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, and XY2M to 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

QCIS Instruction Manual

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.