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Bringing together the best technologies to help you get the most from your quantum hardware
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Q-CTRL provides easy export functionality in python to build compatibility with Qiskit and the analog-layer programming framework Qiskit Pulse. Error-robust quantum logic, hardware characterization and calibration, and even complex machine learning routines can be executed using analog-level programming via Qiskit Pulse. Using this framework we've demonstrated up to 10X performance improvements in IBM hardware.


Combining the “what” with the “how” in experimental quantum control. Q-CTRL’s quantum firmware, hardware characterization routines, and machine-learning packages for hardware optimization connect directly with QM’s Quantum Orchestration platform (QOP) via a custom integration package written in the intuitive QUA language. Users can now exploit real-time processing for ultra-high-speed experimental iteration, automated calibration, and closed-loop optimization to remove one of the biggest bottlenecks in the field: time.


The qctrl-pyquil convenience package provides export functions to PyQuil and permits the execution of Q-CTRL-derived sequences on quantum hardware provided by Rigetti's Quantum Cloud Service. Our custom translation layer ensures that the integrity of the sequence structure and timing is preserved within approximations made in sequencing. And with access to quil-t you can execute custom controls and novel gates through analog-layer programming to take full control.


Get the best of both Q-CTRL and QuTiP through native support for QuTiP objects as Boulder Opal graph element
Q-CTRL provides solutions which can be integrated into any experimental control stack. At its most basic level, this involves converting waveforms described in software into physical outputs from hardware signal generators such as arbitrary waveform generators (AWGs), direct digital synthesizers (DDSs), and vector signal generators (VSGs). Q-CTRL provides a range of pre-built formatting scripts to translate control output into machine compatible formats (e.g. CSV or JSON), and provides a wide range of tools for efficient hardware calibration.
Seamlessly integrate into any experiment
Whether you're designing a quantum stack from the ground up or already running advanced experiments, Q-CTRL can connect directly to your system.
Coded in Python, our tools smoothly integrate with any experimental software stack and allow you to control and characterize your quantum hardware. And with full customization available, our Last-Mile-Integration package lets you automate the most challenging tasks in quantum control for your system.
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