Source-linked AI summary
Quantum neuromorphic computing
Danijela Marković, Julie Grollier
TL;DR
Useful quantum advantage remains elusive, while noise limits the depth of circuits available on current NISQ devices. This perspective organizes quantum neuromorphic approaches into digital parametrized circuits and analog systems that use quantum dynamics, and concludes that data conversion costs constrain potential speedups.
Problem
Useful-task quantum advantage remains elusive, and noise in NISQ devices limits realizable circuit depth.
Source: pp. 1–2Method
The perspective frames digital parametrized circuits and analog quantum dynamics as two approaches to implementing neural networks on quantum hardware.
Source: pp. 3–4Results
Classical-data encoding and quantum-state reconstruction can each require exponentially many operations, compromising the advantage of quantum systems’ large state space.
Source: p. 6Takeaways & Limitations
The review identifies implicit unsupervised learning on classical data and supervised or unsupervised learning on quantum data as likely routes around the quantum-to/from-classical bottleneck.
Source: p. 6Supervised parametrized-circuit experiments have used simple or scaled-down datasets because NISQ devices have few qubits and limited connectivity.
Source: p. 3
Abstract
from arXiv · showhide
Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits, and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and compute. We discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.