Outline

Overview

Quantum statistical physics has revolutionized the world we live in- providing a profound understanding of the microscopic world and driving the technological revolution of the last few decades. Modern physics increasingly relies on solving equations using computational techniques, for modelling anything from the big bang to quantum dot lasers. Building on 2000-level physics, this unit will develop the full formalism for deriving properties of individual atoms and large collections of atoms, and introduce advanced numerical techniques. You will start from Schroedinger's equation and derive the full properties of hydrogen atoms, and systems of particles. You will study perturbation techniques qualitatively, including for the interaction of radiation with atoms. You will study the theoretical foundation of statistical mechanics, including both classical and quantum distributions. You will apply a variety of numerical schemes for solving ordinary and partial differential equations, learn about the suitability of particular methods to particular problems, and their accuracy and stability. The module includes computational lab sessions, in which you will actively solve a range of physics problems. In completing this unit you will gain understanding of the foundations of modern physics and develop skills that will enable you to numerically solve complex problems in physics and beyond.

Unit details and rules

Academic unit	Physics Academic Operations
Credit points	6
Prerequisites ?	(PHYS2011 or PHYS2911 or PHYS2921) and (PHYS2012 or PHYS2912 or PHYS2922)
Corequisites ?	None
Prohibitions ?	PHYS3934 or PHYS3039 or PHYS3939 or PHYS3042 or PHYS3942 or PHYS3043 or PHYS3943 or PHYS3044 or PHYS3944 or PHYS3090 or PHYS3990 or PHYS3991 or PHYS3999 or PHYS3099
Assumed knowledge ?	(MATH2021 or MATH2921 or MATH2061 or MATH2961 or MATH2067)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Sergio Leon-Saval, sergio.leon-saval@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Closed book, 2-hour, time-limited exam.	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Computational Physics Computer Labs Weeks 1,2,3,4,5; see Canvas for details.	10%	Multiple weeks	2 hours	AI allowed
Outcomes assessed:
Out-of-class quiz	Quantum physics quiz Canvas quiz	5%	Week 03 Due date: 12 Mar 2026 at 23:59 Closing date: 22 Mar 2026	20 minutes	AI allowed
Outcomes assessed:
Written work	Computational physics assignment Written assignment	10%	Week 06 Due date: 02 Apr 2026 at 23:59 Closing date: 12 Apr 2026	~ 5 pages	AI allowed
Outcomes assessed:
Written work	Quantum physics assignment Written assignment	10%	Week 07 Due date: 17 Apr 2026 at 23:59 Closing date: 27 Apr 2026	~ 5 pages	AI allowed
Outcomes assessed:
Out-of-class quiz	Statistical mechanics quiz Canvas quiz	5%	Week 12 Due date: 19 May 2026 at 23:59 Closing date: 29 May 2026	20 minutes	AI allowed
Outcomes assessed:
Written work	Statistical physics assignment Written assignment	10%	Week 13 Due date: 27 May 2026 at 23:59 Closing date: 06 Jun 2026	~ 5 pages	AI allowed
Outcomes assessed:
= early feedback task ?

