Spring 2027

Qubit Tune-up and Programming

Explores quantum computer development on hardware like superconducting qubits, covering qubit tune-up, DiVincenzo criteria, gate implementation, and QCVV techniques for error mitigation.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2025: 3.64 GPA, 85.7% A/AB (n=35 letter grades).

What you’ll be able to do

  • Describe the physics underlying a specific qubit hardware.

  • Describe and implement qubit state preparation and measurement (SPAM).

  • Characterize and implement single and two-qubit gates.

  • Define and implement quantum characterization, verification, and validation (QCVV) techniques to diagnose errors in quantum hardware.

  • Utilize data analysis techniques necessary for developing quantum computers.

Source: UW Guide

Grade history

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average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
PHYSICS 763

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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Student experience

the class

Recent recorded grades — Fall 2025: 3.64 GPA, 85.7% A/AB (n=35 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Superconducting qubit hardware
  • Qubit tune-up and DiVincenzo criteria
  • State preparation, measurement, and quantum gates
  • QCVV techniques and error mitigation
  • Scalable and reliable quantum systems

Skills

  • Describing physics of qubit hardware
  • Implementing qubit SPAM
  • Characterizing and implementing quantum gates
  • Applying QCVV techniques for error diagnosis
  • Using data analysis for quantum computing

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F
More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1316 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
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Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "abe052233690792851c843c3da1c8a36b36e685356f4790f55d7389018aa12fc",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}