Single-particle digitization strategy for quantum computation of a $ϕ^4$ scalar field theory

Barata, João; Mueller, Niklas; Tarasov, Andrey; Venugopalan, Raju

doi:10.48550/arxiv.2012.00020

Cited by 3 publications

(6 citation statements)

References 153 publications

(299 reference statements)

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“…In an actual implementation, one would have to consider signed values, since, in general, there is no condition that physically constrains the system to positive values. In principle, signed values can be dealt with by, for example, including an extra qubit that stores the sign of the state (similar to the encoding used in [25]) or using a two's complement encoding. This caveat requires one to modify circuits we detail in the main text to accommodate for the new encodings.…”

Section: Discussionmentioning

confidence: 99%

“…However, including a localized initial state distribution might be important for certain digitizations where one can not prepare the state |p = 0 exactly or if one is simply interested in studying how different production mechanisms influence the final state. Several strategies to prepare |ψ 0 from an integrable template distributions can be found in the literature [25][26][27][28]. Depending exactly on what |ψ 0 one wants to prepare, in principle, one can devise a routine which only requires O(n Q ) basic quantum gate operations.…”

Section: Initial State Preparationmentioning

confidence: 99%

“…The value of the jet quenching parameters q varies drastically between different experimental set-ups, due to the different energy scales being explored. To bridge RHIC, LHC and EIC experimental conditions, we assume that O(0.1 GeV 2 fm −1 ) ≤ q ≤ O(10 GeV 2 fm −1 ) [25,42,43]. The saturation scale Q 2 s is then approximately bounded by…”

Section: Numerical Estimates For the Circuit Parametersmentioning

confidence: 99%

“…with µ an infrared (model dependent) regulator, related to the medium Debye mass; typically µ ∼ O(0.1 − 1 GeV) [25,44,45] and we used the previous estimates to reduce the problem to the ratio between the soft and hard scales at play. Recalling that N s = 2 n Q , we obtain…”

Section: Numerical Estimates For the Circuit Parametersmentioning

confidence: 99%

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A quantum strategy to compute the jet quenching parameter $\hat{q}$

Barata¹,

A.²

2021

Preprint

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Jet quenching, the modification of the properties of a QCD jet when the parton cascade takes place inside a medium, is an intrinsically quantum process, where color coherence effects play an essential role. Despite a very significant progress in the last years, the simulation of a full quantum medium induced cascade remains inaccessible to classical Monte Carlo parton showers. In this situation, alternative formulations are worth being tried and the fast developments in quantum computing provide a very promising direction. The goal of this paper is to introduce a strategy to quantum simulate single particle momentum broadening, the simplest building block of jet quenching. Momentum broadening is the modification of the quark or gluon transverse momentum due interactions with the underlying medium, modeled as a QCD background field. At the lowest order in α s that we consider here, momentum broadening does not involve parton splittings and particle number is conserved, greatly simplifying the quantum algorithmic implementation. This quantity is, however, very relevant for the phenomenology of RHIC, LHC or the future EIC.

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Section: Discussionmentioning

confidence: 99%

Section: Initial State Preparationmentioning

confidence: 99%

Section: Numerical Estimates For the Circuit Parametersmentioning

confidence: 99%

Section: Numerical Estimates For the Circuit Parametersmentioning

confidence: 99%

See 2 more Smart Citations

A quantum strategy to compute the jet quenching parameter $\hat{q}$

Barata¹,

A.²

2021

Preprint

Self Cite

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“…The relative merits of each are only starting to be understood. The question of gauge boson digitization is murkier, with complicated tradeoffs [30][31][32][33][34][35][36][37][38]. Digitizing reduces symmetries -either explicitly or through finitetruncations [39].…”

Section: Introductionmentioning

confidence: 99%

Lattice Renormalization of Quantum Simulations

Carena,

Lamm,

et al. 2021

Preprint

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With advances in quantum computing, new opportunities arise to tackle challenging calculations in quantum field theory. We show that trotterized time-evolution operators can be related by analytic continuation to the Euclidean transfer matrix on an anisotropic lattice. In turn, trotterization entails renormalization of the temporal and spatial lattice spacings. Based on the tools of Euclidean lattice field theory, we propose two schemes to determine Minkowski lattice spacings, using Euclidean data and thereby overcoming the demands on quantum resources for scale setting. In addition, we advocate using a fixed-anisotropy approach to the continuum to reduce both circuit depth and number of independent simulations. We demonstrate these methods with qiskit noiseless simulators for a 2 + 1D discrete non-Abelian D4 gauge theory with two spatial plaquettes.

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State Preparation in the Heisenberg Model through Adiabatic Spiraling

Ciavarella

Caspar

Illa

et al. 2023

Quantum

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An adiabatic state preparation technique, called the adiabatic spiral, is proposed for the Heisenberg model. This technique is suitable for implementation on a number of quantum simulation platforms such as Rydberg atoms, trapped ions, or superconducting qubits. Classical simulations of small systems suggest that it can be successfully implemented in the near future. A comparison to Trotterized time evolution is performed and it is shown that the adiabatic spiral is able to outperform Trotterized adiabatics.

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Single-particle digitization strategy for quantum computation of a $ϕ^4$ scalar field theory

Cited by 3 publications

References 153 publications

A quantum strategy to compute the jet quenching parameter $\hat{q}$

A quantum strategy to compute the jet quenching parameter $\hat{q}$

Lattice Renormalization of Quantum Simulations

State Preparation in the Heisenberg Model through Adiabatic Spiraling

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