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Tensor network based quantum software framework for the NISQ era
| Date | Stars |
|---|---|
| 2026-07-24 | 360 |
| 2026-07-25 | 360 |
| 2026-07-28 | 360 |
| 2026-07-30 | 360 |
| 2026-08-06 | 360 |
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<p align="center">
<a href="https://github.com/tencent-quantum-lab/tensorcircuit">
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<p align="center"> English | <a href="README_cn.md"> 简体中文 </a></p>
TensorCircuit is the next generation of quantum software framework with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.
TensorCircuit is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy and approximate cases. It also supports real quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions since v0.9.
## Getting Started
Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit.readthedocs.io/).
For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 70+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.
The following are some minimal demos.
- Circuit manipulation:
```python
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_ps(z=[0, 1]))
print(c.sample(allow_state=True, batch=1024, format="count_dict_bin"))
```
- Runtime behavior customization:
```python
tc.set_backend("tensorflow")
tc.set_dtype("complex128")
tc.set_contractor("greedy")
```
- Automatic differentiations with jit:
```python
def forward(theta):
c = tc.Circuit(2)
c.R(0, theta=theta, alpha=0.5, phi=0.8)
return tc.backend.real(c.expectation((tc.gates.z(), [0])))
g = tc.backend.grad(forward)
g = tc.backend.jit(g)
theta = tc.array_to_tensor(1.0)
print(g(theta))
```
<details>
<summary> More highlight features for TensorCircuit (click for details) </summary>
- Sparse Hamiltonian generation and expectation evaluation:
```python
n = 6
pauli_structures = []
weights = []
for i in range(n):
pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i + 1) % n]}, n=n))
weights.append(1.0)
for i in range(n):
pauli_structures.append(tc.quantum.xyz2ps({"x": [i]}, n=n))
weights.append(-1.0)
h = tc.quantum.PauliStringSum2COO(pauli_structures, weights)
print(h)
# BCOO(complex64[64, 64], nse=448)
c = tc.Circuit(n)
c.h(range(n))
energy = tc.templates.measurements.operator_expectation(c, h)
# -6
```
- Large-scale simulation with tensor network engine
```python
# tc.set_contractor("cotengra-30-10")
n=500
c = tc.Circuit(n)
c.h(0)
c.cx(range(n-1), range(1, n))
c.expectation_ps(z=[0, n-1], reuse=False)
```
- Density matrix simulator and quantum info quantities
```python
c = tc.DMCircuit(2)
c.h(0)
c.cx(0, 1)
c.depolariExcerpt of 29,567 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a7f3e5af51fd2644, topic:neural-network, topic:pytorch, topic:tensorflow