2nd/10_Wiki/Topic_Programming/AI_and_ML/Quantum-Computing-for-AI.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-quantum-computing-for-ai	Quantum Computing for AI	10_Wiki/Topics	verified	self	Quantum ML QML Quantum Machine Learning	none	A	0.85	applied	quantum machine-learning qml niche		2026-05-10	pending	language framework python qiskit/pennylane

Quantum Computing for AI

매 한 줄

"매 quantum advantage 의 ML — 아직 mostly aspirational". 2026 현재 매 NISQ era — 100~1000 qubit, noisy. Variational quantum circuit (VQC) 의 hybrid classical-quantum optimizer 의 limited niche utility. 매 LLM scaling 의 dominant paradigm — 매 quantum ML 의 매 specialized boutique research.

매 핵심

매 NISQ-era reality (2026)

• IBM Heron r2 (156 qubit), Quantinuum H2 (56 qubit ion trap), Google Willow (105 qubit, 2024 error-corrected milestone).
• Logical qubit count 의 still <10 (Willow 의 1 logical qubit demo). Fault-tolerant ML era 의 ~2030+.
• Practical ML advantage 의 still unproven on real hardware.

매 algorithm classes

• VQC (Variational Quantum Circuit): parameterized gate circuit, classical optimizer (COBYLA, SPSA). 매 QNN 의 base.
• QAOA (Quantum Approximate Optimization): combinatorial opt (max-cut, portfolio).
• VQE (Variational Quantum Eigensolver): ground-state energy, chemistry — 매 closest to practical advantage.
• Quantum kernel: 매 SVM with quantum feature map. Havlíček 2019.
• HHL: linear system solve, exponential speedup in theory — 매 caveats (sparse, well-conditioned, quantum I/O).

매 응용

1. Quantum chemistry (drug discovery, materials).
2. Combinatorial optimization (logistics, finance portfolio).
3. Quantum kernel SVM on small datasets.
4. Generative QML (quantum GAN, quantum Boltzmann) — 매 research stage.

💻 패턴

PennyLane VQC

import pennylane as qml
import torch

n_qubits = 4
dev = qml.device("default.qubit", wires=n_qubits)

@qml.qnode(dev, interface="torch")
def circuit(inputs, weights):
    qml.AngleEmbedding(inputs, wires=range(n_qubits))
    qml.BasicEntanglerLayers(weights, wires=range(n_qubits))
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

weight_shape = {"weights": (2, n_qubits)}
qlayer = qml.qnn.TorchLayer(circuit, weight_shape)

model = torch.nn.Sequential(
    torch.nn.Linear(8, n_qubits),
    qlayer,
    torch.nn.Linear(n_qubits, 2),
)

Qiskit VQE for H2 ground state

from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.second_q.mappers import JordanWignerMapper
from qiskit_algorithms import VQE
from qiskit_algorithms.optimizers import SLSQP
from qiskit.circuit.library import EfficientSU2
from qiskit.primitives import Estimator

driver = PySCFDriver(atom="H 0 0 0; H 0 0 0.74")
problem = driver.run()
mapper = JordanWignerMapper()
hamiltonian = mapper.map(problem.second_q_ops()[0])

ansatz = EfficientSU2(hamiltonian.num_qubits, reps=2)
vqe = VQE(Estimator(), ansatz, SLSQP())
result = vqe.compute_minimum_eigenvalue(hamiltonian)
print(f"Ground state energy: {result.eigenvalue.real:.4f} Ha")

Quantum kernel SVM

from sklearn.svm import SVC
from qiskit_machine_learning.kernels import FidelityQuantumKernel
from qiskit.circuit.library import ZZFeatureMap

feature_map = ZZFeatureMap(feature_dimension=4, reps=2)
qkernel = FidelityQuantumKernel(feature_map=feature_map)

svc = SVC(kernel=qkernel.evaluate)
svc.fit(X_train, y_train)  # 매 small dataset only — kernel eval 의 expensive

QAOA for max-cut

from qiskit_optimization.applications import Maxcut
from qiskit_algorithms import QAOA
from qiskit_algorithms.optimizers import COBYLA
from qiskit.primitives import Sampler

graph = ...  # networkx graph
maxcut = Maxcut(graph)
qubo = maxcut.to_quadratic_program()
qaoa = QAOA(Sampler(), COBYLA(), reps=3)
result = qaoa.compute_minimum_eigenvalue(qubo.to_ising()[0])

IBM Quantum runtime

from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2

service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.backend("ibm_torino")  # Heron r1, 133 qubit
estimator = EstimatorV2(mode=backend)
job = estimator.run([(circuit, observable, params)])
result = job.result()

Barren plateau 의 회피 (CDR / shallow circuit)

# 매 deep VQC 의 gradient 의 vanish exponentially in qubit count
# 매 mitigation: layer-wise training, identity-block init, problem-aware ansatz
ansatz = qml.templates.SimplifiedTwoDesign(
    initial_layer_weights=torch.zeros(n_qubits),  # identity init
    weights=torch.randn(n_layers, n_qubits - 1, 2) * 0.01,
    wires=range(n_qubits),
)

매 결정 기준

상황	Approach
Small molecule chemistry	VQE (closest to practical)
Combinatorial opt, classical heuristic insufficient	QAOA (compare vs SA, branch-and-bound)
Tiny labeled dataset (<100), classical kernel weak	Quantum kernel SVM (sim only)
Standard ML (image, NLP)	classical (LLM, ViT) — 매 quantum 의 X
Production deployment 2026	classical, full stop

기본값: 매 simulator (PennyLane / Qiskit Aer) 에 prototype. 매 real hardware 의 noise + access cost 의 prohibitive for ML. 매 LLM era 에서 매 quantum 의 niche research, 매 practical ML 의 X.

🔗 Graph

• 부모: Quantum-Computing · Machine-Learning
• 응용: Combinatorial-Optimization

🤖 LLM 활용

언제: explain quantum algorithm (HHL, Grover, Shor) 의 high-level intuition; generate Qiskit / PennyLane boilerplate; literature survey. 언제 X: actual quantum algorithm correctness (LLM 의 hallucinate gate sequences, mismeasure circuits). 매 verify with simulator.

❌ 안티패턴

• Quantum hype: claim "exponential speedup" without specifying problem class + caveats.
• NISQ on big data: 매 quantum I/O bottleneck 의 kill any speedup.
• Deep ansatz blind: barren plateau, gradient vanishes — 매 shallow + problem-informed.
• Ignore noise: simulator results 의 not transfer to real hardware without error mitigation (ZNE, PEC).
• Quantum ML for MNIST: classical CNN 의 99%, quantum 의 80% — 매 not a benchmark.

🧪 검증 / 중복

• Verified (Qiskit 1.x docs 2026, PennyLane docs, Preskill "NISQ era" 2018, Google Willow 2024 paper, IBM Quantum roadmap).
• 신뢰도 A (subject-matter), B for "practical advantage" claims.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — VQE/QAOA/quantum kernel patterns + 2026 NISQ reality check