Just-in-time Quantum Circuit Transpilation Reduces Noise

Abstract

Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not "created equal", but their noise level also changes over time. IBM is said to calibrate their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point.This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that justin-time noise measurements benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM's default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.

Calculating spin correlations with a quantum computer

“…Readout errors, gate errors, and decoherence all affect experimental results. 20 Measured results can be compared with a noise model 21 if desired. A further complication is the "transpiling" of quantum circuit diagrams into gates that are available in the hardware.…”

“…The rotation operators effect a change of basis, as shown in Eq. (20). The symbols on the far right represent measurements in the computational basis.…”

“…The broken line separates the creation of the state from the measurement process. The rotation operators effect a change of basis, as shown in Eq (20)…”

Machine Learning Optimization of Quantum Circuit Layouts

“…One of the greatest limitations of NISQ devices is the noise level, such that only shallow quantum circuits can be executed within an acceptable error rate. Very recent work presents worrisome evidence that even very small and shallow circuits are intrinsically difficult to execute on NISQ [21]. Thus, one approach is to partition a circuit such that the high-depth circuit execution is circumvented [22].…”

A QUBO Formulation for Qubit Allocation

“…Other approaches use machine-learning techniques to optimize circuit synthesis [25,26]. Recently, a number of methods have focused on incorporating hardware calibration data in an effort to improve the final circuit fidelities [27][28][29][30][31][32] .…”