Tensor-Based Quantum Machine Learning

Related tags

Deep Learning quantum quantum-computing tensorly quantum-machine-learning tensor-networks quantum-circuit-simulator tensorly-quantum
Overview
https://codecov.io/gh/tensorly/quantum/branch/main/graph/badge.svg?token=5P8GZ8YLO7

TensorLy_Quantum

TensorLy-Quantum is a Python library for Tensor-Based Quantum Machine Learning that builds on top of TensorLy and PyTorch.

With TensorLy-Quantum, you can easily:

  • Create large quantum circuit: Tensor network formalism requires up to exponentially less memory for quantum simulation than traditional vector and matrix approaches.
  • Leverage tensor methods: the state vectors are efficiently represented in factorized form as Tensor-Rings (MPS) and the operators as TT-Matrices (MPO)
  • Efficient simulation: tensorly-quantum leverages the factorized structure to efficiently perform quantum simulation without ever forming the full, dense operators and state-vectors
  • Multi-Basis Encoding: we provide multi-basis encoding out-of-the-box for scalable experimentation
  • Solve hard problems: we provide all the tools to solve the MaxCut problem for an unprecendented number of qubits / vertices

Installing TensorLy-Quantum

Through pip

pip install tensorly-quantum

From source

git clone https://github.com/tensorly/quantum
cd quantum
pip install -e .
Comments
  • Rz has no gradient issue resolved

    Rz has no gradient issue resolved

    Hey there, The way RotZ was implemented it didn't have any gradient. I fixed the issue by using the same template as for the RotY and RotX. I think the tl.tensor() in the original version somehow blocked the backprop. The way it is written now the gradient is correct.

    opened by PatrickHuembeli 3
  • calculate_cut in the VQE example?

    calculate_cut in the VQE example?

    Hello! I have been trying to use your code to compute the MaxCut in the VQE jupyter notebook provided in the example sections. I tried to apply the calculate_cut function on the state as tlq.calculate_cut(state, qubits1, qubits2, weights) but I am having the following error TypeError: only integer tensors of a single element can be converted to an index.

    I see that the cut is calculated differently in the MBE example, but I would like to know if there is an analogue way of doing it with the VQE. Or should I just adapt my Hamiltonian to maximize the cut? Any help is appreciated, Thanks!

    opened by marionsilv 2
  • How to use cuQuantum as a backend

    How to use cuQuantum as a backend

    Hi,

    Thank you for your great work! May I know how to use cuQuantum as a backend as mentioned in your paper? Could you please provide a code example? How does the cuQuantum backend support autograd? Thank you very much!

    opened by nadbp 1
  • CNOT gate issue

    CNOT gate issue

    Hello,

    I have been trying to build a circuit with a CNOT gate acting on non-contiguous qubits (e.g., qubit 1 and 4), but I am finding strange results.

    For example, if I choose an initial state [1,0,0,0]

    and apply the unitary uni = tlq.Unitary([tlq.CNOTL(device=device, dtype=dtype), tlq.CNOTR(device=device, dtype=dtype), tlq.IDENTITY(dtype=dtype, device=device), tlq.IDENTITY(dtype=dtype, device=device)], nqubits, ncontraq, device=device, dtype=dtype)

    I get (for the expected value of Sz): tensor([-1., -1., 1., 1.])

    However, if I apply the CNOT cores to non-adjacent qubits in the same initial state, with uni = tlq.Unitary([tlq.CNOTL(device=device, dtype=dtype), tlq.IDENTITY(dtype=dtype, device=device), tlq.IDENTITY(dtype=dtype, device=device), tlq.CNOTR(device=device, dtype=dtype)], nqubits, ncontraq, device=device, dtype=dtype)

    I find, again for the expected value of Sz: tensor([-2., 2., 2., 0.])

    Is there any limitation regarding the CNOT cores that make it only valid for adjacent qubits, or am I doing something wrong? I am attaching a file with the full code for running: code.txt

    Thanks for the help, Marion Silvestrini.

    opened by marionsilv 2
  • Hamiltonian unitary

    Hamiltonian unitary

    Hello all,

    I was wondering if there is a way in TensorLy Quantum to build a parametrised unitary based on a binary Hamiltonian, such as the Ising model given in the examples, for use in the circuits.

    I mean to use it in an application like a QAOA, for instance. Is there a way to adapt from the binary_hamiltonian function, or something like that?

    Thanks!

    opened by rafaeleb 10
Releases(0.1.0)
Owner
TensorLy
Tensor Learning in Python.
TensorLy
GitHub http://tensorly.org/quantum/dev/
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