Integrating computation through first- and second-year physics major units

COMPUTATION AS A TARGET FOR PHYSICS INSTRUCTION Computation is increasingly recognized as a core aspect of physics practice and target for physics instruction. Literature on computation in physics often focuses on separate computational physics units or courses (see Atherton, 2023, for a review) rather than integrating computation throughout curricula. We document the process of integrating computation in the context of first- and second-year physics units at Monash University to provide a model for embedding computation throughout a three-year Australian Bachelor of Science in physics. GOALS FOR COMPUTATIONAL PHYSICS INSTRUCTION AT MONASH Naturally, computational physics instruction aims to develop a solid foundation of the coding skills that students will require in their careers and to develop literacy with a chosen language. However, we set out more importantly and broadly, to develop the skills needed to break down a task into its separate steps and develop an algorithm (free of syntax) and to develop student’s ability to create interactive visualisations and models, in order to augment their understanding of various physical phenomena. This approach enhances engagement by showcasing the benefits of computational approaches and augments learning through linking interesting phenomena and the coding process. IMPLEMENTATION OF COMPUTATIONAL ACTIVITIES IN APPLIED AND LABORATORY ACTIVITIES Our vertical integration of computational skills begins in first year, where primarily we focus on developing coding skills in support of laboratory data analysis. Here, the activities begin with a basic introduction to coding in the chosen language (originally the Wolfram Language in Mathematica, and currently Python) tailored to lab analysis (plotting, fitting, error analysis). Subsequent tasks build upon this, supported by a mix of examples and exercises that ask students to follow along, modify previously used code, or fill in gaps in templates for each laboratory they complete. This approach is followed in second year with the applications broadened tremendously into fortnightly sessions that serve to create fully interactive demonstrations/visualisations of the physics discussed in other aspects of the unit. These include creating time evolutions of wavefunctions for the common quantum tunnelling problems, visualizations of random walks, manipulable ray traces, and much more. Through this, students not only learn a variety of coding techniques and principles but see very interesting and often hard to imagine aspects of physics come to life through their code! EVALUATING THE SUCCESS OF COMPUTATIONAL INSTRUCTION We assess our success in achieving both computational and physics learning outcomes, as well as enhance engagement, based on feedback provided annually by students in their evaluations of teaching units, on its follow through in higher year level units, and on the difference in achievement in the unit as a whole for years/students where computational applied sessions have been attended, in comparison to those cases where applied sessions were not offered or not attended. REFERENCE Atherton, T. J. (2023). Resource Letter CP-3: Computational physics. American Journal of Physics, 91(1), 7-27

Groupoid Semantics for Thermal Computing

A groupoid semantics is presented for systems with both logical and thermal degrees of freedom. We apply this to a syntactic model for encryption, and obtain an algebraic characterization of the heat produced by the encryption function, as predicted by Landauer's principle. Our model has a linear representation theory that reveals an underlying quantum semantics, giving for the first time a functorial classical model for quantum teleportation and other quantum phenomena.Comment: We describe a groupoid model for thermodynamic computation, and a quantization procedure that turns encrypted communication into quantum teleportation. Everything is done using higher category theor

Simulating chemistry using quantum computers

The difficulty of simulating quantum systems, well-known to quantum chemists, prompted the idea of quantum computation. One can avoid the steep scaling associated with the exact simulation of increasingly large quantum systems on conventional computers, by mapping the quantum system to another, more controllable one. In this review, we discuss to what extent the ideas in quantum computation, now a well-established field, have been applied to chemical problems. We describe algorithms that achieve significant advantages for the electronic-structure problem, the simulation of chemical dynamics, protein folding, and other tasks. Although theory is still ahead of experiment, we outline recent advances that have led to the first chemical calculations on small quantum information processors.Comment: 27 pages. Submitted to Ann. Rev. Phys. Che

Simulating quantum field theory with a quantum computer

Forthcoming exascale digital computers will further advance our knowledge of quantum chromodynamics, but formidable challenges will remain. In particular, Euclidean Monte Carlo methods are not well suited for studying real-time evolution in hadronic collisions, or the properties of hadronic matter at nonzero temperature and chemical potential. Digital computers may never be able to achieve accurate simulations of such phenomena in QCD and other strongly-coupled field theories; quantum computers will do so eventually, though I'm not sure when. Progress toward quantum simulation of quantum field theory will require the collaborative efforts of quantumists and field theorists, and though the physics payoff may still be far away, it's worthwhile to get started now. Today's research can hasten the arrival of a new era in which quantum simulation fuels rapid progress in fundamental physics.Comment: 22 pages, The 36th Annual International Symposium on Lattice Field Theory - LATTICE201

Is Fault-Tolerant Quantum Computation Really Possible?

The so-called "threshold" theorem says that, once the error rate per qubit per gate is below a certain value, indefinitely long quantum computation becomes feasible, even if all of the qubits involved are subject to relaxation processes, and all the manipulations with qubits are not exact. The purpose of this article, intended for physicists, is to outline the ideas of quantum error correction and to take a look at the proposed technical instruction for fault-tolerant quantum computation. It seems that the mathematics behind the threshold theorem is somewhat detached from the physical reality, and that some ideal elements are always present in the construction. This raises serious doubts about the possibility of large scale quantum computations, even as a matter of principle.Comment: Based on a talk given at the Future Trends in Microelectronics workshop, Crete, June 2006. 8 pages, 1 figur