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Quantum Computing (QC)

Thesis Topics

The Quantum Computing Group offers a variety of BSc or MSc thesis projects on topics in quantum information processing, entanglement theory, secure quantum communication, and foundations of quantum mechanics.

Bachelor Theses

Please contact us for a consultation if you are interested in writing a bachelor thesis in our group.

Prerequisites: Knowledge of elementary linear algebra and/or good programming skills in Python/MATLAB.

Currently no items available.

Master Theses

Prerequisites: Knowledge of elementary linear algebra and/or good programming skills in Python/MATLAB. It's a plus if you have completed or are currently taking courses in Quantum Computing, Quantum Information Processing, or Foundations of Quantum Mechanics.

Prof. Dr. Mariami Gachechiladze

mariami.gachechiladze[at]tu-...

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Quantum Physics

Title: lecture notes on quantum computing.

Abstract: These are the lecture notes of the master's course "Quantum Computing", taught at Chalmers University of Technology every fall since 2020, with participation of students from RWTH Aachen and Delft University of Technology. The aim of this course is to provide a theoretical overview of quantum computing, excluding specific hardware implementations. Topics covered in these notes include quantum algorithms (such as Grover's algorithm, the quantum Fourier transform, phase estimation, and Shor's algorithm), variational quantum algorithms that utilise an interplay between classical and quantum computers [such as the variational quantum eigensolver (VQE) and the quantum approximate optimisation algorithm (QAOA), among others], quantum error correction, various versions of quantum computing (such as measurement-based quantum computation, adiabatic quantum computation, and the continuous-variable approach to quantum information), the intersection of quantum computing and machine learning, and quantum complexity theory. Lectures on these topics are compiled into 12 chapters, most of which contain a few suggested exercises at the end, and interspersed with four tutorials, which provide practical exercises as well as further details. At Chalmers, the course is taught in seven weeks, with three two-hour lectures or tutorials per week. It is recommended that the students taking the course have some previous experience with quantum physics, but not strictly necessary.

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Master of Professional Studies in Quantum Computing

The Master of Professional Studies in Quantum Computing is a 30-credit, 10-course, non-thesis graduate program that provides students with foundational, practical, and theoretical topics of quantum computing. Students discover current state-of-the-art quantum computing technology and areas of application, and explore origins, evolution, and possible future states of this technology. Experiential learning is at the core of the program and courses provide students with ample opportunities to apply concepts on current-day commercial quantum computing hardware.

The curriculum prepares students to apply the principles and techniques of quantum computing to the solution of a variety of problems in optimization, secure communications, encryption, materials discovery and any such problems that require considerable computing resources. Students develop quantum computing programs and implement them on quantum computing platforms. Students learn to differentiate the many technologies currently used to implement quantum computers and compare their intrinsic strengths and limitations. No prior knowledge in quantum physics or quantum computing is necessary. 

This 30-credit graduate program that can be completed in less than two years. The program features in-person instruction. Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment. The program is offered through the Science Academy in the College of Computer, Mathematical, and Natural Sciences . Instruction is provided by university faculty and experts in the field. Classes are held in the fall, spring, and summer terms.

Application Deadline

Fall 2024 International Students: March 15, 2024 Domestic Students: June 14, 2024

Any student applying for admission to a graduate program at the University of Maryland must meet the following minimum admission criteria as established by the Graduate School.

• Earned a four-year baccalaureate degree from a regionally accredited U.S. institution, or an equivalent degree from a non-U.S. institution.
• Earned a 3.0 GPA (on a 4.0 scale) in all prior undergraduate and graduate coursework.

General Requirements:

• Statement of Purpose
• Transcript(s)
• TOEFL/IELTS/PTE (international graduate students)

Program-Specific Requirements:

• Graduate Record Examination (GRE) (optional)
• Resume/Curriculum vitae
• Description of research/work experience in engineering, mathematics, and natural sciences
• Prior coursework establishing quantitative ability (linear algebra and calculus required)
• Proficiency in programming languages, Python preferred, demonstrated either through prior programming coursework or substantial software development experience

No prior knowledge in quantum physics or quantum computing is necessary. 

The MPS in Quantum Computing is 30 credits and consists of 7 core courses and 3 electives. Special topics include quantum networks, quantum thermodynamics, quantum machine learning, quantum Monte Carlo, quantum information theory, and quantum computing hardware.

Sample Plan of Study (Full-time, 1.5 years)

Semester 1 (fall)

MSQC601 The Mathematics and Methods of Quantum Computing (core)

MSQC602 Physics of Quantum Devices (core)

MSQC603 Principles of Machine Learning (core)

Semester 2 (spring)

MSQC604 Quantum Computing Architectures and Algorithms (core)

MSQC606 Practical Quantum Computing (core)

MSQC610 Quantum Machine Learning (elective)

Semester 3 (summer)

MSQC612 Quantum Computing Hardware (elective)

Semester 4 (fall)

MSQC605 Advanced Quantum Computing and Applications (core)

MSQC607 Advanced Topics in Quantum Computing (core)

MSQC615 Quantum Thermodynamics (elective)

Sample Plan of Study (Part-time, 2 years)

MSQC613 Quantum Monte Carlo and Applications (elective)

Semester 5 (spring)

Find up to date tuition and fee information  for the MPS in Quantum Computing.

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Theses on the topic of quantum computing

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.

Method Development in Quantum Machine Learning (B.Sc, M.Sc)

The AQUAS project examines how quantum computers can be used to develop catalysts for electrolyzers. To this end, elaborate quantum chemical simulations are needed to identify suitable catalyst materials which will make hydrogen production more efficient. In this project, we study regression-based QML algorithms that can be used to complement simulations by generating suitable surrogate models. This is an exciting and forward-looking topic in the superposition state of quantum computing and hydrogen research.

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.

Quantum Boltzmann Machines for Simulating Molecules (M.Sc.)

Quantum computers have enormous potential when it comes to simulating the quantum mechanics of molecules. Quantum computing is used to approximate the wave function of electrons in a molecule. The approach is variationally optimized, and the energy of the molecule is minimized in the process. A possible approach for the wave function is the use of so-called Restricted Boltzmann Machines (RBMs), a kind of artificial neural network for generating probability distributions. RBMs can be implemented in quantum computing in a similar way. This approach has already been used successfully to simulate molecules.   

