A Depolarizing Noise-aware Transpiler for Optimal Amplitude Amplification
Debashis Ganguly, Wonsun Ahn

TL;DR
This paper introduces a noise-aware transpiler for quantum amplitude amplification that predicts optimal stopping points to maximize accuracy in NISQ devices by using Bayesian analysis of gate noise.
Contribution
It presents a novel transpiler extension that predicts amplification accuracy and halts amplification at the optimal point without circuit execution.
Findings
Accurately predicts the inflection point for amplitude amplification.
Optimizes circuit to maximize accuracy given noise parameters.
Reduces unnecessary amplification to improve results.
Abstract
Amplitude amplification provides a quadratic speed-up for an array of quantum algorithms when run on a quantum machine perfectly isolated from its environment. However, the advantage is substantially diminished as the NISQ-era quantum machines lack the large number of qubits necessary to provide error correction. Noise in the computation grows with the number of gate counts in the circuit with each iteration of amplitude amplification. After a certain number of amplifications, the loss in accuracy from the gate noise starts to overshadow the gain in accuracy due to amplification, forming an inflection point. Beyond this point, accuracy continues to deteriorate until the machine reaches a maximally mixed state where the result is uniformly random. Hence, quantum transpilers should take the noise parameters of the underlying quantum machine into consideration such that the circuit can be…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
