Sonny - Physics tutor - Austin
1st lesson free
Sonny - Physics tutor - Austin

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Sonny

  • Rate PKR 2,776
  • Response 4h
  • Students

    Number of students Sonny has accompanied since arriving at Superprof

    4

    Number of students Sonny has accompanied since arriving at Superprof

Sonny - Physics tutor - Austin
  • 5 (2 reviews)

PKR 2,776/hr

1st lesson free

Contact

1st lesson free

1st lesson free

  • Physics

Quantum Reality - Full Stack AI enablement. GPU + Qubit + DWAVE -/+ IonQ π= Quantum Actuality

  • Physics

Lesson location

Recommended

Sonny is a respected member of our tutor community. He is highly recommended for his commitment and the quality of his lessons. An excellent choice to progress with confidence.

About Sonny

Target Audience and Prerequisites
Audience:
Software engineers, data scientists, researchers, or students exploring quantum advantage in hybrid settings.
Prerequisites: Basic linear algebra, probability, Python programming. Familiarity with classical ML or HPC is helpful but not required.
No prior quantum experience needed—foundational modules build it.


Course Duration and Format
Length:
5 Student Minimum
2 one hour lectures with 1 hour Q&A weekly
12 weeks (or self-paced equivalent), with 4-6 hours/week.
- Format: Lectures + hands-on labs (using simulators and real cloud access via IonQ, Rigetti, or open platforms), assignments, and a capstone hybrid project.

-Tools/Platforms: Qiskit, Cirq, PennyLane (for hybrid differentiable programming), pyQuil/Forest (Rigetti), Ocean (D-Wave), Azure Quantum or Amazon Braket for hybrid sessions, classical HPC elements (e.g., GPU integration).

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About the lesson

  • Primary
  • Middle School
  • SSC
  • +6
  • levels :

    Primary

    Middle School

    SSC

    HSSC

    Adult Education

    Master

    Doctoral

    MBA

    Pre-Primary

  • English

All languages in which the lesson is available :

English

Course Objectives

By the end of the course, participants will be able to:

- Explain core quantum principles (superposition, entanglement, measurement) and their limitations in real hardware.

- Design, simulate, and execute hybrid quantum-classical algorithms (e.g., variational quantum algorithms, quantum machine learning).

- Navigate the full quantum-classical stack: hardware control, compilers, runtime, middleware, and application layers.

- Integrate quantum backends (cloud or simulators) with classical frameworks like PyTorch/TensorFlow for end-to-end workflows.

- Evaluate trade-offs in hybrid models for applications in optimization, chemistry, ML, and materials science.

- Discuss scalability challenges, error correction, and future fault-tolerant integration.

- Quantum control engineering

- Full Stack orchestration using Qiskit and Cirq frameworks


Quantum-Classical Entanglement:
Leveraging Full-Stack Hybrid Models".
This course bridges quantum and classical computing, focusing on hybrid systems where quantum processors (leveraging superposition, entanglement, and interference) collaborate tightly with classical hardware and software.
It emphasizes practical "full-stack" integration—from hardware to high-level applications—drawing on platforms from companies like IonQ (trapped-ion, first publicly traded pure-play quantum firm), Rigetti (superconducting), and D-Wave (annealing with hybrid solvers), alongside broader ecosystems like IBM Qiskit, PennyLane, and HPC-QC frameworks.

The course targets intermediate learners (e.g., computer science, physics, or engineering backgrounds) interested in NISQ-era (Noisy Intermediate-Scale Quantum) realities and near-term hybrid workflows.
It highlights how classical systems handle orchestration, error mitigation, optimization loops, and data preprocessing, while quantum components accelerate intractable subproblems via entanglement-enabled parallelism.

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Rates

Rate

  • PKR 2,776

Pack prices

  • 5h: PKR 10
  • 10h: PKR 20

free lessons

The first free lesson with Sonny will allow you to get to know each other and clearly specify your needs for your next lessons.

  • 1hr

online

  • PKR 2,776/h

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