Cqlib adapter
Project description
Cqlib Adapter
Installation
Install the package using pip:
pip install cqlib-adapter
1. Qiskit Ext
This project provides a Qiskit adapter for the TianYan quantum computing platform. It includes custom quantum gates and integrates with the TianYan backend to enable seamless execution of quantum circuits.
Features
- Custom Quantum Gates: Adds custom gates like
X2P,X2M,Y2P,Y2M,XY2P, andXY2Mto Qiskit. - TianYan Backend Integration: Supports execution of quantum circuits on TianYan quantum computers and simulators.
- Transpilation: Automatically transpiles Qiskit circuits to be compatible with TianYan backends.
QCIS Gates
The following QCIS gates are added to Qiskit:
- X2P: Positive X rotation by π/2.
- X2M: Negative X rotation by π/2.
- Y2P: Positive Y rotation by π/2.
- Y2M: Negative Y rotation by π/2.
- XY2P: Positive XY rotation by a parameterized angle.
- XY2M: Negative XY rotation by a parameterized angle.
Usage Example
Log in to the TianYan Lab, retrieve your Connection Key from the Dashboard page,
and replace your_token in the code below.
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from cqlib_adapter.qiskit_ext import X2PGate
# Create a quantum circuit
qs = QuantumRegister(2)
cs = ClassicalRegister(2)
circuit = QuantumCircuit(qs, cs)
circuit.x(qs[1])
circuit.h(qs[0])
circuit.cx(qs[0], qs[1])
circuit.append(X2PGate(), [qs[0]])
circuit.barrier(qs)
circuit.measure(qs, cs)
circuit.draw()
Circuit Text Diagram:
┌───┐ ┌─────┐ ░ ┌─┐
q0_0: ┤ H ├──■──┤ X2p ├─░─┤M├───
├───┤┌─┴─┐└─────┘ ░ └╥┘┌─┐
q0_1: ┤ X ├┤ X ├────────░──╫─┤M├
└───┘└───┘ ░ ║ └╥┘
c0: 2/═════════════════════╩══╩═
0 1
1. Backend mode
from cqlib_adapter.qiskit_ext import TianYanProvider
# Initialize the TianYan provider
provider = TianYanProvider(token='your_token')
# Retrieve a specific backend (e.g., 'tianyan176-2')
backend = provider.backend('tianyan176-2')
# Run the circuit on the backend
job = backend.run([circuit], shots=3000)
# Retrieve and print the results
print(f'Job ID: {job.job_id()}')
print(f'Job Result: {job.result().get_counts()}')
2. Sampler mode
from cqlib_adapter.qiskit_ext import TianYanProvider, TianYanSampler
# Initialize the TianYan provider
provider = TianYanProvider(token='your_token')
# Retrieve a specific backend (e.g., 'tianyan24')
backend = provider.backend('tianyan24')
# Run the circuit on the backend
job = TianYanSampler(backend=backend).run([circuit], shots=3000)
# Retrieve and print the results
print(f'Job ID: {job.job_id()}')
print(f'Job Result: {job.result()}')
# c0 is the default register name
# cs = ClassicalRegister(2)
print(f'Counts: {job.result()[0].data.c0.get_counts()}')
PennyLane Ext
This project provides a PennyLane device adapter for the CQLib quantum computing framework. It enables seamless execution of PennyLane quantum circuits on various CQLib backends, including TianYan quantum hardware and simulators.
Features
- Multiple Backend Support: Supports execution on TianYan quantum hardware, cloud simulators, and local simulators
- Unified Interface: No need to call cqlib directly - all configuration is done through PennyLane device settings
Supported TianYan Backends
Quantum Hardware
tianyan24, tianyan504, tianyan176-2, tianyan176
Cloud Simulators
tianyan_sw, tianyan_s, tianyan_tn, tianyan_tnn, tianyan_sa, tianyan_swn
Local Simulator
default (local simulator)
Usage Example
import pennylane as qml
from pennylane import numpy as np
TOKEN = "your_token"
dev = qml.device('cqlib.device', wires=2, shots=500, cqlib_backend_name="default",login_key = TOKEN)
@qml.qnode(dev, diff_method="parameter-shift")
def circuit(params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliY(0))
params = np.array([0.5, 0.8], requires_grad=True)
opt = qml.GradientDescentOptimizer(stepsize=0.1)
steps = 10
for i in range(steps):
params = opt.step(circuit, params)
print(f"step {i + 1}: paras = {params}, exps = {circuit(params)}")
License
This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.
Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
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