Coherent Error Simulation

Coherent error or unitary error is a type of error that can be described by a unitary matrix acting on the quantum state. For example, if we want to apply an unitary $U$ on the state, however, we apply a unitary $U'$ instead, which differ from $U$ slighty. The error can be described by an unitary $E = U'U^{\dagger}$ acting on the quantum state. Usually, this unitary is non-Clifford, thus it is hard to simulate with the stabilizer formalism. Here, we use tensor network to simulate a quantum circuit with coherent error.

Quantum Circuit Construction

First, we define the stabilizers for Steane code.

using TensorQEC, TensorQEC.Yao
using TensorQEC.OMEinsum
st = stabilizers(SteaneCode())

6-element Vector{PauliString{7}}:
 XIXIXIX
 IXXIIXX
 IIIXXXX
 ZIZIZIZ
 IZZIIZZ
 IIIZZZZ

Then we generate the encoding circuits of the stabilizers by encode_stabilizers. qc is the encoding circuit, data_qubits are the qubits that we should put initial qubtis in, and code is the structure records information of the encoding circuit.

qcen, data_qubits, code = encode_stabilizers(st)
vizcircuit(qcen)

Now we construct a 7-qubit quantum circuit to perform the following operations:

1. Encoding the initial state with the encoding circuit.
2. Apply an logical X gate which consists of three X gates on the seven qubits.
3. Decoding the state with the encoding circuit.
4. Apply an X gate to recover the initial state.

qc = chain(qcen)
push!(qc, [put(7, i => X) for i in 1:7]...)
push!(qc, qcen')
push!(qc, put(7, 6 => X))
vizcircuit(qc)

This circuit should act trivially on the data qubit. We will check this later.

Circuit Simulation with Tensor Networks

Simulating quantum circuits using tensor networks is a powerful technique, particularly for circuits that are not easily amenable to simulation with classical computers^[Markov]. We can replace gates and density matries by tensors to get the tensor network representation of the quantum circuit. Applying a quantum channel on a density matix is equivalent to connecting two tensors together and contracting them.

To trace out a matrix in the tensor network, we can simply connect the two indices of the matrix and contract them. To partially trace out the ancilla qubits, we can simply connect the output indices of the ancilla qubits and contract them.

We can use the function simulation_tensornetwork to generate the tensor network of the quantum channel.

tn,input_indices,output_indices = simulation_tensornetwork(qc, QCInfo(data_qubits, 7))

(TensorNetwork
Time complexity: 2^80.0
Space complexity: 2^4.0
Read-write complexity: 2^8.977279923499918, [6, 13], [50, 86])

And we contract the tensor network to get the matrix representation of the quantum channel, which is an identity channel.

optnet = optimize_code(tn, TreeSA(; ntrials=1, niters=3), OMEinsum.MergeVectors())
matr = contract(optnet)

2×2×2×2 Array{ComplexF64, 4}:
[:, :, 1, 1] =
 1.0+0.0im  0.0+0.0im
 0.0+0.0im  0.0+0.0im

[:, :, 2, 1] =
 0.0+0.0im  0.0+0.0im
 1.0+0.0im  0.0+0.0im

[:, :, 1, 2] =
 0.0+0.0im  1.0+0.0im
 0.0+0.0im  0.0+0.0im

[:, :, 2, 2] =
 0.0+0.0im  0.0+0.0im
 0.0+0.0im  1.0+0.0im

Also we can compute the circuit fidelity with identity channel directly by connecting the input and output indices of the quantum channel and contracting them.

fidelity_tensornetwork transforms the circuit to a tensor network to calculate fidelity.

tn = fidelity_tensornetwork(qc, QCInfo(data_qubits, 7))
optnet = optimize_code(tn, TreeSA(; ntrials=1, niters=3), OMEinsum.MergeVectors())
infidelity = 1 - abs(contract(optnet)[1])

9.992007221626409e-16

Coherent Error Simulation with Tensor Network

We add coherent error to the circuit by adding unitary error to every unitary gate by error_quantum_circuit, which replaces the gates in the original circuit with the errored gates.

eqc = error_quantum_circuit(qc, 1e-5)
vizcircuit(eqc)

Finally, we can check the infidelity after the circuit with coherent error.

tn = fidelity_tensornetwork(eqc, QCInfo(data_qubits, 7))
optnet = optimize_code(tn, TreeSA(; ntrials=1, niters=3), OMEinsum.MergeVectors())
infidelity = 1 - abs(contract(optnet)[1])

0.0002666655450316302

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