Abstract
We show a pulse-efficient circuit transpilation framework for noisy quantum hardware. This is achieved by scaling cross-resonance pulses and exposing each pulse as a gate to remove redundant single-qubit operations with the transpiler. Crucially, no additional calibration is needed to yield better results than a CNOT-based transpilation. This pulse-efficient circuit transpilation therefore enables a better usage of the finite coherence time without requiring knowledge of pulse-level details from the user. As demonstration, we realize a continuous family of cross-resonance-based gates for SU(4) by leveraging Cartan's decomposition. We measure the benefits of a pulse-efficient circuit transpilation with process tomography and observe up to a 50% error reduction in the fidelity of ${R}_{ZZ}(\ensuremath{\theta})$ and arbitrary SU(4) gates on IBM Quantum devices. We apply this framework for quantum applications by running circuits of the quantum approximate optimization algorithm applied to MAXCUT. For an 11-qubit nonhardware native graph, our methodology reduces the overall schedule duration by up to 52% and errors by up to 38%.
Highlights
Quantum computers have the potential to impact a broad range of disciplines such as quantum chemistry [1], finance [2,3], optimization [4,5], and machine learning [6,7]
V we demonstrate the advantage of our pulse-efficient transpilation by applying it to the quantum approximate optimization algorithm (QAOA) [4]
We investigate the effect of the pulse scaling methodology with quantum process tomography by carefully benchmarking scaled RZZ (θ ) gates [see Fig. 1(a)] with respect to the double-CNOT decomposition; see Fig. 1(b)
Summary
Quantum computers have the potential to impact a broad range of disciplines such as quantum chemistry [1], finance [2,3], optimization [4,5], and machine learning [6,7]. Aggregating instructions and optimizing the corresponding pulses, using, e.g., gradient ascent algorithms such as GRAPE [24], reduces the duration of the pulse schedules [25] Such pulses require calibration to overcome model errors [26,27], which typically needs closed-loop optimization [28,29] and sophisticated readout methods [30,31]. This may be difficult to scale because calibration is time consuming and increasingly harder as the control pulses become more complex.
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