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Updated: May 31, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Noisy quantum learning theory.

Jordan Cotler1,2, Weiyuan Gong3, Ishaan Kannan4

  • 1Department of Physics, Harvard University, Cambridge, Massachusetts, USA.

Nature Communications
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

Noise can erase quantum learning advantages, but new research introduces "noisy BQP" to model fault-tolerant quantum computers. This work explores how noise impacts quantum speedups in real-world experiments, guiding future research toward robust quantum advantages.

Related Experiment Videos

Last Updated: May 31, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Quantum Information Science
  • Computational Complexity Theory

Background:

  • Quantum learning algorithms often assume idealized, noiseless conditions.
  • Real-world quantum systems inevitably interact with noisy, uncharacterized environments.

Purpose of the Study:

  • To investigate the impact of noise on quantum learning speedups.
  • To introduce and analyze the complexity class "noisy BQP" (NBQP) for fault-tolerant quantum computers.
  • To identify conditions for achieving meaningful quantum advantages in noisy experimental settings.

Main Methods:

  • Introduction of the NBQP complexity class.
  • Analysis of noise effects on purity testing and Pauli tomography.
  • Derivation of lower bounds for sample complexity in noisy quantum learning tasks.
  • Investigation of noise-dependent limitations on quantum metrology.

Main Results:

  • Noise can eliminate exponential quantum advantages for idealized learners.
  • NBQP learners can be exponentially weaker than noiseless counterparts, but a superpolynomial gap persists between NISQ and fault-tolerant devices.
  • The exponential advantage for purity testing collapses under depolarizing noise.
  • Noise-dependent polynomial speedups are identified for Pauli tomography.
  • Physical structure can restore quantum speedups in specific scenarios.

Conclusions:

  • Quantum learning primitives are fragile to noise.
  • Realizing practical quantum advantages requires interfacing noise-robust physics with quantum algorithms.
  • Future quantum experiments must account for noise to achieve meaningful speedups.