Control

Quantum Computers are difficult to control. Error rates remain unstable without continuous tuning – High error rates become both a hardware and real-time control problem

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)

Harmoniqs Piccolo and Piccolismo

Metrics

1.67

µs y-gate duration

Free-time optimization: pulse duration found automatically — no prior knowledge needed

Minimum-time formulation: minimizes pulse length while enforcing a fidelity floor (F ≥ F̄)

Constraint enforcement: hard bounds on pulse amplitude, slew, and acceleration

Infeasible-starts: solver escapes local minima that trap GRAPE/CRAB

Smooth pulses: piecewise linear/cubic spline, validated on hardware

Complex-controls: Handles nonlinear Hamiltonians (not just linear-in-controls)

Harvard

Make Your Quantum Hardware Do More Than It Was Designed To Do.

Most quantum hardware gives you a fixed set of native interactions. Our control technology lets you engineer effective interactions the hardware doesn't natively support — without new chips, new lasers, or new atoms. Simply with smarter pulses.

We recently helped prove this is possible. Harmoniqs and our collaborators co-authored a proof that global pulse control (with symmetry breaking) is sufficient for universal analog quantum simulators. Then we built the stack that proves it works.

Piccolo.jl delivered 99.8% gate fidelity and the first experimental three-body interactions outside the blockade regime — outperforming 300 GRAPE runs on a hardware-constrained Rydberg problem. The same optimizer scales across Rydberg, trapped-ion, fermionic, and bosonic platforms.

What this means for you:

Get more out of existing hardware — no hardware changes required.

Unlock gate and simulation capabilities beyond your native gate set.
Replace brittle pulse optimization with methods that converge where standard tools stall.
Using pulse tooling that you can actually encode your hardware constraints and safety barriers that it will respect.

If you're pushing the limits of what your device can do, we'd like to talk.

Read the article

Shorter pulses = less decoherence = better fidelity.

Piccolo is the only open-source tool that finds the shortest pulse meeting your fidelity target — automatically.

Try Piccolo

Scale with Piccolismo

Control

Harmoniqs Piccolo and Piccolismo

Harvard

The Solution

How are we different?

Robotics and Aerospace Algorithms

Real-time Software Design

Shorter pulses = less decoherence = better fidelity.