Assessment summary

Assignments: There are three assignments in this unit, one per module. You are encouraged to start early on the problem assignment as you will be able to solve aspects from it as early as it is released. Assignments are to be submitted through Canvas. Typewritten and handwritten assignments are acceptable – for handwritten assignments, make sure the scans have good resolution and contrast and are not blurry or distorted. Only the parts of the assignment that can readily be read on screen will be marked. Plan plenty of time for uploading your files ahead of the deadline to make room for connectivity issues, as deadlines will be enforced strictly. It is your responsibility to ensure that files are uploaded correctly with all pages.
Quizzes: There will be one statistical mechanics quiz and one quantum physics quiz. All quizzes will be held online via Canvas and last 20 minutes each. You will have to do each quiz within a roughly 24h window on the due date.
Computer Physics Practice Quizzes: Optional multiple choice quizzes are available under Canvas. These quizzes test your understanding of the lecture material and can be completed at any time. The quizzes are formative (weight is 0%).
Computational Physics Computer Labs: The laboratory sessions consist of sets of exercises requiring you to modify the codes introduced in the lectures (available via the Canvas site), and to write your own codes. The tasks involve implementing numerical methods and solving science problems. The laboratory sessions support the lecture material, and are a crucial part of the unit. The lab question sheets are available online as a Canvas quiz, and you may complete the lab at any time each week before the due date. Tutors will be available during the scheduled computer labs to assist you.
Final exam: The final exam will be a supervised, closed book exam on just the Statistical Mechanics and Quantum Physics sections of the course.
Final exam: If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	A student demonstrates a flair for the subject and comprehensive knowledge and understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply subject knowledge to novel situations.
Distinction	75 - 84	A student demonstrates an aptitude for the subject and a solid knowledge and understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply the key ideas of the subject.
Credit	65 - 74	a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad understanding of the unit material but has not fully developed the ability to apply the key ideas of the subject.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge of the subject.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Quantum Physics Lectures 1,2: Basic Postulates; Position Eigenbasis. Wavefunction.	Lecture (2 hr)
	Computational Physics Lecture 1: Numerical error and Euler's method applied to projectile motion	Lecture (1 hr)
	Computational Physics Computer Lab 1: Numerical error and Euler's method applied to projectile motion	Practical (2 hr)
Week 02	Quantum Physics Lectures 3,4: Particle in a 1D infinite well potential; Finite well potential & Harmonic oscillator.	Lecture (2 hr)
	Quantum Physics Tutorial 1	Tutorial (1 hr)
	Computational Physics Lecture 2: Verlet method applied to the Kepler problem	Lecture (1 hr)
	Computational Physics Computer Lab 2: Verlet method applied to the Kepler problem	Practical (2 hr)
Week 03	Quantum Physics Lectures 5,6: Quantum Harmonic Oscillator (cont.); Quantum Physics of Central Potentials.	Lecture (2 hr)
	Quantum Physics Tutorial 2	Tutorial (1 hr)
	Computational Physics Lecture 3: Runge-Kutta methods applied to the simple pendulum	Lecture (1 hr)
	Computational Physics Computer Lab 3: Runge-Kutta methods applied to the simple pendulum	Practical (2 hr)
Week 04	Quantum Physics Lectures 7,8: Orbital Angular Momentum Operator; Spin and Total Angular momentum operators.	Lecture (2 hr)
	Quantum Physics Tutorial 3	Tutorial (1 hr)
	Computational Physics Lecture 4: Applying Forward Time, Centered Space (FTCS) discretisation to heat diffusion and matrix stability analysis - Part 1	Lecture (1 hr)
	Computational Physics Computer Lab 4: Applying Forward Time, Centered Space (FTCS) discretisation to heat diffusion and matrix stability analysis	Practical (2 hr)
Week 05	Quantum Physics Lectures 9,10: Vibrational and Rotational spectra of molecules; Molecular transition selection rules; Angular Momentum Eigenfunctions.	Lecture (2 hr)
	Quantum Physics Tutorial 4	Tutorial (1 hr)
	Computational Physics Lecture 5: Applying Forward Time, Centered Space (FTCS) discretisation to heat diffusion and matrix stability analysis - Part 2	Lecture (1 hr)
	Computational Physics Computer Lab 5: Lax method for advection	Practical (2 hr)
Week 06	Quantum Physics Lectures 11,12,13: Hydrogen Atom; Hydrogen Atom (cont.); Perturbation Theory.	Lecture (3 hr)
Week 06	Quantum Physics Tutorial 5	Tutorial (1 hr)
Week 07	Quantum Physics Lectures 14,15,16: Fine Structure: Spin-orbit interactions; The Lamb shift. Hyperfine Structure; Spontaneous emission rate and time-dependent perturbation theory; Atoms in External Magnetic Field.	Lecture (3 hr)
Week 07	Quantum Physics Tutorial 6	Tutorial (1 hr)
Week 08	Quantum Physics Lectures 17,18,19: Identical Particles; Helium Atom; Multi-electron atoms. Bose-Einstein condensate.	Lecture (3 hr)
Week 08	Quantum Physics Tutorial 7	Tutorial (1 hr)
Week 09	Statistical Physics Lectures 1-3: Thermodynamics recap, Einstein solid, Boltzmann distribution	Lecture (3 hr)
Week 09	Quantum Physics Tutorial 8	Tutorial (1 hr)
Week 10	Statistical Physics Lectures 4-6: Partition function, Maxwell-Boltzmann distribution, Quantum statistics - bosons and fermions; Blackbody radiation	Lecture (3 hr)
Week 10	Statistical Physics Tutorial 1: Classical statistical mechanics	Tutorial (1 hr)
Week 11	Statistical Physics Lectures 7-9: Cosmic microwave background, Black holes; Degenerate Fermi gases, White dwarfs and neutron stars	Lecture (3 hr)
Week 11	Statistical Physics Tutorial 2: Quantum statistical mechanics	Tutorial (1 hr)
Week 12	Statistical Physics Lecture 10: Density of states, Debye theory, Bose-Einstein condensates	Lecture (1 hr)
Week 12	Statistical Physics Tutorial 3: Statistics and astrophysics: neutron stars and black holes	Tutorial (1 hr)
Week 13	Statistical Physics Tutorial 4: A beautiful demonstration: Ising Model	Tutorial (1 hr)

Attendance and class requirements

Lectures, tutorials and computer labs are held in an-person format on-campus. Tutors will be available to assist students during the scheduled computer lab hours (2 hours weekly).

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library eReserve link available in the Canvas site for this unit.

McIntyre, D.H., Minogue, C.A., Tate, J., "Quantum Mechanics," Pearson
Schroeder, Daniel V., “An introduction to thermal physics,” Addison Wesley
Garcia, A., "Numerical methods for physics" (any edition)
Press, W.H. et al. "Numerical Recipes" (any edition)

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. demonstrate an understanding of key concepts in two foundation areas of physics – quantum mechanics of atoms and statistical physics
LO2. apply these concepts to develop models, and to solve qualitative and quantitative problems in scientific contexts, using appropriate mathematical and computing techniques as necessary – Compare and critique different models in quantum and statistical physics
LO3. design computer programs to solve physical problems
LO4. compare and critique different approaches to numerically solving physical problems
LO5. communicate scientific information appropriately, through written work
LO6. analyse a physical problem in quantum physics and statistical physics and develop a formalism appropriate for solving it
LO7. demonstrate a sense of responsibility, ethical behaviour, and independence as a learner and as a scientist.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No major changes since last iteration.

Additional information

The School of Physics recognises that biases and discrimination, including but not limited to those based on gender, race, sexual orientation, gender identity, religion and age, continue to impact parts of our community disproportionately. Consequently, the School is strongly committed to taking effective steps to make our environment supportive and inclusive and one that provides equity of access and opportunity for everyone.

The School has three Equity Officers as a point of contact for students and staff who may have a query or concern about any issues relating to equity, access and diversity. If you feel you have been treated unfairly, bullied, discriminated against or disadvantaged in any way, you are encouraged to talk to one of the Equity Officers or any member of the Physics staff.

More information can be found at https://sydney.edu.au/science/schools/school-of-physics/equity-access-diversity.html

Any student who feels they may need a special accommodation based on the impact of a disability should contact Disability Services:

http://sydney.edu.au/current_students/disability/ who can help arrange support.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

PHYS3034: Quantum, Statistical and Comp Physics

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links