Quantum circuits on top of tensor network
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TensorCircuit is the next generation of quantum circuit simulators with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.
TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.
The following are some minimal demos.
import tensorcircuit as tc c = tc.Circuit(2) c.H(0) c.CNOT(0,1) c.rx(1, theta=0.2) print(c.wavefunction()) print(c.expectation((tc.gates.z(), ))) print(c.perfect_sampling())
Runtime behavior customization:
tc.set_backend("tensorflow") tc.set_dtype("complex128") tc.set_contractor("greedy")
Automatic differentiations with jit:
def forward(theta): c = tc.Circuit(n=2) c.R(0, theta=theta, alpha=0.5, phi=0.8) return tc.backend.real(c.expectation((tc.gates.z(), ))) g = tc.backend.grad(forward) g = tc.backend.jit(g) theta = tc.gates.num_to_tensor(1.0) print(g(theta))
pip install tensorcircuit.
Extra package installation may be required for some features.
For contribution guidelines and notes, see CONTRIBUTING.
For developers, we suggest first configuring a good conda environment. The versions of dependence packages may vary in terms of development requirements. The minimum requirement is the TensorNetwork package. Dockerfile is also provided.
Researches and applications
VQEX - MBL
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