In this master thesis, we will study the suitability of these Quantum Boltzmann Machines (QBMs) for simulating the quantum mechanics of molecules as well as the impact of different designs of QBMs on results and optimization. The first part of the thesis will involve implementing and optimizing a QBM for use in quantum computing. Various design concepts will be evaluated. In the next step, tests will be performed to see how well the different concepts can approximate the exact solution of the molecular Schrödinger equation. The thesis will conclude by running the quantum Boltzmann machine on modern quantum computers and gaining experience with the still-significant measurement noise associated with today's hardware.

The thesis provides an exciting opportunity to explore current challenges in quantum computing. Prior knowledge of molecular quantum physics is a great advantage as well as an interest in working independently on complex topics.

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Design of a Real-Time Embedded Control System for Quantum Computing Experiments

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Quantum Computing Technology

Live this exciting technological revolution… and get some hands-on experience with industry experts

Course Enrollments close on  September 10th, 2023

Quantum computing today.

Quantum Computing is an emerging discipline and a potentially disruptive technology to which companies are beginning to give importance as a key technological agent for the future. It is a technology that, because it is centered on computing, necessarily affects all scientific-technical disciplines and, directly or indirectly, all the products and services that companies currently offer to society.

Quantum Computing changes in its essence the engine of the Information Society in which we live, by proposing a computing model that radically breaks with previous technology. This new technology has the capacity to address computational problems that current computers are not expected to be able to deal with, such as the design of new materials or the analysis and synthesis of drugs.

On the other hand, Quantum Computing has changed the approaches to Information Security, by breaking the current protocols of public key cryptography. This fact, with enormous repercussions in today's society, is undoubtedly one of the main reasons why companies are interested in Quantum Computing. In this context, the demand for experts in Quantum Technologies and Quantum Computing is expected to increase considerably in the coming years.

The purpose of this Master is to train those professionals that companies are beginning to demand. To do this, the following objectives are proposed: (1) Provide a solvent basic training in the fundamentals of quantum computing and communication, (2) That students know the challenges and current research lines of quantum technologies and (3) Train professionals for companies that work with computing and quantum communication technologies.

Quatum Explore Initiative

Universidad politécnica de madrid launches the 4th edition of its online master in quantum computing technology.

Universidad Politécnica de Madrid presents the 4th edition of its online Master in Quantum Computing Technology. The Master's degree has a remarkable practical orientation. Fifty percent of the contents are applied. The University has the collaboration of Accenture as a technology partner. Finally, the master is designed for Graduates in Computer Engineering, Mathematics, Physics and Telecommunications and Industrial Engineering.

Main characteristics of the course:

• Online classes
• Online assessment
• Personal attention online
• Notes of the subjects with examples
• Worksheets and problems
• Class recordings
• Complementary online seminars
• Recordings of complementary seminars

Do not miss the opportunity, get one of the 30 available places!

Sign up for the Master in Quantum Computing Technology 2023-24

Registration open until September 10th, 2023

Online classes start on September 25th, 2023

Two full scholarships

For upm staff.

Two full scholarships are offered for UPM staff in each edition of the Master.

Accenture staff enjoy

A partial scholarship.

Accenture staff enjoy a partial scholarship of twenty five percent of enrolment rates.

Here’s what people are saying about the course

José M. Atienza

Vice-Chancellor of UPM

 UPM once again championing new technologies

Ever since its foundation, the Universidad Politécnica de Madrid has pushed for technological development as a driver of social development. The launch of its Master in Quantum Computing Technology exemplifies the UPM's leading role in the most advanced technologies, specifically revolutionary quantum technologies, where it rubs elbows with the very best in the world.

Agustín Yagüe

Dean of ETSISI

Our contribution to research in Quantum Technologies

Since the beginning of its doctoral program, ETS de Ingeniería de Sistemas Informáticos has been committed to Quantum Computing not only in research but also in teaching. The first doctoral thesis on this topic won the extraordinary doctorate award from the University and there are currently more students developing their thesis in this area. The proposal for this Master by the head of the School of Research in Quantum Computing reinforces our leadership in these innovative technologies.

Contents of the Syllabus

One credit: 4 hours of lectures + team work + individual work, module 1: adaptation (3,75 credits).

• Mathematics adaptation module (2 credits).
• Computer Science adaptation module (1 credit).
• Physics adaptation module (0,75 credits).

Module 2: First Term (24  credits )

• Introduction to Quantum Computing (6 credits).
• Current Quantum Technology (1,5 credits).
• Quantum Algorithms (4,5 credits).
• Quantum Communications (3 credits).
• Quantum Cryptography (4,5 credits).
• Practices with Gate-Oriented Quantum Computing I (4,5 credits).

Module 3: Second Term  (24 credits)

• Quantum Simulation (1,5 credits).
• Adiabatic Quantum Computing (3 credits).
• Quantum Walk Algorithms (3 credits).
• Quantum Errors and Quantum Codes (3 credits).
• Practices with Gate-Oriented Quantum Computing II (7,5 credits).
• Practices with Adiabatic Quantum Computing (6 credits).

Module 4: Master Thesis Project  (12 credits)

The Master Thesis is a research and/or development project that addresses a problem of interest to the scientific community or to companies engaged in the development of Quantum Computing Technologies.

About the teachers of the Master

The teaching staff of the Master is made up of researchers from Universidad Politécnica de Madrid specialized in Quantum Computing and Accenture employees with experience in developing commercial Quantum Computing projects.

They have experience in the direction of Degree and Master Theses and the supervision of Ph. D. Theses, and have relevant contributions in Quantum Computing.

Biography of the members of the Master's teaching staff

Jesús Lacalle

The Director of our Master

Jesús Lacalle has published many articles on Quantum Computing, among which stand out his Discrete Quantum Computing model and his formula for the variance of the sum of two independent quantum computing errors.

He has directed three Degree Theses on Quantum Computing, one of which has received an award from the School, and a PhD Thesis on the same subject that won the award for extraordinary PhD Thesis from the University. He is currently supervising two more PhD Theses on Quantum Computing.

Giannicola Scarpa

Our most international teacher

Giannicola Scarpa holds a PhD in quantum information from the University of Amsterdam. His research relates to entanglement theory and its applications in complexity, economics, source-channel coding, combinatorics, and cryptography. He published in top-ranked journals such as IEEE Transactions on Information Theory and Theory of Computing, and gave talks at conferences like ICALP and QIP. His extensive network of collaborators includes Simone Severini (Amazon Web Services), Harry Buhrman (QuSoft) and the economist Adam Brandenburger (NYU). He is a gender equality and equal opportunities advocate.

