Tensor network backend

Simulating quantum circuits using tensor networks has been studied in the literature^{[Markov2008][Pan2022]}. The YaoToEinsum package provides a convenient way to convert Yao circuits to tensor networks, which can be used for further analysis and optimization.

Tutorial

The main function is

yao2einsum(circuit; initial_state=Dict(), final_state=Dict(), optimizer=TreeSA())

which transforms a Yao circuit to a tensor network that generalizes the hyper-graph (einsum notation). The return value is a TensorNetwork object.

• initial_state and final_state are for specifying the initial state and final state. Left the qubits unspecified if you want to keep them as the open indices.
• optimizer is for specifying the contraction order optimizing algorithm of the tensor network. The default value is the TreeSA() algorithm that developed in ^{[Kalachev2021][Liu2023]}. Please check the README of OMEinsumEinsumContractors.jl for more information.

In the following example, we show how to convert a quantum Fourier transform circuit to a tensor network and contract it to

• Get the matrix representation of the circuit.
• Get the probability of measuring the zero state after applying the circuit on the zero state.

julia> import Yao
julia> using Yao.EasyBuild: qft_circuit
julia> n = 10;
julia> circuit = qft_circuit(n);  # build a quantum Fourier transform circuit
julia> network = Yao.yao2einsum(circuit)  # convert this circuit to tensor networkTensorNetwork
Time complexity: 2^20.052833352562715
Space complexity: 2^20.0
Read-write complexity: 2^20.104081687003603
julia> reshape(Yao.contract(network), 1<<n, 1<<n) ≈ Yao.mat(circuit)true
julia> network = Yao.yao2einsum(circuit;  # convert circuit sandwiched by zero states
               initial_state=Dict([i=>0 for i=1:n]), final_state=Dict([i=>0 for i=1:n]),
               optimizer=Yao.YaoToEinsum.TreeSA(; nslices=3)) # slicing techniqueTensorNetwork
Time complexity: 2^12.25029841790633
Space complexity: 2^5.0
Read-write complexity: 2^13.068778277985412
julia> Yao.contract(network)[] ≈ Yao.zero_state(n)' * (Yao.zero_state(n) |> circuit)true

API

yao2einsum(circuit; initial_state=Dict(), final_state=Dict(), optimizer=TreeSA())
yao2einsum(circuit, initial_state::Dict, final_state::Dict, optimizer)

julia> using Yao

julia> c = chain(3, put(3, 2=>X), put(3, 1=>Y), control(3, 1, 3=>Y))
nqubits: 3
chain
├─ put on (2)
│  └─ X
├─ put on (1)
│  └─ Y
└─ control(1)
   └─ (3,) Y


julia> yao2einsum(c; initial_state=Dict(1=>0, 2=>1), final_state=Dict(1=>ArrayReg([0.6, 0.8im]), 2=>1))
TensorNetwork
Time complexity: 2^4.700439718141093
Space complexity: 2^2.0
Read-write complexity: 2^6.0

TensorNetwork

optimize_code(c::TensorNetwork, optimizer=TreeSA())

contraction_complexity(c::TensorNetwork)

contract(c::TensorNetwork)

References