Quantum Computing Summer School Fellowship

Summary of Program

The Quantum Computing Summer School is an immersive 10-week curriculum that includes tutorials from world-leading experts in quantum computation as well as one-on-one mentoring from LANL staff scientists who are conducting cutting-edge quantum computing research. Summer school fellowship recipients will be exposed to the theoretical foundations of quantum computation and will become skilled at programming commercial quantum computers, such as those developed by D-Wave Systems, Rigetti, and IBM. Roughly twenty students (with the precise number determined based on the applicant pool) will be awarded a fellowship from LANL for the summer school. The fellowship amount ranges from $7,500 to $15,500, based on academic rank (junior, senior, 1st year graduate student, etc.).

Applications for 2023 are open until January 22nd, 2023. 

Highlights from 2022 School

Invited speakers:
Tameem Albash (University of New Mexico), Piotr Czarnik (Jagiellonian University), David Gosset (University of Waterloo), Aram Harrow (MIT), Robert Huang (Caltech), Nathan Killoran (Xanadu), Jarrod McClean (Google), Peter Shor (MIT), Guillaume Verdon, Stephan Woerner (IBM Research)

Finished Projects:

• Resource frugal optimizer for quantum machine learning
• On the practical usefulness of the Hardware Efficient Ansatz
• Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
• Theory for Equivariant Quantum Neural Networks 
• Representation Theory for Geometric Quantum Machine Learning

Highlights from 2021 School

Invited speakers: Tameem Albash (University of New Mexico), Andrew Childs (University of Maryland), Elizabeth Crosson (University of New Mexico), Jens Eisert (Free University of Berlin), David Gosset (University of Waterloo), Shelby Kimmel (Middlebury College), Raymond Laflamme (Institute for Quantum Computing), Pavel Lougovski (Amazon Web Services), Mohan Sarovar (Sandia), Kristan Temme (IBM)

Finished Projects:

• Subtleties in the trainability of quantum machine learning models
• Theory of overparametrization in quantum neural networks
• Entangled Datasets for Quantum Machine Learning
• Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
• Adaptive shot allocation for fast convergence in variational quantum algorithms
• Unifying and benchmarking state-of-the-art quantum error mitigation techniques
• Single-Qubit Cross Platform Comparison of Quantum Computing Hardware

Highlights from 2020 School

Invited speakers: Tameem Albash (U New Mexico), Juan Miguel Arrazola (Xanadu), Robin Blume-Kohout (Sandia), Thomas Bromley (Xanadu), Elizabeth Crosson (U New Mexico), Bill Fefferman (U Chicago), Jay Gambetta (IBM), Sonika Johri (IonQ), Mikhail Lukin (Harvard), Eleanor Rieffel (NASA), Maria Schuld (Xanadu), Graeme Smith (U Colorado), Wim van Dam (UCSB / QCWare), Nathan Wiebe (U Toronto)

Finished Projects:

• Unified Approach to Data-driven Quantum Error Mitigation
• Variational Quantum Algorithm for Estimating the Quantum Fisher Information
• A Generalized Measure of Quantum Fisher Information
• Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in VQE
• Noise- Induced Barren Plateaus in Variational Quantum Algorithms
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks

Highlights from 2019 School

Invited Speakers: Scott Aaronson (UT Austin), Fernando Brandao (CalTech), Elizabeth Crosson (U New Mexico), Christopher Granade (Microsoft Research), Travis Humble (ORNL), John Martinis (Google), Margaret Martonosi (Princeton U), Jarrod McClean (Google), Seth Merkel (IBM), Chris Monroe (U Maryland), Alejandro Perdomo-Ortiz (Zapata Computing), Mauricio Reis (D-Wave), Will Zeng (Stanford U)

Finished Projects:

• An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
• Variational Quantum Linear Solver: A Hybrid Algorithm for Linear Systems
• Noise Resilience of Variational Quantum Compiling
• The Quantum Alternating Operator Ansatz on Max-k Vertex Cover
• Quantum Isomer Search
• The Potential of Quantum Annealing for Rapid Solution Structure Identification

Highlights from 2018 School

Invited Speakers: Sergio Boixo (Google), Alex Condello (D-Wave), Susan Coppersmith (Wisconsin), Ivan Deutsch (UNM), Jungsang Kim (IonQ / Duke), Daniel Lidar (USC), Doug McClure (IBM), Peter Shor (MIT), Matthias Troyer (Microsoft), Eleanor Rieffel (NASA), Will Zeng (Rigetti)

Finished Projects:

• Variational Quantum State Diagonalization
• Quantum-Assisted Quantum Compiling
• Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time

Curriculum

In the first 2 weeks, students will attend lectures given by world-leading experts – from both academia and industry – in quantum computing research. The lectures will cover the following topics:

• Introduction to quantum-mechanical formalism and quantum information theory
• Gate-based quantum computing
  • Universal sets of gates
  • Shor’s, Grover’s, and other algorithms
  • Quantum error correction and fault tolerance
  • Hands-on programming of Rigetti's and IBM’s quantum computers
• Adiabatic quantum computing
  • Introduction to adiabatic algorithms, adiabatic theorem
  • Encoding problems in Ising-model Hamiltonians
  • Hands-on programming of D-Wave’s quantum computer
• Applications
  • Data science / machine learning
  • Quantum chemistry
  • Simulating many-body systems
  • Solving optimization problems

Following the 2-week lecture period, each student will work on a research project in quantum computing for the remaining 8 weeks. For this research project, each student will be paired with a LANL mentor who will propose project topics and provide guidance. Each project will involve some hands-on programming of a quantum computer (either Rigetti's, IBM’s, or D-Wave’s). If time permits, the student will begin preparing their results for publication.

Students

This highly-selective program is designed for upper division undergraduates to early graduate students. We encourage students from all STEM majors (physics, computer science, engineering, math, chemistry, etc.) to apply. Ideally, students should have some familiarity with quantum mechanics and/or linear algebra, as well as some basic programming skills (e.g., in Python). We note that both US citizens and non-US citizens are eligible to apply (citizenship is not a requirement).

Duration

The program will begin on June 5th, 2023 and will last 10 weeks.