Abstract
Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity. However, the highly detailed system characterization required to understand the underlying error sources is an arduous process and impractical with increasing chip size. Open-loop optimal control techniques allow for the improvement of gates but are limited by the models they are based on. To rectify the situation, we provide an integrated open-source tool-set for Control, Calibration and Characterization, capable of open-loop pulse optimization, model-free calibration, model fitting and refinement. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget, all based on a high-performance, feature-rich simulator. We illustrate our methods using simulated fixed-frequency superconducting qubits for which we learn model parameters with less than 1% error and derive a coherence limited cross-resonance (CR) gate that achieves 99.6% fidelity without need for calibration.
Highlights
Efforts to scale-up quantum computation have reached a point where the principal limiting factor is not the number of qubits, but the entangling gate infidelity
The model learned using only single-qubit data is sufficient to accurately predict the performance of the two-qubit gate on the device. We suspect this to be caused by exchange interactions due to coupling and finite temperature: Even when performing only single-qubit gates, the finite temperature causes a partial excitation of higher states, which are exchanged with the other qubit via the coupling and visible in the optimized randomized benchmarking for immediate tune up (ORBIT) data
We investigate whether the Gaussian ansatz is limiting gate fidelities by further refining the optimal pulses using a piecewise constant (PWC) optimization with one pixel per arbitrary waveform generator (AWG) sample
Summary
Scaling up quantum processing units (QPUs) is a monumental task that requires the community to make progress on multiple fronts, most importantly improving gate fidelities and increasing the number of qubits. If a Good Model is known, gates generated by open-loop optimal control will, by definition, work on the experiment, not requiring further closedloop calibration. This enables the use of complex pulses that would otherwise require time-consuming calibration. To characterize the system and provide us with a Good Model, we introduce C3, a tool to optimize model parameters by comparing model prediction to experimental data We refer to this task as model learning.
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