. The mathematical description of how electrolyzers behave in operation can be modeled by differential equations. In this project, we want to explore the extent to which quantum kernel methods can be used to solve differential equations. In addition, a systematic comparison will be made with the quantum neural networks also studied in the project. This is an exciting and forward-looking topic in the superposition state of quantum computing and hydrogen research.
Evaluating the Use of Quantum Graph Neural Networks to Predict Molecular Properties (M.Sc.)
Scalable and cost-effective solutions for storing renewable energy are essential if we are to meet the world's increasing energy demand and simultaneously mitigate climate change. The conversion of electricity to hydrogen, as well as the reverse combustion process, can play an important role. To make catalysis processes in hydrogen production efficient, new materials are constantly being studied. Machine learning methods are already being used to simulate and calculate catalysis properties. Graph-based neural networks (GNN) are proving to be particularly promising in this respect. Since the prediction of potential surfaces and other relevant properties takes place at molecular and atomic level, the use of quantum computers is also being considered. First approaches to implement GNNs on quantum computers have already been published. The objective of the master thesis is to determine the suitability of quantum GNNs for predicting molecular properties. To this end, depending on prior knowledge, an understanding of GNNs, as well as some basic knowledge of quantum computing, must first be acquired. In-depth knowledge of electrocatalysis is not necessarily required. Towards the end of the master thesis, the developed approaches can be tested and evaluated on a real quantum computer.
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Photonic quantum computers and communication systems
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Faraday Discussions
Mixed quantum/classical theory (mqct) approach to the dynamics of molecule–molecule collisions in complex systems †.
* Corresponding authors
a Chemistry Department, Wehr Chemistry Building, Marquette University, Milwaukee, Wisconsin 53201-1881, USA E-mail: [email protected]
b Observatoire de Paris, PSL University, Sorbonne Universite, CNRS, SYRTE, Paris, France
We developed a general theoretical approach and a user-ready computer code that permit study of the dynamics of collisional energy transfer and ro-vibrational energy exchange in complex molecule–molecule collisions. The method is a mixture of classical and quantum mechanics. The internal ro-vibrational motion of collision partners is treated quantum mechanically using a time-dependent Schrödinger equation that captures many quantum phenomena including state quantization and zero-point energy, propensity and selection rules for state-to-state transitions, quantum symmetry and interference phenomena. A significant numerical speed up is obtained by describing the translational motion of collision partners classically, using the Ehrenfest mean-field trajectory approach. Within this framework a family of approximate methods for collision dynamics is developed. Several benchmark studies for diatomic and triatomic molecules, such as H 2 O and ND 3 collided with He, H 2 and D 2 , show that the results of MQCT are in good agreement with full-quantum calculations in a broad range of energies, especially at high collision energies where they become nearly identical to the full quantum results. Numerical efficiency of the method and massive parallelism of the MQCT code permit us to embrace some of the most complicated collisional systems ever studied, such as C 6 H 6 + He, CH 3 COOH + He and H 2 O + H 2 O. Application of MQCT to the collisions of chiral molecules such as CH 3 CHCH 2 O + He, and to molecule–surface collisions is also possible and will be pursued in the future.
- This article is part of the themed collection: New directions in molecular scattering
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Mixed quantum/classical theory (MQCT) approach to the dynamics of molecule–molecule collisions in complex systems
C. Joy, B. Mandal, D. Bostan, M. Dubernet and D. Babikov, Faraday Discuss. , 2024, Advance Article , DOI: 10.1039/D3FD00166K
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Mossavar-rahmani center announces 2024 dunlop undergraduate thesis prize winner.
Aden Barton.
Courtesy Aden Barton
The Mossavar-Rahmani Center for Business and Government (M-RCBG) at Harvard’s Kennedy School of Government announced Aden Barton as the 2024 winner of the John T. Dunlop Undergraduate Thesis Prize in Business and Government.
