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arXiv:2610.37040

Quantum Annealing for Gamma-ray Source Detection and Localization with AGILE Data

Alessandro Rizzo, Nicolò Parmiggiani, Andrea Bulgarelli, Gabriele Panebianco, Carlo Burigana, Vincenzo Fabrizio Cardone, Farida Farsian, Massimo Meneghetti, Giuseppe Murante, Giuseppe Sarracino, Roberto Scaramella, Francesco Schillirò, Vincenzo Testa, Tiziana Trombetti, Fabio Roberto Vitello

quant-phastro-ph.HEastro-ph.IMSubmitted Oct 27, 2026• Announced Oct 28, 2026• 0 citations
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Abstract

The detection and localization of gamma-ray sources via maximum-likelihood analysis is a complex parameter optimization problem. We address this challenge using quantum annealing, a computational approach that solves optimization tasks by encoding them into the ground state of an Ising Hamiltonian. Specifically, we apply this method to observations from AGILE's Gamma-Ray Imaging Detector (GRID) in the 30 MeV-50 GeV band. We reformulated the standard analysis as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We tested this method on a set of AGILE observations generated with AGILEpy, solving the QUBO with simulated quantum annealing. The method reproduces the classical flux and position estimates within their statistical uncertainty and identifies the correct grid node among the candidate positions in 97% of the maps. These promising results demonstrate that quantum annealing could be used in this optimization problem, laying the ground for its deployment on actual quantum annealers.

AI Summary

The paper investigates whether gamma-ray source detection and localization can be encoded for quantum annealing, using simulated AGILE-GRID observations. It replaces the Poisson log-likelihood with unweighted least squares and encodes the background and source amplitudes with 12-bit fixed-point variables. Localization selects among 81 candidate positions using binary indicators and a one-hot penalty. Optimization uses OpenJij simulated quantum annealing, not quantum hardware, with 100 reads and 1,000 sweeps per run. Tests use 500 simulated maps containing one bright point source at high Galactic latitude, with negligible Galactic diffuse emission. The method selects the true grid node in 97% of maps; remaining selections are displaced by one $0.5^\circ$ grid step. Recovered flux has mean bias $-1.9\%$ and standard deviation $8.3\%$, versus $3.0\%$ and $5.0\%$ for the classical pipeline. Background normalization is biased by approximately $-20\%$. Annealing energies agree with closed-form least-squares minima to within about $0.5\%$ of the test statistic for the source model. Reported $\sqrt{TS}$ values correlate strongly with classical results ($r=0.997$), but are systematically higher: mean 65 versus 43, reflecting the different objectives. Stated limitations include testing only bright, isolated sources; finite encoding ranges; unresolved background bias; and an approximate significance calibration for discrete position selection. Fainter and multiple-source cases remain untested. The roughly 1,000-variable, densely connected formulation is estimated to exceed current annealer capacity after embedding; hardware execution and computational-cost comparisons remain future work.

Little sign of AI-generated textIncrementalStrong assumptionsOverclaimsPossible error

Central claim may not hold: The implemented unweighted least-squares objective is not an exact reformulation or generally valid Gaussian approximation of the stated Poisson maximum-likelihood objective. The reported errors relative to simulated truth do not establish the abstract's claim that classical flux and position estimates are reproduced within statistical uncertainty. Validation on isolated, extremely bright sources with negligible Galactic diffuse emission supports only a restricted proof of concept, not general source-detection performance. The joint-localization model produces higher-than-quadratic binary terms, and the paper supplies neither a quadratization nor an equivalent constrained-activation QUBO construction. The unweighted least-squares energy difference lacks the likelihood-ratio and null-distribution justification needed to report its square root as detection significance.

Written by gpt-6.1-sol from the paper's full text. Automated: it can be wrong.

Comments
9 Pages, 5 Figures, presented at AIQxQIA workshop for the AIxIA conference in Perugia, October 2026. To be published as conference proceeding for CEUR-WS Proceedings

