Spring 2020
APh 250/ME 201: Microwave noise in semiconductor electronic devices
Course description: What do radio telescopes, quantum computers, and deep space communication have in common? The transistor microwave amplifier! Low noise amplifiers are a key technology that allow weak signals to be processed and analyzed. The noise above the standard quantum limit added by these amplifiers thus represents a basic limit to the accuracy of a measurement.
This course will provide a comprehensive overview of the physical origin of noise mechanisms in transistor amplifiers and how they may be mitigated. Specific topics to be covered include:

Mathematical description of stochastic processes and fundamental noise sources

Equivalent circuit noise model of fieldeffect and bipolar junction transistors

Nonequilibrium noise mechanisms including hot electron noise
Prerequisites: Basic knowledge of circuits and semiconductor physics.
Class notes
Fall 2019
APh 250/ME 201: A numerical introduction to tensor networks for quantum simulation
Tensor networks have emerged as a powerful tool for the numerical simulation of quantum manybody systems. This course will cover the fundamentals of tensor networks and recent algorithmic developments from a numerical perspective. Emphasis will be placed on both the theoretical foundation and practical numerical implementation of a variety of 1D and 2D tensor network algorithms. Specific topics to be covered include:

Fundamentals of matrix product states, canonical forms, computation of expectation values, matrix product operators, and other basics

Numerical renormalization group for impurity problems

Algorithms to find ground states, including phase estimation, variational methods, imaginary time evolution, and quantum annealing

Density matrix renormalization group, including timedependent and imaginarytime algorithms, and tangent space methods

Pairentangled projected states (PEPS), tensor network renormalization (TNR), 2D canonical forms, isometric PEPS, and fermionic PEPS.
Prerequisites: PH 125, CH 125 or equivalent graduate quantum mechanics course. ACM 104 or equivalent linear algebra course.
Class notes
Spring 2019
APh 250/ME 201:Physics on nearterm quantum computers
Quantum computers with tens of physical qubits and high gate fidelities will become available in the next few years. This class will explore how this new type of computing device could be used to address research questions in physics. Specific topics to be covered include:

Fundamentals of quantum computing and key algorithms

Translating states and Hamiltonians to qubits and Pauli gates

Algorithms to find ground states, including phase estimation, variational methods, imaginary time evolution, and quantum annealing

Algorithms for quantum dynamics

Noise and error mitigation strategies on nearterm devices
Prerequisites: PH 125, CH 125 or equivalent graduate quantum mechanics course. ACM 104 or equivalent linear algebra course. Some familiarity with fundamental concepts of quantum computing is beneficial.
Class notes
Lecture 1: Quantum Simulation 04/01/2019
Lecture 2: Efficient Quantum Simulations 04/03/2019
Lecture 3: Overview of Computational Complexity 04/05/2019
Lecture 4: Review of Linear Algebra and Quantum Mechanics 04/08/2019
Lecture 5: Review of Quantum Computing 04/12/2019
Lecture 6: Second quantization 04/22/2019
Lecture 7: JordanWigner transform 04/26/2019
Lecture 8: BrayviKitaev transform 05/01/2019
Lecture 9: Iterative Phase Estimation 05/03/2019
Lecture 10: Variational Quantum Eigensolver 05/06/2019
Lecture 11: Variational imaginary time evolution 05/08/2019
Lecture 12: QITE, QLancozs, QMETTs 05/13/2019
Lecture 13: Inelastic neutron scattering on quantum hardware 05/20/2019
Lecture 14: Density matrix dynamics 05/22/2019
Lecture 15: Electronphonon coupling on quantum computer 5/29/2019
Lecture 16: Resources estimate 5/31/2019
Lecture 17: Error mitigation 5/31/2019
Lecture 18: Quantum annealing and adiabatic quantum computation BONUS
In class tutorial files: 04/24/2019
Homework 1: 04/05/2019
Homework 2: 04/12/2019
Homework 3: 04/19/2019
Homework 4: 05/01/2019
Github link to solution for qns 2
Homework 5: 05/20/2019