Barton won for his thesis, “The Causal Effect of Welfare Retrenchment: Evidence from Medicaid and SNAP.” He is graduating from Harvard College this week with an A.B. in economics.
The John T. Dunlop Thesis Prize in Business and Government is awarded to graduating seniors who write the best thesis on a challenging public policy issue at the interface of business and government. The prize carries a $2,000 award.
This year’s winning thesis by Barton examines the ongoing Medicaid Unwinding, in which millions have been removed from public insurance based on a state’s caseload prioritization. He finds that disenrollment increases the likelihood of being on private insurance and of being uninsured, and reduces the likelihood of enrollees and disenrollees working in the last week by about 5 percentage points, as individuals likely reduced their labor supply to maintain eligibility. His findings also indicate an increased household financial strain, most conclusively by greatly raising the likelihood individuals delay medical treatment.
In explaining why the center chose to award the John Dunlop Prize to Barton, John A. Haigh, co-director of M-RCBG, said that “Aden’s thesis was impressive in its conception and execution. It represents the type of excellent analysis and policy recommendations at the intersection of business and government that we value so highly here at the center.”
John T. Dunlop, the Lamont University Professor Emeritus, was a widely respected labor economist who served as dean of the Faculty of Arts and Sciences from 1969 to 1973. An adviser to many U.S. presidents, beginning with Franklin D. Roosevelt, Dunlop was secretary of labor under Gerald Ford, serving from March 1975 to January 1976. In addition to serving as secretary of labor, Dunlop held many other government posts, including: director of the Cost of Living Council, (1973-74), chairman of the Construction Industry Stabilization Committee (1993-95), chair of the Massachusetts Joint Labor-Management Committee for Municipal Police and Firefighters (1977-2003) and Chair of the Commission on Migratory Farm Labor (1984-2003). Dunlop served as the second director of the Center for Business and Government from 1987 to1991. The Center, renamed in 2005 as the Mossavar-Rahmani Center for Business and Government, focuses on policy issues at the intersection of business and government. Dunlop died in 2003.
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quantum chemistry. The goal of this thesis is to explore the fundamental knowledge base of quantum computation, to demonstrate how to extract the necessary design criteria for a ... Furthermore quantum computing joins two of the most abstruse and surprisingly nonintuitive subjects known to humans: computation and quantum mechanics. Both have ...
Submitted to the Department of Electrical Engineering and Computer Science on August 27, 2021, in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science Abstract In this thesis we present a compiler for Cavy, an imperative quantum programming language.
Quantum computing is widely thought to provide exponential speedups over classical algorithms for a variety of computational tasks. In classical computing, methods in artificial intelligence such as neural networks and adversarial learning have enabled drastic improvements in state-of-the-art performance for a variety of tasks. We consider the intersection of quantum computing with machine ...
This thesis is about quantum algorithms, complexity, and models of quantum computation. In order to discuss these topics it is necessary to use notations and concepts from classical computer science, which I define in this section. The "big-O" family of notations greatly aids in analyzing both classical and quantum
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Quantum computers can calculate and test extensive combinations of hypotheses simultaneously instead of sequentially (S.‐S. Li et al., 2001). Furthermore, some quantum algorithms can be designed in a way that they can solve problems in much fewer steps than their classical counterparts (their complex‐ ity is lower).
A quantum computer can perform exponentially faster than its classical counterpart. It works on the principle of superposition. But due to the decoherence e ect, the superpo-sition of a quantum state gets destroyed by the interaction with the environment. It is a real challenge to completely isolate a quantum system to make it free of decoherence.
