qujax

Represent a (parameterised) quantum circuit as a pure JAX function that takes as input any parameters of the circuit and outputs either a statetensor or a densitytensor depending on the choice of simulator.

• The statetensor encodes all $2^N$ amplitudes of the quantum state in a tensor version of the statevector, for $N$ qubits.
• The densitytensor represents a tensor version of the $2^N \times 2^N$ density matrix (allowing for mixed states and generic Kraus operators).

Either representation can then be used downstream for exact expectations, gradients or sampling. A JAX implementation of a quantum circuit is useful for runtime speedups, automatic differentiation, support for GPUs/TPUs and compatibility with other JAX code and packages.

Some useful links:

• Documentation
• PyPI
• Example notebooks
• pytket-qujax

Install

pip install qujax

Statetensor simulations with qujax

from jax import numpy as jnp
import qujax

circuit_gates = ['H', 'Ry', 'CZ']
circuit_qubit_inds = [[0], [0], [0, 1]]
circuit_params_inds = [[], [0], []]

qujax.print_circuit(circuit_gates, circuit_qubit_inds, circuit_params_inds);
# q0: -----H-----Ry[0]-----◯---
#                          |   
# q1: ---------------------CZ--

param_to_st = qujax.get_params_to_statetensor_func(circuit_gates,
                                                   circuit_qubit_inds,
                                                   circuit_params_inds)

We now have a pure JAX function that generates the statetensor for given parameters

param_to_st(jnp.array([0.1]))
# Array([[0.58778524+0.j, 0.        +0.j],
#        [0.80901706+0.j, 0.        +0.j]], dtype=complex64)

The statevector can be obtained from the statetensor via .flatten().

param_to_st(jnp.array([0.1])).flatten()
# Array([0.58778524+0.j, 0.+0.j, 0.80901706+0.j, 0.+0.j], dtype=complex64)

We can also use qujax to map the statetensor to an expected value

st_to_expectation = qujax.get_statetensor_to_expectation_func([['Z']], [[0]], [1.])

Combining the two gives us a parameter to expectation function that can be differentiated seamlessly and exactly with JAX

from jax import value_and_grad

param_to_expectation = lambda param: st_to_expectation(param_to_st(param))
expectation_and_grad = value_and_grad(param_to_expectation)
expectation_and_grad(jnp.array([0.1]))
# (Array(-0.3090171, dtype=float32),
#    Array([-2.987832], dtype=float32))

Densitytensor simulations with qujax

param_to_dt = qujax.get_params_to_densitytensor_func(circuit_gates,
                                                     circuit_qubit_inds,
                                                     circuit_params_inds)
dt = param_to_dt(jnp.array([0.1]))
dt.shape
# (2, 2, 2, 2)

The densitytensor has shape (2,) * 2 * N and the density matrix can be obtained with .reshape(2 * N, 2 * N).

Expectations can also be evaluated through the densitytensor

dt_to_expectation = qujax.get_densitytensor_to_expectation_func([['Z']], [[0]], [1.])
dt_to_expectation(dt)
# Array(-0.3090171, dtype=float32)

Again everything is differentiable, jit-able and can be composed with other JAX code.

Notes

• We use the convention where parameters are given in units of π (i.e. in [0,2] rather than [0, 2π]).
• By default, the simulators are initiated in the all 0 state, however the optional statetensor_in or densitytensor_in argument can be used for arbitrary initialisations and combining circuits.

pytket-qujax

You can also generate the parameter to statetensor/densitytensor functions from a pytket circuit using the pytket-qujax extension. In particular, the tk_to_qujax and tk_to_qujax_symbolic functions. An example notebook can be found at pytket-qujax_heisenberg_vqe.ipynb.

Contributing

Bugs and feature requests are managed using GitHub issues.

Pull requests are welcomed!

1. First fork the repo and create your branch from develop.
2. Add your code.
3. Add your tests.
4. Update the documentation if required.
5. Check the code lints (run black . --check and pylint */)
6. Issue a pull request into develop.

New commits on develop will then be merged into main on the next release.

Cite

@software{qujax2022,
  author = {Samuel Duffield and Kirill Plekhanov and Gabriel Matos and Melf Johannsen},
  title = {qujax: Simulating quantum circuits with JAX},
  url = {https://github.com/CQCL/qujax},
  year = {2022},
}