This course provides engineers, scientists, and technical managers with a foundational understanding of Quantum Processing Units (QPUs).
It explores QPU architectures, qubit technologies, quantum gates, operational principles, programming models, and future trends in quantum processors.
Through lectures, hands-on exercises, and case studies, participants will gain the essential knowledge to work in quantum computing systems and prepare for further specialization.
Learning Objectives:
By the end of the course, participants will be able to:
- Describe the fundamental structure and operation of a QPU.
- Differentiate between various physical implementations of qubits.
- Understand how QPUs execute quantum algorithms via gate sequences.
- Analyze the challenges related to QPU error correction, scaling, and decoherence.
- Interact with QPUs via cloud platforms (e.g., IBM Quantum, AWS Braket).
- Explore trends and future architectures in scalable quantum processing.
Target Audience:
- Engineers (Hardware, Software, Systems)
- Computer Scientists
- Physicists and Applied Mathematicians
- Technical Managers and CTOs
- Researchers entering the field of quantum computing
Prerequisites:
- Basic understanding of linear algebra (matrices, vectors)
- Familiarity with classical computer architecture
- No prior quantum computing experience required (quantum basics will be introduced)
Day 1 Agenda:
Module 1: Introduction to Quantum Computing and QPUs
- What is a QPU? Why does it matter?
- Classical vs Quantum Processing
- Concept of Qubits: Superposition, Entanglement, Measurement
- Overview of major QPU providers (IBM, IonQ, Rigetti, etc.)
Exercise 1: Qubit simulation — Visualize superposition and entanglement using a quantum circuit simulator.
Module 2: Anatomy of a QPU
- Qubits and their physical implementations:
- Superconducting Qubits
- Trapped Ions
- Neutral Atoms
- Photonic Qubits
- Emerging Topological Qubits
- Gate sets and instruction sets (e.g., Clifford+T, Universal Gate Sets)
Lab 1: Build basic single- and two-qubit circuits using Qiskit or Cirq.
Module 3: QPU Hardware Architecture
- Qubit Control: Pulse-level programming basics
- Cryogenic systems and dilution refrigerators
- Readout and Measurement techniques
- Error sources: Bit-flip, Phase-flip, Depolarizing noise
Discussion: Why maintaining coherence is the hardest part of quantum computing.
Day 2 Agenda:
Module 4: How a QPU Runs a Quantum Program
- Quantum Circuits → Hardware Instructions
- Compilation and Optimization
- Mapping and Routing (qubit connectivity constraints)
- Example: Running a Grover’s Algorithm on a QPU
Lab 2: Write and execute a simple quantum program on a real cloud-accessed QPU.
Module 5: Error Correction and Fault Tolerance
- Introduction to Quantum Error Correction (QEC)
- Logical qubits vs physical qubits
- Surface codes and other QEC strategies
- Threshold theorem and implications for large-scale QPUs
Exercise 2: Explore logical qubits vs physical qubits overhead with an interactive calculator.
Module 6: Current Limitations and Future Trends
- Near-term devices: NISQ (Noisy Intermediate-Scale Quantum)
- Roadmaps to million-qubit QPUs
- Hybrid Quantum-Classical computing (CPU-GPU-QPU orchestration)
- Companies and Research Trends (Google, IBM, Xanadu, QuEra)
Workshop: Design a conceptual QPU architecture for a hypothetical startup focused on quantum machine learning.
Final Deliverables:
- Certificate of Completion (optional)
- Access to quantum programming environment after the course
- Printed workbook and online resource pack
- Optional knowledge check (20 multiple-choice questions)
Materials Provided:
- Participant Guide (PDF)
- Full Slide Deck (PDF)
- Access to Qiskit, Braket, or Cirq demo environments
- Cheat Sheets (Quantum gates, QPU technologies comparison)
- Case studies: Real-world QPU applications (e.g., chemistry simulation, optimization)