Determining the proton content with a quantum computer

Pérez-Salinas, Adrián; Cruz-Martinez, Juan; Alhajri, Abdulla; Carrazza, Stefano

doi:10.1103/physrevd.103.034027

Cited by 36 publications

(32 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Achieving a quantum advantage requires extending the approach to multi-qubit circuits. For instance, one can implement the gate set defined in this work to apply on several different qubits, and then add entangling gates, see [5,6]. Whether this extension to multi-qubit circuits is more flexible than the approach of this work remains unclear.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, this algorithm should be regarded as a subroutine to be included in a more complex computation. For instance, this subroutine could play the role of a classifier [5] or an approximant of unknown functions [6]. These works also suggest the use of similar approaches extended to circuits with many qubits.…”

Section: Introductionmentioning

confidence: 99%

“…The query complexity of the process increases linearly with the number of neurons. This observation is critical to find out an equivalent result in a quantum formulation in order to support progress in quantum machine learning [5][6][7][8][9][10][11][12][13]. Previous works have already made relevant contributions in this area [14,15], in particular assessing the universal expressive power of quantum circuits.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

One qubit as a universal approximant

et al. 2021

Self Cite

View full text Add to dashboard Cite

A single-qubit circuit can approximate any bounded complex function stored in the degrees of freedom defining its quantum gates. The single-qubit approximant presented in this work is operated through a series of gates that take as their parameterization the independent variable of the target function and an additional set of adjustable parameters. The independent variable is re-uploaded in every gate while the parameters are optimized for each target function. The output state of this quantum circuit becomes more accurate as the number of re-uploadings of the independent variable increases, i. e., as more layers of gates parameterized with the independent variable are applied. In this work, we provide two different proofs of this claim related to both Fourier series and the Universal Approximation Theorem for Neural Networks, and we benchmark both methods against their classical counterparts. We further implement a single-qubit approximant in a real superconducting qubit device, demonstrating how the ability to describe a set of functions improves with the depth of the quantum circuit. This work shows the robustness of the re-uploading technique on Quantum Machine Learning.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

One qubit as a universal approximant

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…As an example, a recent study [84] presents an algorithm for computing predictions of parton distribution functions and the hadronic tensor, where it is also speculated that "quantum supremacy" could possibly be demonstrated in this area of research as such a task has proven very difficult with classical computers. As another example, a recent study has presented a proof-of-principle demonstration of the determination of proton PDFs with a quantum computer [85], laminating the way for further applications to, for example, protons bound in a nucleus. These new methods and new technologies will be most relevant when aiming at the highest possible perturbative precision, and we are confident that our NNLO analysis will provide a solid reference for such future studies.…”

Section: Discussionmentioning

confidence: 99%

TUJU21: NNLO nuclear parton distribution functions with electroweak-boson production data from the LHC

Helenius,

Walt,

Vogelsang

2021

Preprint

View full text Add to dashboard Cite

We present new sets of nuclear parton distribution functions (nPDFs) at next-to-leading order and next-to-next-to-leading order in perturbative QCD. Our analyses are based on deeply inelastic scattering data with charged-lepton and neutrino beams on nuclear targets, and experimental data from measurements of W ± , Z boson production in p+Pb collisions at the LHC. In addition, a set of proton baseline PDFs is fitted within the same framework and with the same theoretical assumptions. The results of our global QCD analysis are compared to existing nPDF sets and to the previous nPDF set TUJU19 which was based on DIS data only. Our work is performed using an open-source tool, xFitter, and the required extensions of the code are discussed as well. We find good agreement with the data included in the fit and a lower value for χ 2 /N dp when performing the fit at next-to-next-to-leading order. We apply the resulting nuclear PDFs to electro-weak boson production in Pb+Pb collisions at the LHC and compare the results to the most recent data from ATLAS and CMS.

show abstract

“…Quantum algorithms have recently started to come under the spotlight in high-energy physics given the stringent demands that the field [8] will meet in the coming Run 3 of the CERN's Large Hadron Collider (LHC), the posterior highluminosity phase [9], and the planned future colliders [10][11][12][13]. Recent applications include the speed up of jet clustering algorithms [14][15][16], jet quenching [17], determination of parton densities [18], simulation of parton showers [19][20][21], heavyion collisions [21], quantum machine learning [22][23][24] and lattice gauge theories [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Quantum algorithm for Feynman loop integrals

Ramírez-Uribe,

Rentería-Olivo,

Rodrigo

et al. 2021

Preprint

View full text Add to dashboard Cite

We present the first flagship application of a quantum algorithm to Feynman loop integrals. The two onshell states of a Feynman propagator are identified with the two states of a qubit and a quantum algorithm is used to unfold the causal singular configurations of multiloop Feynman diagrams. Since the number of causal states to be identified is nearly half of the total number of states in most cases, an efficient modification of Grover's algorithm is introduced, requiring only O(1) iterations. The output of the quantum algorithm in the IBM quantum simulator is used to bootstrap the causal representation in the loop-tree duality of representative multiloop topologies. The algorithm may also find application and interest in graph theory to solve problems involving directed acyclic graphs.

show abstract

Determining the proton content with a quantum computer

Cited by 36 publications

References 51 publications

One qubit as a universal approximant

One qubit as a universal approximant

TUJU21: NNLO nuclear parton distribution functions with electroweak-boson production data from the LHC

Quantum algorithm for Feynman loop integrals

Contact Info

Product

Resources

About