重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
qiskit by k-dense-ai/claude-scientific-skills
npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill qiskitQiskit 是全球最受欢迎的开源量子计算框架,下载量超过 1300 万次。您可以构建量子电路、针对硬件进行优化、在模拟器或真实的量子计算机上执行,并分析结果。支持 IBM Quantum(100+ 量子位系统)、IonQ、Amazon Braket 等提供商。
主要特性:
uv pip install qiskit
uv pip install "qiskit[visualization]" matplotlib
from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler
# 创建贝尔态(纠缠的量子比特)
qc = QuantumCircuit(2)
qc.h(0) # 在量子比特 0 上应用 Hadamard 门
qc.cx(0, 1) # 从量子比特 0 到 1 的 CNOT 门
qc.measure_all() # 测量所有量子比特
# 本地运行
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
print(counts) # {'00': ~512, '11': ~512}
from qiskit.visualization import plot_histogram
qc.draw('mpl') # 电路图
plot_histogram(counts) # 结果直方图
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
关于详细的安装、认证和 IBM Quantum 账户设置:
references/setup.md涵盖主题:
关于使用量子门、测量和组合来构建量子电路:
references/circuits.md涵盖主题:
关于执行量子电路和计算结果:
references/primitives.md涵盖主题:
关于优化电路并为硬件执行做准备:
references/transpilation.md涵盖主题:
关于展示电路、结果和量子态:
references/visualization.md涵盖主题:
关于在模拟器和真实量子计算机上运行:
references/backends.md涵盖主题:
关于实现四步量子计算工作流:
references/patterns.md涵盖主题:
关于实现特定的量子算法:
references/algorithms.md涵盖主题:
如果您需要:
references/setup.mdreferences/circuits.mdreferences/circuits.mdreferences/primitives.mdreferences/primitives.mdreferences/transpilation.mdreferences/visualization.mdreferences/backends.mdreferences/backends.mdreferences/patterns.mdreferences/algorithms.mdreferences/algorithms.md从模拟器开始:在使用硬件前先在本地测试
from qiskit.primitives import StatevectorSampler
sampler = StatevectorSampler()
始终进行编译:在执行前优化电路
from qiskit import transpile
qc_optimized = transpile(qc, backend=backend, optimization_level=3)
使用合适的原语:
选择执行模式:
from qiskit import QuantumCircuit, transpile
from qiskit.primitives import StatevectorSampler
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit import transpile
service = QiskitRuntimeService()
backend = service.backend("ibm_brisbane")
qc_optimized = transpile(qc, backend=backend, optimization_level=3)
sampler = Sampler(backend)
job = sampler.run([qc_optimized], shots=1024)
result = job.result()
from qiskit_ibm_runtime import Session, EstimatorV2 as Estimator
from scipy.optimize import minimize
with Session(backend=backend) as session:
estimator = Estimator(session=session)
def cost_function(params):
bound_qc = ansatz.assign_parameters(params)
qc_isa = transpile(bound_qc, backend=backend)
result = estimator.run([(qc_isa, hamiltonian)]).result()
return result[0].data.evs
result = minimize(cost_function, initial_params, method='COBYLA')
每周安装量
55
代码仓库
GitHub 星标数
17.3K
首次出现
2026年1月20日
安全审计
已安装于
opencode48
codex47
gemini-cli46
claude-code44
cursor44
github-copilot43
Qiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.
Key Features:
uv pip install qiskit
uv pip install "qiskit[visualization]" matplotlib
from qiskit import QuantumCircuit
from qiskit.primitives import StatevectorSampler
# Create Bell state (entangled qubits)
qc = QuantumCircuit(2)
qc.h(0) # Hadamard on qubit 0
qc.cx(0, 1) # CNOT from qubit 0 to 1
qc.measure_all() # Measure both qubits
# Run locally
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
print(counts) # {'00': ~512, '11': ~512}
from qiskit.visualization import plot_histogram
qc.draw('mpl') # Circuit diagram
plot_histogram(counts) # Results histogram
For detailed installation, authentication, and IBM Quantum account setup:
references/setup.mdTopics covered:
For constructing quantum circuits with gates, measurements, and composition:
references/circuits.mdTopics covered:
For executing quantum circuits and computing results:
references/primitives.mdTopics covered:
For optimizing circuits and preparing for hardware execution:
references/transpilation.mdTopics covered:
For displaying circuits, results, and quantum states:
references/visualization.mdTopics covered:
For running on simulators and real quantum computers:
references/backends.mdTopics covered:
For implementing the four-step quantum computing workflow:
references/patterns.mdTopics covered:
For implementing specific quantum algorithms:
references/algorithms.mdTopics covered:
If you need to:
references/setup.mdreferences/circuits.mdreferences/circuits.mdreferences/primitives.mdreferences/primitives.mdreferences/transpilation.mdreferences/visualization.mdreferences/backends.mdreferences/backends.mdStart with simulators : Test locally before using hardware
from qiskit.primitives import StatevectorSampler sampler = StatevectorSampler()
Always transpile : Optimize circuits before execution
from qiskit import transpile qc_optimized = transpile(qc, backend=backend, optimization_level=3)
Use appropriate primitives :
Choose execution mode :
from qiskit import QuantumCircuit, transpile
from qiskit.primitives import StatevectorSampler
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
sampler = StatevectorSampler()
result = sampler.run([qc], shots=1024).result()
counts = result[0].data.meas.get_counts()
from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler
from qiskit import transpile
service = QiskitRuntimeService()
backend = service.backend("ibm_brisbane")
qc_optimized = transpile(qc, backend=backend, optimization_level=3)
sampler = Sampler(backend)
job = sampler.run([qc_optimized], shots=1024)
result = job.result()
from qiskit_ibm_runtime import Session, EstimatorV2 as Estimator
from scipy.optimize import minimize
with Session(backend=backend) as session:
estimator = Estimator(session=session)
def cost_function(params):
bound_qc = ansatz.assign_parameters(params)
qc_isa = transpile(bound_qc, backend=backend)
result = estimator.run([(qc_isa, hamiltonian)]).result()
return result[0].data.evs
result = minimize(cost_function, initial_params, method='COBYLA')
Weekly Installs
55
Repository
GitHub Stars
17.3K
First Seen
Jan 20, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode48
codex47
gemini-cli46
claude-code44
cursor44
github-copilot43
marimo-batch:Python批处理任务神器,Pydantic声明式数据源与UI/CLI双模式
954 周安装
references/patterns.mdreferences/algorithms.mdreferences/algorithms.md