Rafael Delgado

One of our latest additions

Rafael L. Delgado has a PhD in theoretical physics from Universidad Complutense de Madrid. He has experience on high energy physics, both during his PhD and his second postdoc at INFN-Fi. He has also some experience on condensed matter and dark matter. As a researcher on theoretical physics, he has a long experience using several supercomputing facilities: the Tirant cluster of the BSC-CNS in; the facilities of the Leibniz Supercomputing Centre (SuperMUC, SuperM UC-NG and C2PAP) while he was at TUM; the Fermilab high capacity storage system; and local clusters on UCM, TUM (T30f cluster) and INFN-Fi. He was also involved in a project involving the usage of the computing facilities of the Theory Department at CERN.

Alberto García

Our Quantum Data Scientist

Alberto García is graduated on Physics by Universidad Complutense de Madrid. On his 4th year degree, he specialized in Quantum Information in Technische Universität München (TUM) where he also developed his bachelor thesis on Quantum Cryptogrpahy. On 2017, he joined Accenture and, three months later, he helped to launch the Quantum initiative inside Accenture Digital in Spain. Since then, he has been developing real business use cases with both quantum paradigms (adiabatic and gate model) and carrying out benchmarks of all available real quantum computers to determine the state-of-art of Quantum Computing.

Luis M. Pozo

One of our multidisciplinary teachers

Luis M. Pozo has a PhD in Mathematics from the Universidad Complutense de Madrid, and also a Physics degree by the UNED. He has teached in the Universidad Complutense from 1997 to 2012, and in the Universidad Politécnica de Madrid since then. He has several publications on Theoretical Physics from a mathematical point of view, and has collaborated with Prof. Lacalle in three articles on quantum computing errors.

Ulises Arranz

Ex Accenture

Our link with the industry

Ulises Arranz studied Computer Science at Universidad Politécnica de Madrid. He worked in Accenture for +32 years, focused on technology and innovation areas.

He launched and managed the Quantum Computing initiative in Accenture Spain for 3 years. He has participated in multiple events promoting and creating awareness around quantum technologies. He has been part of the EU Strategic Advisory Board for the European Quantum Flagship. He is now actively participating in the launching of the new European Quantum Industrial Consortium.

Rafael Martín-Cuevas

Our expert in Quantum Computing Architectures

Rafael Martín-Cuevas studied Computer Science at Universidad Politécnica de Madrid, where he is pursuing a Master's Degree on Software for Distributed and Embedded Systems -with a Master's Thesis on Quantum Computing-, and a PhD in Quantum Computing. Parallelly, he has been part of Accenture's Quantum Computing initiative since 2018 (mainly in the research and development of applications and architectures), is certified as Qiskit Advocate by IBM, and is involved in several training initiatives.

Alejandro Borrallo

Our expert in Quantum Financial Services

Alejandro Borrallo is a physicist within the Quantum Computing team at Fujitsu Spain. He completed his master’s degree in theoretical physics on the Universidad Complutense de Madrid with specialization in Quantum Information Theory. He has more than 4 years of experience in identification and implementation of real business uses cases by using Quantum and Quantum Inspired technologies, mainly in the Financial Services context. He is currently working with the Digital Annealer technology in Fujitsu as senior optimization engineer, delivering Quantum Inspired solutions for optimization problems across different industries.

Carlos González

Our expert in quantum communications

Carlos González has a PhD in Mathematics from Universidad Complutense de Madrid. His research in Quantum Information Theory relates to entanglement theory, complexity theory, cryptography and random quantum evolutions with a recent interest in Machine Learning applications in which he is supervising a Postdoc. He has collaborated with more than twenty researchers from top institutions (UCL, MPQ, CWI, IQOQI) in fourteen publications in top ranked journals in Physics and Mathematical Physics.

André L. Fonseca

Universidad ORT Uruguay

Our first PhD in Quantum Computing

André Fonseca de Oliveira has an Electrical Engineer degree and a Master in Control Theory, both from the Universidad de la República Uruguay, and a PhD in Quantum Computing from the Universidad Politécnica de Madrid. He has five papers on Quantum Information and Computation in journals and more than fifteen articles in congresses and conferences. He has directed two Degree and a Master Thesis on the subject. He is currently supervising a Master and a Degree Thesis.

Rafael Hernández

Shreyas Ramesh 

Responsible for the Accenture Global Quantum Computing

Shreyas is a Director, a Global Lead within Accenture's Technology Incubation (Quantum Computing group), with over 20 years of experience in leading and implementing cutting-edge solutions within Quantum Computing, xR, Mobility, Robotics and IoT across various industries and geographies; managing 100+ people globally and enabling 10,000+ employees across several emerging technologies. He is also responsible for the Global Quantum Computing group's P&L that extends to Accenture's Federal Services. He is an Accenture Certified Senior Technology Architect, certified via MIT on Quantum Information Sciences and an inventor within Accenture with several patents across industries.

Activities and results related to the Master:

• Observers of quantum systems cannot agree to disagree: l ink 1 ,  link 2 ,   link 3

Don’t wait! Look at what past students have to say about this course

Srinjoy Ganguly

CEO of AdroitERA

Graduated in the Master

The master in quantum technology provided by Technical University of Madrid has provided me an extensive knowledge in the field of quantum computing and its surrounding areas. The unique aspect of this masters which I found to be very beneficial was the collaboration between the academia and industry which enabled to bridge the gaps in learning. I have learnt a lot of important subjects related to quantum machine learning, adiabatic quantum computation and various industry use-cases of quantum computing applied frequently. Apart from the subjects, I thoroughly enjoyed working on my master’s thesis related to quantum natural language processing which was not related to the subjects taught to me but my supervisor - Luis M. Pozo and every other faculty member were extremely supportive and ensured that I do well in my thesis. Every faculty promoted original and critical thinking throughout the course. I strongly recommend this master’s program to anyone who would like to pursue their career in the field of quantum computing because of the presence of industry and academic leaders, professionals and mentors guiding you at each and every step to become proficient in state of the art quantum technologies.

Laura N. Gatti

Universidad de Montevideo

Master Thesis and PhD Student

During my Master's thesis and my PhD I have received extensive theoretical training on Quantum Computing, one of the most important research topics today. I have learned to use powerful tools to build new algorithms and to implement practical applications. The solid theoretical base acquired allows me to understand the evolution of a rabidly dynamic industry based on Quantum Technologies. I especially remember the academic discussion generated to grow in the area.

