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Version 1 Beta Live
Piccolissimo
Proprietary, cloud-based high-performance solution for large-scale, complex real-world problems
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)Scalable Quantum Solver
Harmoniqs' proprietary high-performance solver layer. Purpose-built for quantum optimal control at scales where generic solvers hit a wall — more qubits, more levels, larger Hilbert spaces — without sacrificing physical accuracy or gate fidelity.
Extends Piccolo for large-scale, complex real-world quantum problems
Handles larger Hilbert spaces and higher qubit counts than generic solvers
Advanced integrators for high-accuracy dynamics simulation
Built on HPC infrastructure
Advancing Quantum Research Together
Our work builds on robotics-grade control theory and published research. Let’s explore how Piccolo can support your experiments and future publications.
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