Skip to content

AWS Quantum Practitioner - Fact Sheet (Anticipated)

Important Notice

This certification is anticipated based on AWS direction with Amazon Braket. As of this writing, AWS has not formally announced an exam with the QPC-C01 code, format, or pricing. The fields below represent realistic estimates derived from adjacent AWS specialty exams (AIF, MLA, MLS, DAS, ANS) and public Braket capabilities. Verify all details against AWS Training and Certification when the exam is officially released.

Anticipated Exam Identity

Attribute Anticipated value
Certification body AWS Training and Certification
Anticipated exam code QPC-C01
Level Specialty
Delivery Pearson VUE testing center or online proctored via Pearson OnVUE
Anticipated duration 170 minutes
Anticipated questions 65 multiple choice and multi-response
Anticipated passing score 750 / 1000 (scaled)
Anticipated cost 300 USD
Validity 3 years (standard AWS)
Languages English at launch; localized later

Anticipated Domain Blueprint

Domain Anticipated weight
1. Quantum Computing Fundamentals 20%
2. Amazon Braket Service 25%
3. Quantum Circuits and Gates 15%
4. Hybrid Quantum-Classical Workflows 15%
5. Quantum Algorithms 15%
6. Cost and Operational Considerations 10%

Key Concepts by Domain

Domain 1: Fundamentals

  • Qubit, superposition, entanglement, measurement, decoherence
  • Bloch sphere visualization
  • State vector representation; Dirac notation (|0>, |1>, |+>, |->)
  • Pauli matrices (X, Y, Z), Hadamard (H), phase (S, T)
  • Unitary evolution and reversibility
  • No-cloning theorem
  • Bell states and their preparation

Domain 2: Amazon Braket

  • Braket service architecture: notebooks, hybrid jobs, tasks, devices
  • SDK (Python amazon-braket-sdk)
  • Supported hardware: IonQ (Aria, Forte), Rigetti (Ankaa), IQM (Garnet), QuEra (Aquila, neutral atom)
  • Simulators: SV1 (state vector), DM1 (density matrix), TN1 (tensor network), local simulators
  • IAM permissions: braket:CreateQuantumTask, GetQuantumTask, etc.
  • S3 result storage
  • CloudWatch metrics, CloudTrail audit
  • Braket Pulse for low-level pulse control
  • Braket Direct for reserved capacity

Domain 3: Circuits and Gates

  • Single-qubit gates: I, X, Y, Z, H, S, T, Rx, Ry, Rz
  • Multi-qubit gates: CNOT, CZ, SWAP, CCNOT (Toffoli), CSWAP (Fredkin)
  • Universal gate sets
  • Circuit depth and width
  • Native gate sets per QPU (varies by hardware)
  • Circuit transpilation and optimization
  • Compilation strategies

Domain 4: Hybrid Workflows

  • Braket Hybrid Jobs: container-based hybrid execution
  • Variational algorithms requiring classical optimizer + quantum circuit
  • Priority queueing for hybrid jobs
  • BYO container or managed PennyLane container
  • Integration with Amazon SageMaker for ML
  • Result aggregation and post-processing

Domain 5: Algorithms

  • Grover's algorithm (search; quadratic speedup)
  • Shor's algorithm (factoring; conceptual at exam scale)
  • Quantum Phase Estimation (QPE)
  • Variational Quantum Eigensolver (VQE)
  • Quantum Approximate Optimization Algorithm (QAOA)
  • Quantum Machine Learning basics
  • Annealing (relevant for some hardware)
  • Quantum Fourier Transform (QFT)

Domain 6: Cost and Operations

  • Per-task pricing + per-shot pricing model
  • Device-specific pricing (varies significantly)
  • Reservation model (Braket Direct)
  • Hybrid Job pricing (compute + quantum)
  • Simulator pricing per minute
  • Cost monitoring with AWS Cost Explorer and tagging
  • Error mitigation strategies and cost trade-off
  • Choosing simulator vs QPU for development phase

Hardware on Amazon Braket (verify current list)

Provider Modality Devices (representative)
IonQ Trapped ion Aria, Forte
Rigetti Superconducting Ankaa
IQM Superconducting Garnet
QuEra Neutral atom (analog Hamiltonian) Aquila

Hardware comes and goes from Braket; always check device.is_available and the AWS console for current devices and queue depths.

Simulators on Amazon Braket

Simulator Type Use
SV1 State vector General purpose, up to ~34 qubits
DM1 Density matrix Simulating noise, smaller qubit count
TN1 Tensor network Larger circuits with limited entanglement
LocalSimulator In-process Free for development; limited size

Official Resources

  • Amazon Braket: https://aws.amazon.com/braket/
  • Braket docs: https://docs.aws.amazon.com/braket/
  • Braket SDK: https://github.com/amazon-braket/amazon-braket-sdk-python
  • Braket Tutorials: https://github.com/amazon-braket/amazon-braket-examples
  • AWS Quantum blog: https://aws.amazon.com/blogs/quantum-computing/
  • PennyLane integration: https://docs.pennylane.ai/projects/braket/
  • IonQ docs: https://ionq.com/docs
  • Rigetti docs: https://docs.rigetti.com/
  • AWS Center for Quantum Computing
  • Book: Quantum Computing: An Applied Approach (Hidary)
  • Book: Programming Quantum Computers (Johnston, Harrigan, Gimeno-Segovia)
  • Book: Quantum Computation and Quantum Information (Nielsen and Chuang) for depth
  • AWS Skill Builder Braket courses
  • Braket Examples GitHub repo (work through every notebook)
  • Qiskit Textbook (provider-agnostic learning resource)