Senaida Hernández

Postdoctoral Student

During my work under the supervision of professors of the Master, I have come to understand the advantages that Quantum Mechanics may offer to Artificial Intelligence. I have also learnt to implement Machine Learning algorithms inspired on Quantum Physics in Python. From my experience working with them and my background as a quantum physicist, I strongly recommend this Master, as it is one of its kind, it is strongly based on state-of-the-art quantum technologies and counts with a strong team of researchers and users of this technology as professors and mentors.

Todor Krasimirov

Accenture Scholar and Teacher Team Collaborator

According to my personal experience in Quantum Computing working hand-to-hand with Accenture and Giannicola Scarpa, this master methodology encourages students to be part of the interesting and innovative world of quantum computation by giving them appropriate introduction lectures adapted to their previous knowledge. The contents gather the most relevant topics related with the existing types of quantum computation and their best practical applications, ensuring the understanding of how quantum mechanics is used as a form of information processing in a different and powerful way that allows high-complexity problem resolution.

Frequently Asked Questions

How is the selection process.

The places will be assigned to the students with the highest score by adding the following values: 1- Three times the average of the record of their university degree (maximum 30), if it is in Computer Science, Computer Engineering, Industrial Engineering, Mathematics, Physics or Telecommunications Engineering. 2- Two times the average of the record of their university degree (maximum 20), if point 1 above does not apply. 3- Two times the average of the record of their university Master's degree (maximum 20), if the student has these studies and it is in Computer Science, Computer Engineering, Industrial Engineering, Mathematics, Physics or Telecommunications Engineering. 4- Once the average of the record of their university Master's degree (maximum 10), if the student has these studies and point 3 above does not apply. 5- Three points for each year of professional experience in the field of the Master (maximum 15).

How is the practical part?

The practices are carried out on services provided by different companies (D-Wave Systems, IBM, QUTE Platform, Rigetti Computing...). Algorithms adapted to each of the systems will be implemented and run and new algorithms will be designed for the industry.

Gemini Center on Quantum Computing

Preparing for the quantum revolution

Master’s thesis

Possible topics for master thesis:

Contact us if you are interested:

20 Quantum Computing Masters Courses

As the field of Quantum Computing continues to grow, the need for University and advanced educated individuals to make up the Quantum Computing workforce has grown as well. Below is a list of Master’s Degree Programs in the Quantum Computing Field offered by Universities around the world, featuring programs that range from onsite to online, and from one to two years in duration.

University College London (UCL)

UCL offers an MSc. degree in Quantum Technologies , with a robust curriculum that features compulsory modules in Advanced Quantum Theory, Quantum Computation and Communication, Quantum Technologies, and an Individual Research Project, among other non-compulsory optional modules. Optional modules include Astronomical Spectroscopy, Materials and Energy Materials, Physics of the Earth, Physics of Advanced Materials, Theoretical Condensed Matter, Advanced Topics in Statistical Mechanics, and more.

The program is offered under the UCL department of Physics and Astronomy, which is currently ranked 4th in the UK in the QS World University Rankings by Subject 2022 for Physics & Astronomy. The program is offered onsite at UCL’s London Bloomsbury Campus. For domestic students, the yearly tuition spend comes to 14,100 GBP, with tuition for international students set at 35,000 GBP.

Duration: One Calendar Year

University of Wisconsin–Madison

The University of Wisconsin-Madison offers an M.S. in Physics–Quantum Computing (MSPQC) program . Located in Wisconsin, USA, the program consists of an interesting selection of core courses and optional or elective courses that total 30 credits at the end of the program duration. Courses featured on the syllabus include Introduction to Quantum Computing, Introduction to Quantum Mechanics, Quantum Mechanics, Atomic and Quantum Physics, Introduction to Atomic Structure, Advanced Quantum Computing, Solid State Physics, Advanced Solid State Physics, Atomic and Quantum Physics, Quantum Computing Laboratory, and Independent Study.

Tuition is calculated per the number of credits in the program, with 8+ credits set at a tuition rate of $6,125.28 for domestic students, and $12,788.72 for International Students. The program is offered Onsite.

University of Rhode Island

Students who have earned a bachelor’s degree in Physics or a closely related field and have a basic understanding of quantum mechanics are eligible for the M.S. in Quantum Computing offered by URI. To guarantee that our graduates have the necessary grounding for employment or further education in this rapidly developing subject, URI the university collaborates with industrial enterprises and institutes.

Although the University’s website offers minimal information about the program, intending applicants are encouraged to contact the University directly for more information.

Duke University

Duke University offers its Quantum Computing Concentration with either an MS pathway or an MEng pathway. Located in North Carolina, the program is onsite and is available to international and domestic applicants alike. The syllabus includes Introduction to Quantum Engineering, Quantum Information and Computation, Quantum Information Theory, Quantum Error Correction, Programming, Data Structures, and Algorithms in C++, Fundamentals of Computer Systems, and other notable software and hardware courses within Duke’s Electrical and Computer, Engineering (ECE) department.

For information on tuition and more on the program, intending applicants can contact the department directly via the course website .

Purdue University

The first online-only entry on this list is offered by Purdue University located in Indiana, USA. The MicroMasters® Program in Quantum Technology : Computing is offered completely online and taught by Professors at Purdue University including Avinash Rustagi, a Post-doctoral Research Associate, in the Department of Electrical and Computer Engineering; Zubin Jacob, an Associate Professor of Electrical and Computer Engineering, Pramey Upadhyaya, an Assistant Professor of Electrical and Computer Engineering, Mahdi Hosseini, an Assistant Professor of Electrical and Computer Engineering, and Mohammad Mushfiqur Rahman, a PhD Student.

Students would be required to commit 7-9 hours per week of dedicated time to learning and the courses are instructor-led. The syllabus consists of five courses, which include Introduction to Quantum Science & Technology, Applied Quantum Computing I: Fundamentals, Applied Quantum Computing II: Hardware, Applied Quantum Computing III: Algorithm, and Software Quantum Detectors. Tuition is set at $4,725.

Duration: 10 months.

University of Sussex

The University of Sussex located in Brighton, UK offers a Quantum Technology MSc that is taken fully in person, but with an online version that is available. The modules and courses in the program include Quantum Optics and Quantum Information, MSc Project Research Skills (PGT), Practical Quantum Technologies, and an MSc Project P&A (PGT), among other optional courses.

Tuition is set at £11,275 per year for full-time domestic students and £22,975 per year for full-time international students.

