Quantum speedup for track reconstruction in particle accelerators

Duarte, Magano,; Kumar, Ajay; Kālis, Mārtiņš; Locāns, Andris; Glos, Adam; Pratapsi, Sagar; Quinta, Gonçalo M.; Dimitrijevs, Maksims; Rivošs, Aleksander; Bargassa, P.; Seixas, J.; Ambainis, Andris; Omar, Yasser

doi:10.1103/physrevd.105.076012

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Several studies exist on the potential advantage of QC algorithms for track reconstruction. A complexity study [16] shows some degree of potential speedup in quantum search algorithms using a seeding/following approach. Alternatively, a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of the tracking problem and proof-of-principles have been built using quantum annealing [17] or a Variational Quantum Eigensolver (VQE) [18] to find solutions, obtaining reasonable tracking performance numbers but making no promise on HL-LHC conditions or timing improvements.…”

Section: Jinst 18 P11028mentioning

confidence: 99%

A quantum algorithm for track reconstruction in the LHCb vertex detector

Nicotra,

Lucio Martinez,

de Vries

et al. 2023

J. Inst.

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High-energy physics is facing increasingly demanding computational challenges in real-time event reconstruction for the near-future high-luminosity era. Using the LHCb vertex detector as a use case, we explore a new algorithm for particle track reconstruction based on the minimisation of an Ising-like Hamiltonian with a linear algebra approach. The use of a classical matrix inversion technique results in tracking performance similar to the current state-of-the-art but with worse scaling complexity in time. To solve this problem, we also present an implementation as a quantum algorithm, using the Harrow-Hassadim-Lloyd (HHL) algorithm: this approach can potentially provide an exponential speedup as a function of the number of input hits over its classical counterpart, in spite of limitations due to the well-known HHL Hamiltonian simulation and readout problems. The findings presented in this paper shed light on the potential of leveraging quantum computing for real-time particle track reconstruction in high-energy physics.

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Section: Jinst 18 P11028mentioning

confidence: 99%

A quantum algorithm for track reconstruction in the LHCb vertex detector

Nicotra,

Lucio Martinez,

de Vries

et al. 2023

J. Inst.

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“…Recent studies have highlighted guarantees regarding the expressivity, generalisation power, and trainability of quantum models [25][26][27][28][29][30]. Moreover, the efficacy of applying QML models to High Energy Physics (HEP) data analysis is exemplified in studies for classification [31][32][33][34][35][36], reconstruction [37][38][39], anomaly detection [40][41][42][43], and Monte Carlo integration [44,45]. A summary of advancements in QML applied to HEP is found in [46].…”

Section: Introductionmentioning

confidence: 99%

Guided quantum compression for high dimensional data classification

Belis,

Odagiu,

Grossi

et al. 2024

Mach. Learn.: Sci. Technol.

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Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers. Present quantum machine learning applications usually diminish this complexity by reducing the dimensionality of the data, e.g., via auto-encoders, before passing it through the quantum models. Here, we design a classical-quantum paradigm that unifies the dimensionality reduction task with a quantum classification model into a single architecture: the guided quantum compression model. We exemplify how this architecture outperforms conventional quantum machine learning approaches on a challenging binary classification problem: identifying the Higgs boson in proton-proton collisions at the LHC. Furthermore, the guided quantum compression model shows better performance compared to the deep learning benchmark when using solely the kinematic variables in our dataset.

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“…In this framework, high-energy physics represents an interesting testbed for quantum devices. On the one hand, quantum computation can be applied “downstream”, to optimize data analysis and event reconstruction from experiments [ 11 , 12 , 13 , 14 , 15 ]. On the other hand, the “upstream” investigation of gauge theories, especially in their lattice formulation [ 16 , 17 , 18 , 19 , 20 ], can benefit from the possibility of performing quantum simulations of regimes not achievable with perturbative techniques.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Quantum Phase Transitions of the Schwinger Model: Real-Time Dynamics on IBM Quantum

Pomarico

Cosmai

Facchi

et al. 2023

Entropy

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Simulating the real-time dynamics of gauge theories represents a paradigmatic use case to test the hardware capabilities of a quantum computer, since it can involve non-trivial input states’ preparation, discretized time evolution, long-distance entanglement, and measurement in a noisy environment. We implemented an algorithm to simulate the real-time dynamics of a few-qubit system that approximates the Schwinger model in the framework of lattice gauge theories, with specific attention to the occurrence of a dynamical quantum phase transition. Limitations in the simulation capabilities on IBM Quantum were imposed by noise affecting the application of single-qubit and two-qubit gates, which combine in the decomposition of Trotter evolution. The experimental results collected in quantum algorithm runs on IBM Quantum were compared with noise models to characterize the performance in the absence of error mitigation.

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Quantum speedup for track reconstruction in particle accelerators

Cited by 8 publications

References 27 publications

A quantum algorithm for track reconstruction in the LHCb vertex detector

A quantum algorithm for track reconstruction in the LHCb vertex detector

Guided quantum compression for high dimensional data classification

Dynamical Quantum Phase Transitions of the Schwinger Model: Real-Time Dynamics on IBM Quantum

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