Syllabus

Exam: Fundamentals of Quantum Computing Using Qiskit v2.X Developer (ENU) Credential: IBM Certified Quantum Computation using Qiskit v2.X Developer — Associate Exam date (mine): August 4, 2026

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Exam Logistics

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Section Weights at a Glance

Sections 1–5 together are ~73% of the exam — prioritize accordingly.

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Section 1 — Create Quantum Circuits (18%)

1.1 Basic circuit construction

• QuantumCircuit(num_qubits, num_clbits) constructor and its variants
• QuantumRegister, ClassicalRegister, AncillaRegister; named registers
• Single-qubit gates: x, y, z, h, s, sdg, t, tdg, p, rx, ry, rz, u, sx, id
• Two-qubit gates: cx/cnot, cz, cy, ch, swap, iswap, cp, crx, cry, crz, rxx, ryy, rzz, ecr
• Multi-qubit gates: ccx (Toffoli), cswap (Fredkin), mcx
• measure(), measure_all(), measure_active(); barrier(), reset()
• Circuit composition: compose(), append(), tensor(); inverse(), power(), copy()
• Controlled/custom gates: .control(), .to_gate(), .to_instruction(), gate labels
• Circuit properties: depth(), size(), width(), num_qubits, count_ops(), qubits/clbits
• Building well-known circuits: Bell states, GHZ states, superposition prep

1.2 Parameterized circuits

• qiskit.circuit.Parameter — symbolic parameters in gates
• ParameterVector — vectors of parameters
• assign_parameters() (dict or list binding; inplace flag)
• circuit.parameters attribute (sorted order matters for list binding)
• Parameter expressions (arithmetic on parameters)
• Passing parameter values via PUBs at runtime instead of binding

1.3 Dynamic circuits (classical feedforward)

• Mid-circuit measurement
• with circuit.if_test((clbit/creg, value)): blocks and else branch
• switch, for_loop, while_loop control-flow constructs
• Use cases: teleportation, active reset, conditional gates

1.4 Transpilation and optimization

• Why transpile: basis gates, coupling map / connectivity, ISA circuits
• generate_preset_pass_manager(optimization_level, backend=...) — the v2.x standard
• Optimization levels 0–3 (what each adds: layout, routing, translation, optimization)
• Transpiler stages: init → layout → routing → translation → optimization → scheduling
• Layout and routing concepts: initial layout, SWAP insertion, VF2 layout
• transpile() function vs pass managers
• Target, backend.target, basis gate sets (e.g., ECR/CZ + rz, sx, x)
• Effect of transpilation on depth/gate counts; comparing before vs after

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Section 2 — Perform Quantum Operations (16%)

2.1 Pauli operators and observables

• qiskit.quantum_info.Pauli — construction from strings (e.g., Pauli('IZ')), phase conventions
• Qubit ordering: little-endian — rightmost character acts on qubit 0 (classic trap)
• SparsePauliOp — construction (from_list, from_sparse_list), coefficients, simplify()
• Composing/manipulating operators: compose(), tensor(), expand(), adjoint()
• .to_matrix() conversions; matrix forms of I, X, Y, Z, H, S, T
• Defining observables for the Estimator (Hamiltonians as Pauli sums)

2.2 Quantum states and operators (quantum_info)

• Statevector — from_label(), from_instruction(), evolving with evolve()
• DensityMatrix basics
• Operator class — building from circuits/matrices, unitary checks
• Inner products, fidelity (state_fidelity), probabilities(), probabilities_dict()
• Statevector.sample_counts(), expectation_value()

2.3 Applying quantum operations & gate effects

• What each gate does to basis states and Bloch-sphere states
• Global vs relative phase; effect of Z/S/T on superposition states
• Creating entanglement (H + CX); recognizing entangled vs product states
• Commutation basics (e.g., do X and Z commute), gate identities (HZH = X, HXH = Z)
• Rotation gates as exp(-iθP/2); special angles (rz(π) ≡ Z up to phase)

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Section 3 — Run Quantum Circuits (15%)

3.1 Qiskit Runtime service and backends

• QiskitRuntimeService() — account setup, channels, saving credentials
• service.backend(...), service.least_busy(operational=True, simulator=False)
• Backend properties: num_qubits, basis_gates, coupling_map, target
• ISA circuits requirement: circuits must be transpiled to backend ISA before running on hardware (v2 primitives reject non-ISA circuits)

