INTRODUCTION TO QUANTUM COMPUTING

PHYSICS 779

Explores applications of quantum theory to both the hardware and the software that underpin modern quantum information technology. Advanced quantum circuit theory: Clifford group and Gottesman-Knill theorem, Mathematica code. Decoherence: density matrices, probability distributions, T1 and T2. Advanced error correction: master equation, Kraus operators, fault tolerance, quantum tomography. Hardware: Trapped ions, Paul traps, sideband cooling, CZ and MS gates, neutral atoms, superconductors, quantum dots.

PHYSICS 765

Dive into the quantum computing stack by starting with real-world applications and progressing through the essential components needed for fault-tolerant quantum computers. Learn about key areas such as quantum algorithms, and advanced quantum error correction. Explore algorithms for scientific applications, including quantum phase estimation and the HHL algorithm, and examine the derivation, trade-offs, and implementation of sophisticated error correction techniques. Learn to analyze and estimate the full-stack quantum resources required for novel quantum algorithms by utilizing classical computational tools, in order to develop and evaluate scalable, high-utility quantum computing solutions.

STAT 780

Quantum computation issues, including probability, statistics, sensing, information, machine learning, and applying data science to quantum information science.

PHYSICS 707

Provides an intensive introduction to the experimental techniques of quantum computing. Students will do 8 experiments chosen from: Bell violation with entangled photons, Stern-Gerlach, Pulsed NMR, Optical pumping of Rb, Nanofabrication, Fiber optics communication, Diode pumped YAG laser, and Acousto-optic modulator.

PHYSICS 763

Explore the development of quantum computers using specific hardware platforms, such as superconducting qubits. Walk through the entire process from a fabricated device to a functional quantum computer, focusing on qubit tune-up and the DiVincenzo criteria. Implement state preparation and measurement, single and two-qubit gates, and apply quantum characterization, verification, and validation (QCVV) techniques to diagnose and mitigate errors. Utilize quantum computer simulators and real quantum devices when available, leveraging current experimental data analysis methods to understand how to build scalable and reliable quantum systems. Knowledge of quantum mechanics [such asPHYSICS 531] required.