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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Related Experiment Video

Updated: Aug 17, 2025

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

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Quantum variational algorithms are swamped with traps.

Eric R Anschuetz1, Bobak T Kiani2

  • 1MIT Center for Theoretical Physics, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA. eans@mit.edu.

Nature Communications
|December 15, 2022
PubMed
Summary
This summary is machine-generated.

Variational quantum models, even shallow ones without barren plateaus, are often untrainable due to a scarcity of local minima. Noisy optimization of many quantum models is impossible with limited queries, hindering practical applications.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Optimization

Background:

  • Classical neural networks are highly trainable despite optimizing complex loss functions.
  • Variational quantum models often suffer from trainability issues, notably barren plateaus in deep models.

Purpose of the Study:

  • To investigate the trainability of variational quantum models beyond the phenomenon of barren plateaus.
  • To analyze the impact of local minima and noisy optimization on quantum model trainability.

Main Methods:

  • Proving trainability limitations for shallow variational quantum models lacking barren plateaus.
  • Analyzing quantum model trainability within a statistical query framework.
  • Numerical confirmation of theoretical results on various problem instances.

Main Results:

  • Shallow variational quantum models without barren plateaus have a superpolynomially small fraction of local minima near the global minimum, rendering them untrainable without good initial guesses.
  • Noisy optimization of a wide range of quantum models is infeasible with sub-exponential queries.

Conclusions:

  • Barren plateaus are not the sole cause of trainability issues in quantum models.
  • The trainability of shallow quantum models is severely limited by the landscape of local minima and query complexity.
  • While many quantum algorithms face challenges, specific variational algorithms may offer practical utility with further research.