3.2 Execution modes

• Job mode — single primitive request, standalone queuing
• Batch mode — multiple independent jobs submitted together, parallel scheduling, no state between jobs
• Session mode — dedicated/exclusive access window; iterative workloads (e.g., variational algorithms); sequential jobs without re-queuing
• When to choose each mode; Session(backend=...), Batch(backend=...) context managers
• Passing mode= to a primitive (session, batch, or backend object)
• Session/batch lifecycle: max time, interactive timeout, closing

3.3 Running with primitives

• Instantiating SamplerV2(mode=...) / EstimatorV2(mode=...)
• job = primitive.run(pubs); job is asynchronous
• Local testing: StatevectorSampler / StatevectorEstimator (qiskit.primitives), Aer simulators, fake backends (FakeManilaV2 etc.)
• Shots: setting per-run and per-PUB; default shot behavior

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Section 4 — Use the Sampler Primitive (12%)

4.1 Concept

• Sampler returns per-shot measurement samples / bitstring distributions (not expectation values)
• Circuits must contain measurements for Sampler
• Sampler PUB shape: (circuit,) or (circuit, parameter_values) and optional shots: (circuit, param_values, shots)

4.2 Results

• result = job.result(); indexing PUB results: result[0]
• result[0].data.<creg_name> (e.g., .data.meas for measure_all(), .data.c for default register)
• BitArray: get_counts(), get_bitstrings(), num_shots, num_bits
• join_data() when multiple classical registers exist

4.3 Options (error suppression for Sampler)

• sampler.options.default_shots
• Dynamical decoupling: options.dynamical_decoupling.enable = True, sequence types (XX, XpXm, XY4)
• Twirling: options.twirling.enable_gates, enable_measure, num_randomizations, shots_per_randomization
• Note: Sampler has no resilience_level (that's Estimator-only)

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Section 5 — Use the Estimator Primitive (12%)

5.1 Concept

• Estimator returns expectation values ⟨ψ|O|ψ⟩ of observables
• Circuits must NOT have measurements (observable defines the measurement)
• Estimator PUB shape: (circuit, observables), (circuit, observables, parameter_values), optional precision
• Observables must be mapped to the transpiled circuit layout: observable.apply_layout(isa_circuit.layout)
• Broadcasting rules for arrays of observables/parameters

5.2 Results

• result[0].data.evs (expectation values) and .data.stds (standard errors)
• result[0].metadata (e.g., target precision, shots)

5.3 Options (error mitigation — Estimator)

• options.default_precision / default_shots
• Resilience levels: 0 (none), 1 (default — measurement error mitigation / TREX), 2 (adds ZNE)
• options.resilience.zne_mitigation = True; ZneOptions: noise_factors, extrapolator (linear, exponential, polynomial)
• Measurement mitigation: options.resilience.measure_mitigation
• PEC (probabilistic error cancellation) awareness — options.resilience.pec_mitigation
• Dynamical decoupling and twirling (TwirlingOptions) also apply to Estimator
• Error suppression (DD, twirling — before/during execution) vs error mitigation (TREX, ZNE, PEC — post-processing) distinction

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Section 6 — Visualize Circuits, Measurements, and States (11%)

6.1 Circuit visualization

• circuit.draw() — outputs: 'text', 'mpl', 'latex'; reverse_bits, fold, idle_wires args
• Reading circuit diagrams: gate order (left→right), qubit ordering (top = q0), controls vs targets
• Identifying the circuit that produces a given diagram (and vice versa)

6.2 Measurement/counts visualization

• plot_histogram(counts) — single and multiple datasets, legend, sort, number_to_keep
• plot_distribution() for quasi-probabilities
• Interpreting histograms: bitstring ordering (little-endian), identifying Bell/GHZ signatures

6.3 State visualization

• plot_bloch_vector() — a single Bloch vector; spherical/cartesian coordinates
• plot_bloch_multivector(statevector) — per-qubit Bloch spheres; entangled qubits show zero-length vectors (center of sphere)
• plot_state_qsphere() — amplitude = blob size, phase = color; reading relative phases
• plot_state_city(), plot_state_hinton(), plot_state_paulivec()
• Statevector.draw('latex') / array_to_latex
• Matching a state (e.g., |+⟩, |−⟩, |i⟩) to its Bloch sphere position: |0⟩=+Z, |1⟩=−Z, |±⟩=±X, |±i⟩=±Y

