Adaptive Quantum Circuites (AQCs) – means having pulses, readout, thresholds, other circuit parameters and control flow dynamically modified, in real-time, based on computation and outcomes of mid-circuit measurements, allowing for unlimited branching and fully adaptive quantum-classical circuits.
Feedback and Feed-Forward are generally composed of three steps: (1) qubit readout or other input from the quantum system; (2) real-time ultra-fast computation and decision making; (3) dynamical and parametric generation of response drive. The Pulse Processing Unit (PPU) of the OPX does all three, offering arbitrary feedback capabilities and the most advanced quantum controller in the industry.
Quantum feedback, in its most arbitrary form, allows utilizing and reacting in real-time to information processed from measurements. This requires a high level of integration between classical and quantum resources. Here we focus on quantum real-time (QRT) latency benchmarks, which refer to the delay required for operations that must occur faster than qubit decoherence. We present the numbers for our Quantum Machines’ OPX controllers. To learn more about the control benchmarking system we use click below.
OPX+ controller’s architecture and its QUA programming language, allow for intuitive programming of comprehensive feedback. The PPU responds to measurements, performs calculations and orchestrates the experiment’s control flow and qubit drives based on measurements. As a result, OPX can do much more than the traditional “play/no play” feedback or two-paths-only branching. It easily supports the implementation of advanced dynamic circuits right out of the box. Here we show OPX+ latency for a few QRT feedback benchmarks, such as Active Reset using standard feedback in under 250 ns and π-pulse calibration under 300 ns (numbers for analog-to-analog, while analog-to-digital requires half the latency). Read below for an explanation of the protocols.
A pulse is either generated or not, according to the Boolean result of classical processing of measurement results. From the moment the last point of a measurement input pulse comes to the controller, OPX processing unit performs state discrimination and decision making, and then produces a reset pulse conditionally on the qubit being in the excited state. This takes 224 ns for one qubit, the fastest arbitrary analog-to-analog feedback in the industry. Easily extendable to any number of qubits simultaneously, and can be performed aggregating or distributing the processing.
#Ultra-fast Active Reset
play('pi', 'qubit', condition = I > threshold)
OPX can change the flow of the program in real-time based on the classical processing of measurement results. The ability to respond to events with real-time decision making based on measurements allows for repeat-until-success (RUS) sequences, where the controller responds dynamically to events as they unfold, in a non-deterministic process. For example, RUS active reset allows to continuously reset until the qubit is in the required state with a given probability, to perform experimental shots always in the required conditions without wasting time.
#Repeat-until-success Active Reset
with while_(I > threshold):
play('pi', 'qubit')
measure('readout', 'resonator', ..., I, Q)The OPX can dynamically change parameters (e.g. amplitude, frequency, phase, chirp, threshold, etc.) of a pulse-level operation based on processed measurement results. For example, in a π-pulse calibration, we instruct the pulse processor to change the amplitude of a the pulse dynamically, responding to a measurement, comparing the result to a previous value. Parametric changes are done with ultra-low latency, improving repetition rates of experiments, calibration rates and reducing overhead.
#Dynamic π-pulse Calibration
measure('readout', 'resonator', ..., I, Q)assign(A, 1+slope*(I - I0)) play('pi'*amp(A), 'qubit')
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2022, what a year it has been! As the research in quantum computing continues to increase ...
Speed-up protocols with loops and dynamic circuits running on the lowest level of the hardware, without uploading or memory issues.
Calibrate coefficients and parameters in real-time to achieve the highest operation fidelities keeping the up-time high.
Track important parameters dynamically and with the lowest latencies, to mitigate errors and drifts. For example, update the qubit drive frequency every 500 ns.
Scale up your system seamlessly, without changes of codes and marginal latency buildup. Your flexible quantum control solution at any scale.
Finally, a Practical Way to Benchmark Quantum Controllers
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