Quantum computing for fluids: Where do we stand?

Succi, Sauro; Itani, W.; Sreenivasan, K.; Steijl, R.

doi:10.1209/0295-5075/acfdc7

Cited by 21 publications

(1 citation statement)

References 35 publications

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“…Quantum technologies promise increased capabilities in a number of areas, such as metrology, [ 1 ] cryptography, [ 2,3 ] physical simulations, [ 4 ] and computational fluid dynamics. [ 5 ] However, to see an advantage in many of these applications requires that the quantum device operate fault tolerantly and at a large scale, [ 6 ] a challenge that is unlikely to be met with current architectures within the next 5–10 years. Recently, noisy intermediate‐scale quantum (NISQ) devices [ 7 ] have received significant attention, with much effort expended to show near‐term quantum advantage.…”

Section: Introductionmentioning

confidence: 99%

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Hanks,

Lee,

Kim

2024

Advanced Physics Research

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The effective use of noisy intermediate‐scale quantum devices requires error mitigation to improve the accuracy of sampled measurement distributions. The more accurately the effects of noise on these distributions can be modeled, the more closely error mitigation will be able to approach theoretical bounds. The characterization of noisy quantum channels and the inference of their effects on general observables are challenging problems, but in many cases a change in representation can greatly simplify the analysis. Here, spin Wigner functions for multiqudit systems are investigated. This study generalizes previous kernel constructions, capturing the effects of several probabilistic unitary noise models in few parameters.

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Section: Introductionmentioning

confidence: 99%

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Hanks,

Lee,

Kim

2024

Advanced Physics Research

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Quantum-enriched large-eddy simulation with the incompressible Schrödinger flow

Han,

Yang

2024

Acta Mech. Sin.

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Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits

Siegl,

Wassing,

Mieth

et al. 2024

CEAS Aeronaut J

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Quantum circuits with trainable parameters, paired with classical optimization routines can be used as machine learning models. The recently popularized physics-informed neural network (PINN) approach is a machine learning algorithm that solves differential equations by incorporating them into a loss function. Being a mesh-free method, it is a promising approach for computational fluid dynamics. The question arises whether the properties of quantum circuits can be leveraged for a quantum physics-informed machine learning model. In this study, we compare the classical PINN-ansatz and its quantum analog, which we name the physics-informed quantum circuit (PIQC). The PIQC simulations are performed on a noise-free quantum computing simulator. Studying various differential equations, we compare expressivity, accuracy and convergence properties. We find that one-dimensional problems, such as the linear transport of a Gaussian-pulse or Burgers’ equation, allow a successful approximation with the classical and the quantum ansatz. For these examples, the PIQC overall performs similarly to PINN and converges more consistently and for Burgers’ equations even faster. While this is promising, the chosen quantum circuit approach struggles to approximate discontinuous solutions which the classical PINN-ansatz can represent. Based on this comparison, we extrapolate that the required number of qubits for solving two-dimensional problems in aerodynamics may already be available in the next few years. However, the acceleration potential is currently unclear and challenges like noisy circuits and approximations of discontinuous solutions have to be overcome.

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Quantum computing for fluids: Where do we stand?

Abstract: We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical ﬂuids. Diﬀerent strategies, along with their potential merits and liabilities, are discussed and commented on.

Cited by 21 publications

References 35 publications

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Quantum-enriched large-eddy simulation with the incompressible Schrödinger flow

Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits

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Quantum computing for fluids: Where do we stand?

Abstract: We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical ﬂuids. Diﬀerent strategies, along with their potential merits and liabilities, are discussed and commented on.&#xD;

Cited by 21 publications

References 35 publications

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Noise‐Tailored Constructions for Spin Wigner Function Kernels

Quantum-enriched large-eddy simulation with the incompressible Schrödinger flow

Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits

Contact Info

Product

Resources

About

Abstract: We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical ﬂuids. Diﬀerent strategies, along with their potential merits and liabilities, are discussed and commented on.