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Section 7 — Retrieve and Analyze Results (10%)

7.1 Job management

• job.job_id(), job.status(), job.done(), job.cancel()
• Asynchronous execution model; retrieving completed jobs later: service.job(job_id)
• service.jobs(...) filtering (backend, session, pending)
• job.metrics(), job.usage() — QPU time awareness

7.2 Result objects (V2)

• PrimitiveResult structure: iterable of PubResults
• PubResult.data (DataBin), PubResult.metadata, job-level result.metadata
• Sampler: counts/bitstrings from BitArray (see §4.2)
• Estimator: evs, stds, ensemble_standard_error (see §5.2)

7.3 Analysis

• Converting counts → probabilities; normalizing over shots
• Comparing ideal (simulator) vs noisy (hardware) distributions
• Computing expectation values from counts manually (e.g., ⟨Z⟩ = (n₀ − n₁)/shots)
• Interpreting standard errors and precision vs shots relationship (error ∝ 1/√shots)

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Section 8 — Operate with OpenQASM (6%)

8.1 OpenQASM 3

• Exporting: qiskit.qasm3.dumps(circuit) (string), qiskit.qasm3.dump(circuit, file)
• Importing: qiskit.qasm3.loads(program_str), qiskit.qasm3.load(file)
• OpenQASM 3 program structure: OPENQASM 3.0;, include "stdgates.inc";, qubit[n], bit[n], gate syntax
• OpenQASM 3 features vs 2: classical control flow, input parameters, typed classical data

8.2 OpenQASM 2 (interoperability)

• QuantumCircuit.from_qasm_str() / qiskit.qasm2.loads(), qasm2.dumps()
• Reading a short QASM program and predicting the circuit it builds

8.3 REST API awareness

• Submitting Runtime jobs with QASM payloads via the IBM Quantum REST API (high-level awareness)

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Cross-Cutting Concepts (show up everywhere)

• Little-endian convention — qubit 0 is rightmost in bitstrings, statevector indices, and Pauli strings
• V2 primitives only — SamplerV2/EstimatorV2, PUBs; V1 (backend.run, Sampler/Estimator V1) is deprecated/removed
• Qiskit 1.0/2.0 packaging changes: qiskit_ibm_runtime vs qiskit; removal of qiskit.execute; Aer moved to qiskit_aer
• The Qiskit patterns workflow: Map → Optimize (transpile) → Execute (primitives) → Post-process
• Common exam traps: forgetting apply_layout on observables, running non-ISA circuits, Sampler without measurements, Estimator with measurements, parameter binding order

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Prerequisites Checklist

• Python: comfortable with classes, context managers (with), NumPy arrays
• Linear algebra: matrix multiplication, tensor products, eigenvalues, unitary/Hermitian matrices
• Complex numbers: modulus, phase, Euler's formula
• Dirac notation: |0⟩, |1⟩, |+⟩, |−⟩, bras/kets, inner/outer products
• Core QC concepts: superposition, entanglement, measurement/collapse, no-cloning

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Official & Recommended Resources

1. Official exam page + Sample Test — IBM Training: C9008400 certification (work the sample test until ≥90%)
2. IBM Quantum Documentation — docs.quantum.ibm.com: primitives, execution modes, error mitigation, transpilation guides
3. IBM Quantum Learning platform — learning.quantum.ibm.com: "Basics of Quantum Information" (John Watrous), "Quantum Computing in Practice"
4. Qiskit YouTube channel — "Preparing for the Qiskit Developer Certification 2.0" series
5. Hands-on: local Qiskit install + qiskit-ibm-runtime + qiskit-aer; experiment with every API in this syllabus
6. Community: SchrodinTeq sample-test walkthroughs, Udemy practice exams, Quantum Computing Stack Exchange

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Sources: IBM Training certification page; exam-guide writeups corroborated across SchrodinTeq, ppalme's review, CertificationBox, and ValidExam objective listings. Verified 2026-07-24 against the official IBM Study Guide (resource-artifactory/official/C1000-179_STU_StudyGuideQiskitv2.pdf) — all 8 sections and weights match exactly (official section order: 1 operations, 2 visualize, 3 create, 4 run, 5 sampler, 6 estimator, 7 retrieve, 8 OpenQASM).