--- name: quantum-computing-guide-8f78fd98cbfd description: "Explore quantum computing research with Qiskit and Cirq frameworks" metadata: openclaw: emoji: "⚛️" category: "domains" subcategory: "physics" keywords: ["quantum computing", "Qiskit", "Cirq", "quantum circuits", "qubit", "quantum algorithms"] source: "wentor-research-plugins" --- # Quantum Computing Guide A skill for conducting quantum computing research using Qiskit (IBM) and Cirq (Google) frameworks. Covers quantum circuit construction, fundamental algorithms, noise simulation, and practical considerations for running experiments on quantum hardware. ## Quantum Computing Fundamentals ### Key Concepts ``` Qubit: The basic unit of quantum information - Superposition: A qubit can be in a state |0>, |1>, or any linear combination alpha|0> + beta|1> where |alpha|^2 + |beta|^2 = 1 - Measurement: Collapses to |0> with probability |alpha|^2 or |1> with probability |beta|^2 Entanglement: Two qubits can be correlated in ways impossible classically - Bell state: (|00> + |11>) / sqrt(2) - Measuring one qubit instantly determines the other Quantum gates: Unitary operations that transform qubit states - Single-qubit: H (Hadamard), X (NOT), Z, S, T, Rx, Ry, Rz - Two-qubit: CNOT, CZ, SWAP - Multi-qubit: Toffoli (CCNOT), Fredkin (CSWAP) ``` ## Building Quantum Circuits with Qiskit ### Basic Circuit Construction ```python from qiskit import QuantumCircuit from qiskit_aer import AerSimulator def create_bell_state() -> QuantumCircuit: """ Create a Bell state (maximally entangled pair). """ qc = QuantumCircuit(2, 2) # Apply Hadamard to qubit 0 (creates superposition) qc.h(0) # Apply CNOT with qubit 0 as control, qubit 1 as target qc.cx(0, 1) # Measure both qubits qc.measure([0, 1], [0, 1]) return qc def run_circuit(qc: QuantumCircuit, shots: int = 1024) -> dict: """ Run a quantum circuit on a simulator. Args: qc: Quantum circuit to execute shots: Number of measurement repetitions """ simulator = AerSimulator() result = simulator.run(qc, shots=shots).result() counts = result.get_counts() return { "counts": counts, "probabilities": { state: count / shots for state, count in counts.items() } } ``` ### Quantum Teleportation Circuit ```python def quantum_teleportation() -> QuantumCircuit: """ Implement quantum teleportation protocol. Transfers the state of qubit 0 to qubit 2 using entanglement. """ qc = QuantumCircuit(3, 3) # Prepare an arbitrary state on qubit 0 qc.rx(1.2, 0) qc.rz(0.7, 0) qc.barrier() # Create entangled pair (qubits 1 and 2) qc.h(1) qc.cx(1, 2) qc.barrier() # Bell measurement on qubits 0 and 1 qc.cx(0, 1) qc.h(0) qc.measure([0, 1], [0, 1]) qc.barrier() # Conditional corrections on qubit 2 qc.cx(1, 2) qc.cz(0, 2) qc.measure(2, 2) return qc ``` ## Fundamental Quantum Algorithms ### Algorithm Overview | Algorithm | Speedup | Problem | |-----------|---------|---------| | Grover's | Quadratic (sqrt(N)) | Unstructured search | | Shor's | Exponential | Integer factorization | | VQE | Heuristic | Ground state energy | | QAOA | Heuristic | Combinatorial optimization | | Quantum Phase Estimation | Exponential | Eigenvalue estimation | | HHL | Exponential (conditions apply) | Linear systems | ### Variational Quantum Eigensolver (VQE) ```python from qiskit.circuit.library import TwoLocal def build_vqe_circuit(n_qubits: int, depth: int = 2) -> dict: """ Build a parameterized ansatz circuit for VQE. Args: n_qubits: Number of qubits depth: Circuit depth (repetitions) """ ansatz = TwoLocal( n_qubits, rotation_blocks=["ry", "rz"], entanglement_blocks="cx", entanglement="linear", reps=depth ) return { "circuit": ansatz, "n_parameters": ansatz.num_parameters, "description": ( "VQE uses a classical optimizer to minimize " " where psi(theta) is the " "parameterized quantum state and H is the Hamiltonian." ) } ``` ## Noise and Error Mitigation ### Simulating Realistic Noise ```python from qiskit_aer.noise import NoiseModel, depolarizing_error def create_noisy_simulator(error_rate: float = 0.01) -> dict: """ Create a noise model for realistic quantum simulation. Args: error_rate: Depolarizing error probability per gate """ noise_model = NoiseModel() # Single-qubit gate error error_1q = depolarizing_error(error_rate, 1) noise_model.add_all_qubit_quantum_error(error_1q, ["h", "rx", "ry", "rz"]) # Two-qubit gate error (typically higher) error_2q = depolarizing_error(error_rate * 10, 2) noise_model.add_all_qubit_quantum_error(error_2q, ["cx"]) return { "noise_model": noise_model, "single_qubit_error": error_rate, "two_qubit_error": error_rate * 10, "mitigation_strategies": [ "Zero-Noise Extrapolation (ZNE)", "Probabilistic Error Cancellation (PEC)", "Measurement error mitigation", "Dynamical decoupling", "Quantum error correction (surface codes)" ] } ``` ## Running on Real Hardware ### Practical Considerations ``` 1. Qubit connectivity: Real devices have limited qubit connections (not all-to-all) SWAP gates are needed to route operations -> increases circuit depth 2. Gate fidelity: Single-qubit gates: ~99.9% fidelity Two-qubit gates: ~99-99.5% fidelity Limits useful circuit depth to ~100-1000 gates 3. Coherence times: T1 (energy relaxation): 100-500 microseconds T2 (dephasing): 50-200 microseconds Circuit must complete before decoherence 4. Queue times: Real quantum computers have job queues (minutes to hours) Use simulators for development; reserve hardware for final runs ``` ## Publishing Quantum Computing Research Report the exact device used (name, calibration date), number of qubits and connectivity, gate set and fidelities, transpilation settings, number of shots, error mitigation techniques applied, and comparison with classical simulation where tractable. 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