Duration: One year full-time or two years part-time.

Trinity College Dublin, The University of Dublin

The University of Dublin offers two in-person master’s degree programs , which are the Quantum Science and Technology program; and the Quantum Fields, Strings and Gravity program. Modules offered in both of these programs include several relevant courses like Introduction to Quantum Information Science, Introduction to quantum information theory, Core concepts e.g. no-cloning, teleportation and entanglement theory, Special topics and the quantum industry, Current challenges and opportunities in the quantum sector, Industrial and academic speakers, Quantum Material Science, Harnessing quantum effects in superconducting systems, among others, with one of the modules being a Quantum project/Internship.

Tuition for domestic students for both programs comes to €10815, and €9,975 respectively, and €24,938, and €22,575 for international students. The program is offered in the university’s Dublin campus in Ireland.

Duration: One year.

Australian National University

The ANU’s Master of Science in Quantum Technology is an in-person program in Canberra, Australia. The program offers a range of modules including Optical Physics, Rapid Prototyping, Prototyping and Systems Integration, Quantum Technology, Masters Special Topics in Physics, Physics for Future Leaders, Quantum Mechanics courses, Quantum Technology elective courses, Programming for Scientists, Electronics and Data Analysis, among others. In total, a maximum of 24 credits can be taken by students in order to complete the program.

Tuition is set at $38,800 per 48 units for domestic students and $49,330.00 per annum for international students.

Duration: Two years full-time.

The University of Queensland Australia

Students of the University of Queensland Australia’s Master of Quantum Technology will take courses designed to increase their knowledge of the subject matter, including Condensed Matter Physics, Atomic Physics & Quantum Optics, Statistical Mechanics, Quantum Mechanics II, Quantum Technologies, Quantum Mechanics I, and an MQTech Research Project, among other courses in Physics and Computer Science.

Students will study fully in person on the university’s campus in Brisbane, Australia. Domestic students’ tuition is set at AUD $31,200 and for international students, tuition is set at AUD $46,864.

Duration: 1.5 years

Eidgenössische Technische Hochschule (ETH) Zürich

ETH offers a Master’s Degree in Quantum Engineering on-site on its Zurich, Switzerland campus. Students will take up to 24 credit units in courses and more credit units assigned to a semester project, a QuanTech Workshop, a master’s thesis, and an internship in the industry. Tuition is currently set at 730 Swiss francs per semester.

Duration: Two years.

University of California, Los Angeles

The UCLA Master of Quantum engineering program is a full-time course of study that includes an internship, a capstone presentation on the internship, and nine courses totaling 36 units. Courses in this program include Introduction to Quantum Computing, Quantum Programming, Lab Modules, Introduction to Quantum Information, Quantum Algorithms, Theory of Quantum Devices and other elective courses. The program will be undertaken in California, USA, with tuition set at $51,174.19.

Notable members of faculty at UCLA associated with the program include Clarice Aiello, an Assistant Professor in the Department of Electrical and Computer Engineering; Amartya Banerjee, an Assistant Professor in the Department of Materials Science and Engineering; Wes Campbell, an Associate Professor in the Department of Physics & Astronomy, among others.

Duration: One Year Full time

Université PSL

Taught in person on PSL’s Paris campus, the Master of Quantum Science and Technology features two research internship as part of the program, as well as several courses across four semesters that cover the subject matter. These courses include; Fundamentals of quantum systems, Quantum mechanics, Quantum optics, Quantum communications, Mathematical methods for quantum engineering, Introduction to quantum computing, Support hardware for Quantum technologies, Control of open quantum systems, Quantum detectors & sensors, Quantum programming, Case Studies: Applications of Quantum Technology, Single photons emitters and detectors, among others.

Tuition fees are set by ministerial decree (env. €243). Potential students are advised to note that they will also need to provide proof of payment for the CVEC (Student Life and Campus Fee) of €95. On the program’s steering commitee are Nicolas Bergeal (ESPCI Paris – PSL), Jérôme Lodewyck (Observatoire de Paris – PSL), Franck Pereira (Observatoire de Paris – PSL), Pierre Rouchon (Mines Paris – PSL), Philippe Goldner (Ecole nationale supérieure de Chimie de Paris – PSL), Carlo Sirtori (ENS – PSL).

Duration: Two Years

German Academic Exchange Service, or DAAD

DAAD offers the MSc High Performance Computing / Quantum Computing (HPC/QC) program on its Deggendorf, Germany campus. Across three semesters, students will study various modules including Physics for HPC/QC, Software Engineering, HPC/QC Programming Lab, HPC/QC Technology, Advanced Mathematics for HPC/QC, Computer Architectures for HPC/QC, Networks for HPC/QC, Optimisation Methods, HPC/QC Infrastructure, System Design and Application of HPC/QC Systems, Advanced Mathematics and Physics for HPC/QC, among others, as well as a master’s thesis, and master’s colloquium.

Tuition is set at 62 EUR student service fee per semester.

Duration: Three semesters.

Nebrija University

Nebrija University offers an opportunity for potential students to earn a Master´s Degree in Quantum Computing through blended learning, using both in-person and online elements. The program consists of several courses and modules, including Physics and mathematics for quantum computing, Quantum technologies, Programming models, Quantum algorithms, Programming languages, Error correction, Tools and simulators, Applications and use cases, Research methodology: Engineering, Skill development in the company, and a Master’s Thesis.

Facaulty and instructors on the program include Professor Jose Luis Rosales Bejarano, Professor Francisco Gálvez Ramírez, Professor Alberto García García, Professor Francisco García Herrero, and José Javier Paulet González.

Tuition is set at 4,695€ for one semester, and 715€ for each additional class.

University of Glasgow

The University of Glasgow’s Quantum Technology MSc is offered in person on the university’s Glasgow, Scotland campus. The program features a master’s thesis in either Physics or Engineering, as well as several courses to be taken over the duration of the program.

The courses include Fundamentals of Sensing and Measurement, Experimental Techniques in Quantum Optics, Introduction to Research in Nanoscience and Nanotechnology, Advanced Data Analysis for Physics and Astronomy, Relativistic Quantum Fields, Statistical Mechanics, Quantum Theory, Magnetism & Superconductivity, Quantum Electronic Devices, and Optical Communications, among others.

Tuition is set at £11520, and £25980 for domestic and international students respectively.

Duration: Twelve months full-time.

