Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation
Shin Nishio, Yulu Pan, Takahiko Satoh, Hideharu Amano, and Rodney Van, Meter
TL;DR
This paper presents an error-aware compilation method for IBM's 20-qubit quantum computers that improves success probability and reduces error, using a fidelity-based metric and beam search heuristic.
Contribution
It introduces a novel compilation approach that accounts for qubit error rates and employs a beam search heuristic to optimize circuit placement on NISQ devices.
Findings
Increased estimated success probability of quantum circuits.
Reduced KL-divergence indicating closer approximation to ideal distributions.
Effective error-aware compilation on real IBM quantum hardware.
Abstract
NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation to achieve meaningful computation. Our compilation tool development focuses on the fact that the error rates of individual qubits are not equal, with a goal of maximizing the success probability of real-world subroutines such as an adder circuit. We begin by establishing a metric for choosing among possible paths and circuit alternatives for executing gates between variables placed far apart within the processor, and test our approach on two IBM 20-qubit systems named Tokyo and Poughkeepsie. We find that a single-number metric describing the fidelity of individual gates is a useful but imperfect guide. Our compiler uses this subsystem and maps complete circuits onto the machine using a beam search-based heuristic that will scale as processor and program sizes grow. To evaluate the whole compilation process, we…
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.