I am grateful to Professor Maria Spriopulu as my thesis advisor, academic advisor, and mentor for introducing me to quantum computing, high energy physics, and the excitement of research ever since my freshman year. My collaborators, especially Professor Daniel Jafferis, Professor Seth Lloyd, Dr.
quantum sensing, quantum communications, and quantum computing, promise to have transformative impact on sci-ence, engineering and technology as we know it.1 This arti-cle presents a pedagogical introduction to quantum computing and reviews recent research to develop and apply quantum al-gorithms and utilize quantum computing hardware platforms
of humanoid robots, this Master Thesis researches the application of quantum robotics through using simulated and real quantum computing systems. According to Pitchbook, in the U.S. alone the amount of investment into quantum computing start-ups has increased from about $4 million to about $300 million in the last five years.
algebra, the mathematical side of classical computing. This relation is rooted in the fact that the basic playground of q-processing is the complex space H(n) generated by the set Bnof binary vectors of length n, which is the basic arena of classical computation. Later, when the qof q-processing is interpreted as genuine quantum feature, the
Types for Quantum Computing Ross Duncan Merton College, Oxford Oxford University Computing Laboratory Submitted for the degree of Doctor of Philosophy Michaelmas Term 2006. Abstract This thesis is a study of the construction and representation of typed models of quantum mechanics for use in quantum computation. We introduce logical
This thesis work takes signi cant steps towards building a practical quantum computer using nuclear spins and liquid state nuclear magnetic resonance (NMR) techniques. I present experimental results for proof of principle of quantum computing in a series of small im-plementations of quantum algorithms, culminating in the implementation of an ...
The quantum illumination protocol, on the other hand, has advantage over classical illumination even in presence of decoherence. This thesis provides the optimum receiver design for quantum illumination, and extends quantum illumination target detection to the realistic scenario with target fading and the Neyman-Pearson decision criterion.
Towards provisioning this massive connectivity and efi-ciently processing the voluminous data available at the user and network sides, quantum‐powered computing methods have a strong potential in realising the ambitions of a service‐driven, fully intelligent 6G communication network.
View PDF Abstract: In this thesis, we investigate whether quantum algorithms can be used in the field of machine learning for both long and near term quantum computers. We will first recall the fundamentals of machine learning and quantum computing and then describe more precisely how to link them through linear algebra: we introduce quantum algorithms to efficiently solve tasks such as matrix ...
Research Collection
Abstract. Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement. A quantum computer is a device that performs quantum computing. Such a computer ...
Finally, the thesis will assess how well the optimization works on IBM's real quantum computing hardware. The thesis provides an exciting opportunity to address current optimization challenges using quantum computing and to make an important contribution in this field. Prior knowledge of numerical optimization is a great advantage, as well as a ...
1. INTRODUCTION. As of 2016, actual quantum computers are yet to be. developed, but using sma ll number of bits several. experiments a re carri ed out. Research in the field of. Quantum Computing ...
Abstract. Quantum information processors have been proposed to solve classically intractable or unsolvable problems in computing, sensing, and secure communication. There has been growing interest in photonic implementations of quantum processors as they offer relatively long coherence lengths, precise state manipulation, and efficient ...
3.3. Quantum machine learning. The vast amounts of data anticipated to be processed in 5G and 6G use cases will require computational power and computation time capabilities that are challenging to achieve in current systems. But traditional machine learning techniques take a long time as data volumes expand.
These lecture notes were formed in small chunks during my \Quantum computing" course at the University of Amsterdam, Feb-May 2011, and compiled into one text thereafter. Each chapter ... comes from the rst chapter of my PhD thesis [248], with a number of additions: the lower bound for Simon, the Fourier transform, the geometric explanation of ...
We developed a general theoretical approach and a user-ready computer code that permit study of the dynamics of collisional energy transfer and ro-vibrational energy exchange in complex molecule-molecule collisions. The method is a mixture of classical and quantum mechanics. The internal ro-vibrational motion of co New directions in molecular scattering
The Mossavar-Rahmani Center for Business and Government (M-RCBG) at Harvard's Kennedy School of Government announced Aden Barton as the 2024 winner of the John T. Dunlop Undergraduate Thesis Prize in Business and Government. Barton won for his thesis, "The Causal Effect of Welfare Retrenchment: Evidence from Medicaid and SNAP."