University of Barcelona

The University of Barcelona offers a Master’s degree in Quantum Science and Technologies in person on its campus in Barcelona, Spain. The program features an internship and a mix of core and elective courses aimed at grounding students in the subject matter. These courses include Advanced Quantum Mechanics, Condensed Matter Physics, Quantum Information Theory, Entrepreneurship & Innovation, Advanced Quantum Information, Quantum Communications and Cryptography, Quantum Statistical Inference, Quantum Field Theory, Machine Learning Quantum and Classical, Tensor Networks, Quantum Computation, Quantum Materials, Quantum Technology with Superconducting Circuits, among others.

Tuition is currently set at 27.67 euros per credit or 82 euros per credit for students who are not EU nationals and do not currently reside in Spain.

Duration: One year

King Fahd University of Petroleum & Minerals

KFUPM’s Professional Master in Quantum Information and Computing is offered in person on its Dhahran, Saudi Arabia campus. Courses included in the program include Introduction to Quantum Information and Computing, Fundamentals of Quantum Optics, Quantum Algorithms, Math for Quantum Computing, Quantum Computer and Architecture, Quantum Cryptography, Quantum Hardware, Quantum Communication, as well as an Industrial Research project.

Tuition is set at 5000 SAR per semester, and 7500 SAR per semester for domestic and international students respectively.

Duration: Two semesters.

Charles III University of Madrid (Universidad Carlos III de Madrid)

Potential students have the opportunity to earn a Master in Quantum Technologies and Engineering degree in person on the University’s Madrid, Spain campus. Courses in the program include Quantum technologies and engineering, Matrix quantum mechanics, Wave quantum mechanics, Electromagnetic optics and photonics, Quantum computing, Sensors and classical measurement instrumentation systems, Pre-quantum information and communication, Secure computer network systems, Laboratory on quantum computing, Quantum internet and quantum cryptography, Quantum control, Quantum logic and information processing, English Quantum radars, and more core and elective courses.

Tuition for the program for EU students is €4,800 (€80/ECTS credit) per year and for Non-EU students is €7,200 (€120/ECTS credit) per year.

The University of Arizona

The University of Arizona’s Optical Sciences (MS) – Quantum Information Science and Engineering is offered in person in Arizona, USA, and features two unique tracks. 32 units are needed to complete the program with both thesis and non-thesis options, which are the Thesis track, which consists of a research project, a thesis, and a final defense; and the MS Report track, which consists of a project with a lesser scope, a report, and a final defense.

Core courses in the program include Quantum Mechanics, Foundations of Quantum Optics, Introduction to Quantum Information and Computation, and Quantum Nanophotonics. While several elective courses make up a huge part of the program’s offerings including Electromagnetic Waves, Solid State Optics, Probability and Statistics, Statistical Optics, Photonics, Optical Physics, and Lasers, From Photonics Innovation to Marketplace, Laser Beams and Resonators, Fundamentals of Information and Network Security, Q. Inf. Processing and Q. Error Correction, Error Correction, Detection and Estimation in Engineering Systems, Ethical Issues in Information (3), among others.

Tuition is calculated based on units and is currently set at $7,007.68 for 7+ units per year, and $16,978.68 for 9+ units per year for domestic and international students respectively.

Duration: Contact the University of Arizona.

University of Copenhagen

The University of Copenhagen offers a Master of Science (MSc) in Quantum Information Science on its Copenhagen, Denmark campus in collaboration with the Technical University of Denmark (DTU). Modules included in the program will be taught jointly by DTU and the University.

These modules include Introduction to Quantum Information Science (UCPH), Introduction to Quantum Computing (UCPH), Applied Quantum Physics: Quantum Information Technology (DTU), and a choice between a range of restricted elective courses within Quantum Information Theory (UCPH), Physical Implementation of Quantum Information Processing (UCPH), Experimental Techniques in Quantum Technology (DTU), Quantum Compilers (DTU), and Quantum Algorithms and Machine Learning (DTU). Students can also select between a wide range of elective courses which specialize in aspects of computer science, mathematics, or physics.

Tuition is currently set in the range of EUR 10,000 to 17,000 per 60 ECTS/academic year and students are advised to contact the University to obtain specific tuition and fees information.

The availability of advanced learning opportunities in the field of Quantum Computing is growing and now more than ever, aspiring professionals in the Quantum Computing workspace can take advantage of this to position themselves as part of a fast-growing industry.

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The master thesis is a research, innovation and/or development project, with the objective of showing you can work independently on an advanced level, on acquiring and creating new knowledge within your field of specialisation. The project includes defining objectives and goals, gathering relevant background information about the knowledge frontier, doing novel work using relevant methods and documenting and critically evaluating the work and contribution.

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Knowledge : Insight in how knowledge, services and technology are created and reported within the student's master specialty area, and understanding of advanced theory and practice within this topic. Awareness of important principles of research ethics / academic / professional honesty. Skills : Ability to acquire in-depth knowledge in the chosen topic using established working methods, e.g. getting knowledge from literature search and combine this with own previous knowledge and results of own investigations. General competence : Be able to complete a larger, independent project, including defining a project plan with milestones, reporting partial results and writing a master thesis according to established standards.

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Publicaciones ALF

Sergio martín domene winner of a tfm award for research in quantum communications.

Sergio Martín Domene , researcher in the Laser and Photonics Applications group and member of the ERC Attostructura project, has been awarded third prize within the 1st Edition of the Awards for Excellence in Master’s Thesis for Research in Quantum Communications .

The objective of the contest is to recognize the Master’s Thesis presented by USAL students in the years 2022 and 2023 to promote both scientific and technical research and the transfer of knowledge.

The award-winning work is titled “Study of the dynamics of excited magnetization using structured laser pulses.”

One of the most active fields currently on the scientific scene is quantum computing, where the communications of the future are advancing. Although the theory necessary to understand quantum information and computing is quite advanced, technologically it is still a challenge to manufacture physical support devices. In this sense, there are different ways to build or materialize a physical system that is capable of storing a unit of quantum information, called a qubit (quantum bit). Some of these proposals are based on the recent fields of spintronics and magnonics, that is, the use of magnetic systems to enable the storage and manipulation of data. Understanding at a fundamental level how these systems behave is crucial to continue promoting research.

