Quantum Computing GuideSAFE

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---
name: quantum-computing-guide
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 "
            "<psi(theta)|H|psi(theta)> 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 (

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