Leakage Suppression

When working with multilevel quantum systems (like transmons), population can "leak" from the computational subspace to higher energy levels. This guide shows how to suppress leakage in Piccolo.jl.

The Problem

Consider a 3-level transmon where we want to implement gates only on the |0⟩ and |1⟩ states. During optimization, population might temporarily occupy |2⟩, which can:

Reduce gate fidelity
Cause errors in subsequent operations
Lead to non-unitary dynamics if |2⟩ decays

EmbeddedOperator

The key tool for handling subspace gates is EmbeddedOperator. It creates a full unitary that acts as the specified gate on the computational subspace and as identity on leakage levels.

For a 3-level system with 2-level computational subspace:

\[U_{\text{embedded}} = \begin{pmatrix} U_{\text{gate}} & 0 \\ 0 & I \end{pmatrix}\]

Basic Usage

using Piccolo
using Random
Random.seed!(42)

# Create a 3-level transmon
sys = TransmonSystem(levels = 3, δ = 0.2, drive_bounds = [0.4, 0.4])

# Define X gate in computational subspace
U_goal = EmbeddedOperator(:X, sys)

size(U_goal.operator)

(3, 3)

U_goal.subspace

2-element Vector{Int64}:
 1
 2

Construction Options

# From symbol (gate name)
U_X = EmbeddedOperator(:X, sys)
U_H = EmbeddedOperator(:H, sys)

# From matrix
custom_gate = ComplexF64[1 0; 0 exp(im * π / 4)]  # T gate
U_T = EmbeddedOperator(custom_gate, sys)

EmbeddedOperator{ComplexF64}(ComplexF64[1.0 + 0.0im 0.0 + 0.0im 0.0 + 0.0im; 0.0 + 0.0im 0.7071067811865476 + 0.7071067811865475im 0.0 + 0.0im; 0.0 + 0.0im 0.0 + 0.0im 0.0 + 0.0im], [1, 2], [3])

Accessing Subspace Information

# Computational subspace indices
U_goal.subspace

2-element Vector{Int64}:
 1
 2

# Leakage indices in isomorphic vector space
leak_indices = get_iso_vec_leakage_indices(U_goal)
leak_indices

8-element Vector{Int64}:
  3
  6
  9
 12
 13
 14
 16
 17

Leakage via PiccoloOptions

The easiest way to add leakage handling is through PiccoloOptions.

Add Leakage Objective

Penalize population in leakage states with a soft cost:

opts_cost = PiccoloOptions(
    leakage_cost = 10.0,  # Weight on leakage penalty
    verbose = false,
)

PiccoloOptions(false, true, ExponentialAction.expv, true, false, nothing, 1.0, false, false, 0.01, 10.0)

Add Leakage Constraint

Enforce a hard bound on leakage:

opts_constraint = PiccoloOptions(
    leakage_constraint = true,
    leakage_constraint_value = 1e-3,  # Max 0.1% leakage
    verbose = false,
)

PiccoloOptions(false, true, ExponentialAction.expv, true, false, nothing, 1.0, false, true, 0.001, 0.01)

Both Together

opts = PiccoloOptions(
    leakage_cost = 10.0,
    leakage_constraint = true,
    leakage_constraint_value = 1e-3,
    verbose = false,
)

PiccoloOptions(false, true, ExponentialAction.expv, true, false, nothing, 1.0, false, true, 0.001, 10.0)

Complete Example: X Gate on 3-Level Transmon

# 1. Create multilevel system
sys = TransmonSystem(levels = 3, δ = 0.2, drive_bounds = [0.4, 0.4])

# 2. Define embedded gate
U_goal = EmbeddedOperator(:X, sys)

# 3. Create trajectory
T, N = 20.0, 100
times = collect(range(0, T, length = N))
pulse = ZeroOrderPulse(0.05 * randn(2, N), times)
qtraj = UnitaryTrajectory(sys, pulse, U_goal)

# 4. Configure leakage suppression
opts = PiccoloOptions(
    leakage_cost = 5.0,
    leakage_constraint = true,
    leakage_constraint_value = 1e-3,
    verbose = false,
)

# 5. Solve
qcp = SmoothPulseProblem(qtraj, N; Q = 100.0, piccolo_options = opts)
cached_solve!(
    qcp,
    "leakage_suppression_2";
    max_iter = 150,
    verbose = false,
    print_level = 1,
)

fidelity(qcp)

0.14267269018434495

Manual Leakage Objectives and Constraints

For more control, add leakage handling manually using LeakageObjective and LeakageConstraint. Both take the leakage indices in the isomorphic vector space:

traj = get_trajectory(qcp)
leak_indices = get_iso_vec_leakage_indices(U_goal)

# Leakage objective (soft penalty)
leak_obj = LeakageObjective(leak_indices, :Ũ⃗, traj; Qs = fill(10.0, N))

# Leakage constraint (hard bound)
leak_constraint = LeakageConstraint(1e-3, leak_indices, :Ũ⃗, traj)

NonlinearKnotPointConstraint{DirectTrajOpt.Constraints.var"#32#33"{Piccolo.Control.QuantumConstraints.var"#leakage_constraint#7"{Float64, Vector{Int64}}}}(DirectTrajOpt.Constraints.var"#32#33"{Piccolo.Control.QuantumConstraints.var"#leakage_constraint#7"{Float64, Vector{Int64}}}(Piccolo.Control.QuantumConstraints.var"#leakage_constraint#7"{Float64, Vector{Int64}}(0.001, [3, 6, 9, 12, 13, 14, 16, 17])), [:Ũ⃗], false, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10  …  91, 92, 93, 94, 95, 96, 97, 98, 99, 100], [nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing  …  nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing], 8, 18, 800)

Strategies for Difficult Problems

1. Start Without Leakage Constraints

Get a working solution first, then add constraints:

# Step 1: Optimize without leakage constraints
qtraj2 = UnitaryTrajectory(sys, pulse, U_goal)
qcp_initial = SmoothPulseProblem(qtraj2, N; Q = 100.0)
cached_solve!(
    qcp_initial,
    "leakage_initial_2";
    max_iter = 100,
    verbose = false,
    print_level = 1,
)

# Step 2: Add leakage suppression
opts = PiccoloOptions(
    leakage_cost = 5.0,
    leakage_constraint = true,
    leakage_constraint_value = 1e-3,
    verbose = false,
)
qcp_leakage = SmoothPulseProblem(qtraj2, N; Q = 100.0, piccolo_options = opts)
cached_solve!(
    qcp_leakage,
    "leakage_with_suppression_2";
    max_iter = 150,
    verbose = false,
    print_level = 1,
)

fidelity(qcp_initial)

0.2536874122193978

fidelity(qcp_leakage)

0.2536874122193978

2. Increase Gate Time

Faster gates often have more leakage. Try a longer gate time if leakage is high.

3. Use More Levels

Include more levels to capture dynamics accurately:

sys_4level = TransmonSystem(levels = 4, δ = 0.2, drive_bounds = [0.2, 0.2])

QuantumSystem: levels = 4, n_drives = 2

4. Reduce Drive Amplitude

Lower drives can reduce leakage:

sys_low_drive = TransmonSystem(levels = 3, δ = 0.2, drive_bounds = [0.1, 0.1])

QuantumSystem: levels = 3, n_drives = 2

Analyzing Leakage

After solving, visualize the state evolution including leakage levels:

using CairoMakie

traj = get_trajectory(qcp)
fig = plot_unitary_populations(traj)