PennyLane 操作、测量与梯度

import os
import pennylane as qml
from pennylane import numpy as pnp
from quantum_source_sdk import SchedulerClient

scheduler = SchedulerClient(
    base_url=os.environ["QUANTUM_SOURCE_API_URL"],
    currency="OPEN",  # 如需 FULL，在准备任何任务前改为 FULL
)
backend_id = os.environ["QUANTUM_SOURCE_BACKEND"]
organization_id = os.environ["QUANTUM_SOURCE_ORGANIZATION_ID"]
dev = qml.device(
    "quantum_source.device",
    wires=2,
    backend=backend_id,
    organization_id=organization_id,
    scheduler=scheduler,
    auto_approve_quote=False,
)

plugin_caps = dev.capabilities
print("PennyLane 操作：", sorted(plugin_caps.operations))
print("可观察量：", sorted(plugin_caps.observables))
print("测量类型：", sorted(plugin_caps.measurement_processes))

@qml.set_shots(128)
@qml.qnode(dev, diff_method=None)
def experiment(theta):
    qml.RY(theta, wires=0)
    return (
        qml.counts(wires=[0], all_outcomes=True),
        qml.probs(wires=[0]),
        qml.expval(qml.Z(0)),
    )

tape = experiment.construct((0.42,), {})
prepared_statistics = dev.prepare(tape)
print(prepared_statistics.quote)

# 确认这个报价后，单独运行；这会执行本批次：
# counts, probabilities, expectation = prepared_statistics.confirm()

@qml.set_shots(128)
@qml.qnode(dev, diff_method=None)
def three_axes():
    qml.RY(0.37, wires=0)
    qml.RZ(0.28, wires=0)
    return qml.expval(qml.X(0)), qml.expval(qml.Y(0)), qml.expval(qml.Z(0))

prepared_axes = dev.prepare(three_axes.construct((), {}))
print("展开任务数：", prepared_axes.task_count)
print("总 shots：", prepared_axes.total_shots)
print(prepared_axes.quote)

# 决定执行这三个实验后再运行；返回 X、Y、Z 三个期望值：
# x_value, y_value, z_value = prepared_axes.confirm()

angles = pnp.array([0.0, 0.5, 1.0], requires_grad=False)
broadcast_tape = qml.tape.QuantumScript(
    [qml.RY(angles, wires=0)],
    [qml.expval(qml.Z(0))],
    shots=32,
)
prepared_broadcast = dev.prepare(broadcast_tape)
print("任务 / 总 shots：", prepared_broadcast.task_count, prepared_broadcast.total_shots)
print(prepared_broadcast.quote)

# 决定执行后再运行；本例结果形状为 (3,)：
# expectations = prepared_broadcast.confirm()

# 独立的梯度分支：这台设备允许每个具体批次自动批准报价。
# qml.grad 调用会执行梯度所需的实验。
gradient_dev = qml.device(
    "quantum_source.device",
    wires=1,
    backend=backend_id,
    organization_id=organization_id,
    scheduler=scheduler,
    auto_approve_quote=True,
)

@qml.set_shots(32)
@qml.qnode(gradient_dev, interface="autograd", diff_method="parameter-shift")
def loss(theta):
    qml.RY(theta[0], wires=0)
    qml.RZ(theta[1], wires=0)
    return qml.expval(qml.X(0))

theta = pnp.array([0.2, 0.3], requires_grad=True)

# 执行本次梯度实验时，取消以下代码的注释：
# gradient = qml.grad(loss)(theta)
# print(gradient)
# print(gradient_dev.job_ids)
# print(gradient_dev.total_credits_used)

# 为提供原生 probabilities 的设备构造概率测量分支。
probability_dev = qml.device(
    "quantum_source.device",
    wires=2,
    backend=backend_id,
    organization_id=organization_id,
    scheduler=scheduler,
    provider_probability_shots=128,
    auto_approve_quote=False,
)
probability_tape = qml.tape.QuantumScript(
    [qml.Hadamard(0), qml.CNOT(wires=[0, 1])],
    [qml.probs(wires=[0, 1])],
    shots=None,
)

# 先确认这台设备的 probabilities 契约，再准备：
# probability_quote = probability_dev.prepare(probability_tape)

# 完成实验后关闭本次创建的设备与连接。
dev.close()
# gradient_dev.close()
# probability_dev.close()
scheduler.close()