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Qubit Types

Every platform has a calibration ceiling. Most stacks hit it and stop. Harmoniqs pushes through it.

Piccolo
Python
Calibration Protocol.jl
1using Piccolo
2using Random
3# Define system
4H_drift = PAULIS[:Z]
5H_drives = [PAULIS[:X], PAULIS[:Y]]
6sys = QuantumSystem(H_drift, H_drives, [1.0, 1.0])
7# Create trajectory
8T, N = 10.0, 100
9times = collect(range(0, T, length=N))
10pulse = ZeroOrderPulse(0.1 * randn(2, N), times)
11qtraj = UnitaryTrajectory(sys, pulse, GATES[:X])
12# Solve
13qcp = SmoothPulseProblem(qtraj, N; Q=100.0, R=1e-2)
14solve!(qcp, max_iter=100)
Calibration Protocol.py
1import pypiccolo
2import numpy as np
3# Define system
4H_drift = PAULIS['Z']
5H_drives = [PAULIS['X'], PAULIS['Y']]
6sys = QuantumSystem(H_drift, H_drives, [1.0, 1.0])
7# Create trajectory
8T, N = 10.0, 100
9times = np.linspace(0, T, N)
10pulse = ZeroOrderPulse(0.1 * np.random.randn(2, N), times)
11qtraj = UnitaryTrajectory(sys, pulse, GATES['X'])
12# Solve
13qcp = SmoothPulseProblem(qtraj, N, Q=100.0, R=1e-2)
14solve(qcp, max_iter=100)
CAT
Neutral Atoms
Trapped Ions
Photonics
Silicon
Superconducting
CAT Qubits

Stabilization eats your coherence budget

Bias advantage gone by the gate layer

Kerr and dispersive shifts drift every run

Leakage caps logical fidelity

Encoding change = full stack rewrite

Prep fidelity craters on real hardware

Harmoniq’s Piccolo Ecosystem has… 
1

Advanced calibration automation

2

Improved gate sequencing efficiency

3

Adaptive drift compensation

4

Potential to reduce manual retuning cycles

Cat Qubits CTA

The Piccolo ecosystem builds on robotics-grade control theory and published research. Let’s explore how Piccolo and Piccolismo can scale your experiments and future publications.
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Neutral Atoms

Fidelities plateau below roadmap targets

Position jitter caps gate fidelity

Array reshuffle restarts calibration

Blockade regime forces speed tradeoffs

Every site drifts differently

Demo cycles stall on calibration, not physics

Harmoniq’s Piccolo Ecosystem has… 
1

Real-time adaptive calibration

2

Noise compensation across distributed qubit arrays

3

Intelligent optimization of control sequences

4

Potential to improve stability windows for larger atom arrays

Neutral Atoms CTA

The Piccolo ecosystem builds on robotics-grade control theory and published research. Let’s explore how Piccolo and Piccolismo can scale your experiments and future publications.
Explore collaboration
SCALE WITH PICCOLISIMO
Trapped Ions

Gate fidelity has no margin left

Chain scaling multiplies calibration load

Slow gates cap circuit depth

Raman stability is a full-time job

Spectator modes leak into every gate

New gate scheme rebuilds the stack

Harmoniq’s Piccolo Ecosystem has… 
1

Optimization of gate timing

2

Drift reduction

3

Fidelity improvements

4

Reduction in operational costs via improved calibration cycles

Trapped Ions CTA

Need Text for this
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Photonics Qubits

Phase drift never stabilizes

Characterization takes weeks per chip

Thermal drift shifts baselines between runs

Every chip needs a fresh control map

Mode matching consumes the team's quarter

Harmoniq’s Piccolo Ecosystem has… 
1

Control optimization for photonic experiments

2

Real-time correction of optical instability

3

Calibration support for lab-scale systems

Silicon Spin Qubits

No two devices share a calibration

Charge noise kills long gates

On-chip wiring constrains pulse shaping

Calibration doesn't transfer, doesn't scale

New wafer wipes the control library

Harmoniq’s Piccolo Ecosystem has… 
1

Adaptive calibration for chip-embedded qubits

2

Integration into semiconductor control stacks

3

Potential for scalable control intelligence at fabrication level

Superconducting Qubits

TLS defects reshuffle every cooldown

Cross-resonance drift erodes yields

DAC limits constrain pulses before physics does

Transmon + cavity compounds at scale

Leakage undermines error correction

Each chip launch restarts calibration

Harmoniq’s Piccolo Ecosystem has… 
1

Hardware-validated at 98.8% fidelity

2

Handles transmon + bosonic oscillator (multi-mode) Hamiltonians

3

Compatible with 16 GSa/s DAC hardware out of the box

The Solution

‘Piccolo.jl is a meta-package for quantum optimal control using the Pade Integrator Collocation (Piccolo) method. This package reexports the following packages

QuantumCollocation.jl

NamedTrajectories.jl

TrajectoryIndexingUtils.jl

PiccoloQuantumObjects.jl

PiccoloPlots.jl

How are we different?

Robotics and Aerospace Algorithms

Piccolo uses proven algorithms from robotics and aerospace fields that have mastered the design of precision control under uncertainty.

Learn more

Real-time Software Design

Users can design control sequences, calibrate in situ, and compensate for noisy, drifting hardware in real time.

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One tool. Five platforms.

Whether you're running superconducting qubits, neutral atoms, or cold atom lattices — Piccolo works on your hardware.
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