In this work, a theoretical study is carried out through numerical simulations, demonstrating that it is possible to carry out ultrafast control of the magnetization (macroscopic description of a set of spins, a quantum mechanical property) of a ferromagnetic material hundreds of nanometers in size. , thanks to the interaction of structured laser pulses of a few picoseconds in duration. The novel aspect lies in the fact that these generated pulses consist of an intense circularly polarized magnetic field isolated from the electric field, obtained from the strong focusing of two out-of-phase cylindrical vector beams propagating in perpendicular directions. On the one hand, this scheme allows avoiding losses due to the Joule effect in the magnetic sample due to the currents that would be induced by the interaction with the electric field. On the other hand, the interaction of the magnetization field of the sample with the magnetic field of the optical field is much more direct, resulting in a chiral and nonlinear effect on the dynamics when the polarization state is circular.

This award is part of the Complementary Plan in Quantum Communications, within the NextGeneration program of the European Union (PRTRC17.I1) which is financed by the European Union, the Ministry of Science and Innovation, the Transformation and Resilience Recovery Plan and the Junta of Castilla y León through its Nos Impulsa program.

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• 14 November 2023

DeepMind AI accurately forecasts weather — on a desktop computer

• Carissa Wong

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Conventional weather forecasts are the result of intensive processing of data from weather stations around the world. Credit: Carlos Munoz Yague/Look At Science/Science Photo Library

Artificial intelligence (AI) firm Google DeepMind has turned its hand to the intensive science of weather forecasting — and developed a machine-learning model that outperforms the best conventional tools, as well as other AI approaches, at the task.

The model, called GraphCast, can run on a desktop computer, and its predictions are more accurate than those of conventional models — and it makes them in minutes, rather than hours.

“GraphCast currently is leading the race amongst the AI models,” says computer scientist Aditya Grover, at the University of California, Los Angeles. The model was described 1 in Science on 14 November.

Predicting the weather is a complex and energy-intensive task. The standard approach is called numerical weather prediction (NWP), which uses mathematical models based on physical principles. These tools, known as physical models, are run on supercomputers and crunch weather data from buoys, satellites and weather stations worldwide. The calculations accurately map how heat, air and water vapour move through the atmosphere, but they are expensive to run.

Forecast revolution

To reduce the financial and energy costs of forecasting, several technology companies have developed machine-learning models that rapidly predict the future state of global weather from past and current weather data. Among them are DeepMind, computer-chip-maker Nvidia and Chinese tech firm Huawei, alongside a slew of start-up companies such as Atmo, based in Berkeley, California. Of these, Huawei’s Pangu-weather model is the strongest rival to the gold-standard NWP system at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, UK, which provides world-leading weather predictions up to 15 days in advance.

Machine learning is spurring a revolution in weather forecasting, says Matthew Chantry, a machine-learning coordinator at the ECMWF. AI models run 1,000– 10,000 times faster than conventional NWP models, giving scientists more time to interpret and communicate the predictions, says data-visualization researcher Jacob Radford, at the Cooperative Institute for Research in the Atmosphere in Colorado.

GraphCast outperforms conventional and AI-based approaches at most global-weather-forecasting tasks. Researchers first trained the model using estimates of past global weather made from 1979 to 2017 by physical models. This allowed GraphCast to learn links between weather variables such as air pressure, wind, temperature and humidity.

The trained model uses the ‘current’ state of global weather and weather estimates from 6 hours earlier to predict the weather 6 hours ahead. Earlier predictions are fed back into the model, enabling it to make estimates further into the future. DeepMind researchers found that GraphCast could use global weather estimates from 2018 to make forecasts up to ten days ahead in less than a minute, and the predictions were more accurate than were those of the ECMWF’s high-resolution forecasting system (HRES) — one version of its NWP — which takes hours to forecast.

Severe weather

“In the troposphere, which is the part of the atmosphere closest to the surface that affects us all the most, GraphCast outperforms HRES on more than 99% of the 12,000 measurements that we’ve done,” says computer scientist Rémi Lam at DeepMind in London. Across all levels of the atmosphere, the model outperformed HRES on 90% of weather predictions.

GraphCast predicted the state of five weather variables close to Earth’s surface, such as the air temperature 2 metres above the ground, and six atmospheric variables, such as wind speed, further from Earth’s surface.

It also proved useful in predicting severe weather events, such as the paths taken by tropical cyclones, and extreme heat and cold episodes, says Chantry.

When they compared the forecasting ability of GraphCast with that of Pangu-weather, the DeepMind researchers found that their model beat 99% of weather predictions that had been described in a previous Huawei study.

Chantry notes that although GraphCast’s performance was superior to other models in this study, based on its evaluation by certain metrics. Future assessments using other metrics could lead to slightly different results.

Training data

Rather than replacing conventional approaches entirely, machine-learning models, which are still experimental, could boost particular types of weather prediction that standard approaches aren’t good at, says Chantry — such as forecasting rainfall that will hit the ground within a few hours.

“And standard physical models are still needed to provide the estimates of global weather that are initially used to train machine-learning models,” says Chantry. “I anticipate it will be another two to five years before people can use forecasting from machine-learning approaches to make decisions in the real world,” he adds.

In the meantime, problems with machine-learning approaches must be ironed out. Unlike NWP models, researchers cannot fully understand how AIs such as GraphCast work because the decision-making processes happen in AI’s ‘black box’ , says Grover. “This calls into question their reliability,” he says.

AI models also run the risk of amplifying biases in their training data and require a lot of energy for training, although they consume less than NWP models, says Grover.

doi: https://doi.org/10.1038/d41586-023-03552-y

Lam, R. et al. Science https://doi.org/10.1126/science.adi2336 (2023).

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Fellowships, Awards & Honors: Graduate Students

A year in review of honors and achievements by UW Chemistry graduate students.

Jump to Prizes for Best Ph.D. Thesis Jump to Prizes for M.S. Research and Thesis Jump to Merit Awards Jump to University and External Awards

Excellence in Chemistry Graduate Awards

These fellowships were awarded in Autumn 2022 and are funded by various donor-supported gift funds as indicated below each awardee’s name.

