Version Alpha Live

Piccolo Intonato

Online calibration of pulse shapes and control hardware for integration of the Piccolo ecosystem

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)

Quantum Pulse Calibration

Harmoniqs' robotics-inspired model-based hardware calibration techniques, honed for the quantum calibration problem domain. Piccolo Intonato corrects optimal control pulses from Piccolo and Piccolissimo to be as performant on hardware as possible.

Robotics-inspired model-based calibration adapted for quantum systems

Takes optimized pulses from Piccolo/Piccolissimo and corrects for real hardware imperfections

Bridges the sim-to-real gap at the hardware integration layer

Adapts in real time as hardware drifts

Advancing Quantum Research Together

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

Piccolo Intonato

Quantum Pulse Calibration

The Solution

How are we different?

Robotics and Aerospace Algorithms

Real-time Software Design

Advancing Quantum Research Together