Adewunmi Felicia Adebanjo Howard J. Ringold Endowed Fellowship in Chemistry

Austin (Augie) Dobrecevich Chemistry Fellowship Fund

Kinsey Drake Paul H. and Karen Gudiksen Endowed Fund

Adrian Guerrero Ritter Endowed Scholarship Fund

Michaela Gustaitis Mary K. Simeon and Goldie Simeon Read Chemistry Research Endowment

Max Hoffman Faculty Endowment for Graduate Study in Chemistry

Greta Jacobson Eugene S. Mindlin Endowed Fellowship Fund

Thao Kim Basil G. and Gretchen F. Anex Endowed Fund

Jay Lee Rowland Endowed Fellowship in Chemistry

Gage Owens Niels H. Andersen Endowed Graduate Fellowship in Chemistry

Alyson Shoji Dorothy Shimasaki Gilmer Endowed Student Support Fund

Fubin Song Lewis R. and Joan M. Honnen Endowed Fellowship in Chemistry

Maxwell Taub Chemistry Graduate Alumni Fund

Bailey Vahsholtz Paul H. and Karen Gudiksen Endowed Fund

Caleb Walton Lloyd E. and Florence M. West Endowed Fellowship in Chemistry

Jamison Whitten Larry R. Dalton Term Ph.D. Fellowship in Chemistry

Marcus Woodworth Boris and Barbara L. Weinstein Endowed Graduate Fellowship in Chemistry

Haoxian Xu Howard J. Ringold Endowed Fellowship in Chemistry

Chisa Zensho Lloyd E. and Florence M. West Endowed Fellowship in Chemistry

Prizes for Best Ph.D. Thesis

These awards, established in Fiscal Year 2022, recognize doctoral research and carry a $1,000 prize which was disbursed in Spring 2023. They were funded by departmental fellowships and endowed funds established by philanthropic support from faculty, friends, and alumni.

Ulri Lee Gary and Sue Christian Prize for Best Thesis in Analytical Chemistry

Jitkanya Wong Niels H. Andersen Prize for Best Thesis in Organic Chemistry

Rob Weakly B. Seymour Rabinovitch Prize for Best Thesis in Physical Chemistry

Jonathan Kephart George H. Cady Prize for Best Thesis in Inorganic Chemistry

Prizes for M.S. Research and Thesis

These awards, established in Fiscal Year 2022, recognize master’s program research and carry a $500 prize which was disbursed in spring 2023. They were funded by the Lloyd E. and Florence M. West Fellowship in Chemistry which Lloyd and Florence West endowed to give back to the university where Lloyd cultivated the necessary tools for his distinguished career and rewarding life.

Camille Jackson Lloyd E. and Florence M. West Prize for Best Thesis “Investigating the surface reactivity of black phosphorus.”

Bofei Wang Lloyd E. and Florence M. West Prize for Excellence in Research “Regio and diastereoselective synthesis of allylic amine through hydrocupration of terminal alkynes.”

Merit Awards

These awards, revised in Fiscal Year 2023, recognize doctoral research and carry a $1,000 prize which was disbursed in Spring 2023. They were funded by departmental fellowships and endowed funds established by philanthropic support from faculty, friends, and alumni.

Caitlin Cain (Analytical) Kwiram/CCR Fellowship

Jack Geary (Inorganic) Ritter Endowed Scholarship Fund

Kristina Herman (Physical) Kwiram/CCR Fellowship

Micaela Homer (Inorganic) Mary K. Simeon and Goldie Simeon Read Chemistry Research Endowment

Lixin Lu (Physical) Kwiram/CCR Fellowship

Julian Smith-Jones (Organic) Irving and Mildred Shain Endowed Fund in Chemistry

Tammi van Neel (Analytical) Kwiram/CCR Fellowship

Sarah Zeitler (Organic) Irving and Mildred Shain Endowed Fund in Chemistry

University and External Awards

Lejla Biberic Accelerating Quantum-Enabled Technologies NSF Research Traineeship

Madeleine Breshears Advanced Experience Program in Clean Energy, Torrance Tech Due Diligence Track

Caitlin Cain Student Excellence Award, Chinese American Chromatography Association

Tyson Carr Clean Energy Institute Graduate Fellowship

Matthew Chang Clean Energy Institute Graduate Fellowship

Hannah Contreras Clean Energy Institute Graduate Fellowship

Andrei Draguicevic Clean Energy Institute Graduate Fellowship

Erin Dunnington NSF Graduate Research Fellowship Honorable Mention

Matthew Elardo NSF Graduate Research Fellowship

Jacob Finney Clean Energy Education & Training Fellowship

Jack Geary Advanced Experience Program in Clean Energy, Torrance Tech Due Diligence Track

Theresa Gozzo UW Excellence in Teaching Award

Amanda Haack 2022 Younger Chemists Committee ACS Travel Award, Division of Analytical Chemistry

Mercie Hodges Clean Energy Institute Graduate Fellowship

Julisa Juarez Accelerating Quantum-Enabled Technologies NSF Research Traineeship Mark A. Jones-ARCS Foundation Endowed Fellowship in Chemistry

Jonathan Kephart ACS Division of Inorganic Chemistry Young Investigator Award

Phuong Le Clean Energy Institute Graduate Fellowship

Adelaide Levenson Clean Energy Institute Graduate Fellowship

Ben Link Clean Energy Institute Graduate Fellowship

Xiaolin Liu Clean Energy Institute Graduate Fellowship

Anika McManamen Distinguished Thesis Award, UW Graduate School

Ben Mitchell Clean Energy Institute Graduate Fellowship

Hao Nguyen Clean Energy Institute Graduate Fellowship

Emily Nishiwaki Clean Energy Institute Graduate Fellowship

Meredith Pomfret Clean Energy Institute Graduate Fellowship

Justin Pothoof Advanced Experience Program in Clean Energy, Torrance Science Policy Analysis Track

Michael Riehs Clean Energy Institute Graduate Fellowship

Ricardo Rivera-Maldonado 2022 Outreach & Service Award, Clean Energy Institute Clean Energy Education & Training Fellowship

Gillian Shen Clean Energy Institute Graduate Fellowship

Thom Snoeren Clean Energy Institute Graduate Fellowship

Ella Spurlock Nicole A. Boand-ARCS Foundation Endowed Fellowship in Chemistry

Milomir Suvira Graduate School Conference Presentation Award Office of Science Graduate Student Research (SCGSR) award, U.S. Department of Energy (DOE)

Rachel Tenney Clean Energy Institute Graduate Fellowship NSF Graduate Research Fellowship

Jodie Tokihiro Institute of Translational Health Sciences TL1 Fellow

Tammi van Neel Advancing Science Conference Grant, NOBCChE

Kelly Walsh Advanced Experience Program in Clean Energy, Torrance Tech Due Diligence Track

Caleb Walton Accelerating Quantum-Enabled Technologies NSF Research Traineeship

Other Fellowships, Awards & Honors

LEADERSHIP IN DIVERSITY, EQUITY, AND INCLUSION AWARDS FACULTY, STAFF, & POSTDOCS UNDERGRADUATE STUDENTS

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