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The detection and localization of gamma-ray sources via maximum-likelihood analysis is a complex parameter optimization problem. We address this challenge using quantum annealing, a computational approach that solves optimization tasks by encoding them into the ground state of an Ising Hamiltonian. Specifically, we apply this method to observations from AGILE's Gamma-Ray Imaging Detector (GRID) in the 30 MeV-50 GeV band. We reformulated the standard analysis as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We tested this method on a set of AGILE observations generated with AGILEpy, solving the QUBO with simulated quantum annealing. The method reproduces the classical flux and position estimates within their statistical uncertainty and identifies the correct grid node among the candidate positions in 97% of the maps. These promising results demonstrate that quantum annealing could be used in this optimization problem, laying the ground for its deployment on actual quantum annealers.
The paper investigates whether gamma-ray source detection and localization can be encoded for quantum annealing, using simulated AGILE-GRID observations. It replaces the Poisson log-likelihood with unweighted least squares and encodes the background and source amplitudes with 12-bit fixed-point variables. Localization selects among 81 candidate positions using binary indicators and a one-hot penalty. Optimization uses OpenJij simulated quantum annealing, not quantum hardware, with 100 reads and 1,000 sweeps per run.
Tests use 500 simulated maps containing one bright point source at high Galactic latitude, with negligible Galactic diffuse emission. The method selects the true grid node in 97% of maps; remaining selections are displaced by one 0.5∘0.5^\circ0.5∘ grid step. Recovered flux has mean bias −1.9%-1.9\%−1.9% and standard deviation 8.3%8.3\%8.3%, versus 3.0%3.0\%3.0% and 5.0%5.0\%5.0% for the classical pipeline. Background normalization is biased by approximately −20%-20\%−20%. Annealing energies agree with closed-form least-squares minima to within about 0.5%0.5\%0.5% of the test statistic for the source model. Reported TS\sqrt{TS}TS​ values correlate strongly with classical results (r=0.997r=0.997r=0.997), but are systematically higher: mean 65 versus 43, reflecting the different objectives.
Stated limitations include testing only bright, isolated sources; finite encoding ranges; unresolved background bias; and an approximate significance calibration for discrete position selection. Fainter and multiple-source cases remain untested. The roughly 1,000-variable, densely connected formulation is estimated to exceed current annealer capacity after embedding; hardware execution and computational-cost comparisons remain future work.
We propose a unified approach for reformulating diverse computational challenges into Quadratic Unconstrained Binary Optimization (QUBO) models and solving them via quantum annealing. By applying this framework across multiple domains, we demonstrate its flexibility in handling structurally different problems within a single mathematical formulation. The study highlights QUBO’s capability to exploit quantum parallelism, integrate regularization seamlessly, and address ill-posed inverse problems. These advantages position QUBO-based quantum annealing as a promising tool for large-scale optimization and reconstruction tasks, with strong potential for impact in scientific computing, engineering, and data-driven applications.
A bstract High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.
Quantum annealers can be used to solve many (possibly NP-hard) combinatorial optimization problems, by formulating them as quadratic unconstrained binary optimization (QUBO) problems or, equivalently, using the Ising formulation. In this paper we analyse the ability of quantum D-Wave annealers to find the maximum clique on a graph, expressed as a QUBO problem. Due to the embedding limit of 164 nodes imposed by the anneler, we conducted a study on graph decomposition to enable instance embedding. We thus propose a decomposition algorithm for the complementary maximum independent set problem, and a graph generation algorithm to control the number of nodes, the number of cliques, the density, the connectivity indices and the ratio of the solution size to the number of other nodes. We then statistically analysed how these variables affect the quality of the solutions found by the quantum annealer. The results of our investigation include recommendations on ratio and density limits not to be exceeded, as well as a series of precautions and a priori analyses to be carried out in order to maximise the probability of obtaining a solution close to the optimum.
To date, conventional computers have never been able to efficiently handle certain tasks, where the number of computation steps is likely to blow up as the problem size increases. As an emerging technology and new computing paradigm, quantum computing has a great potential to tackle those hard tasks efficiently. Among all the existing quantum computation models, quantum annealing has drawn significant attention in recent years due to the realization of the commercialized quantum annealer, sparking research interests in developing applications to solve problems that are intractable for classical computers.However, designing and implementing algorithms that manage to harness the enormous computation power from quantum annealer remains a challenging task. Generally, it requires mapping of the given optimization problem into quadratic unconstrained binary optimization(QUBO) problem and embedding the subsequent QUBO onto the physical architecture of quantum annealer. Additionally, practical quantum annealers are susceptible to errors leading to low probability of the correct solution.In this study, we focus on solving Boolean satisfiability (SAT) problem using quantum annealer while addressing practical limitations. We have proposed a mapping technique that maps SAT problem to QUBO, and we have further devised a tool flow that embeds the QUBO onto the architecture of a quantum annealing device. Additionally, We have optimized the proposed embedding flow to reduce run-time in addition to shortening the qubit chain length, leading to robust quantum annealing. To further improve the reliability of quantum annealing, we have also developed a post processing embedding technique that enlarges the energy gap between ground state and the first excited state. To demonstrate the effectiveness of proposed methods, we have conducted experiments on real quantum annealing devices manufactured by D-Wave Systems, showing compelling result of using quantum annealer to solve SAT problem.
Quantum annealers offer an efficient way to compute high quality solutions of NP-hard problems when expressed in a QUBO (quadratic unconstrained binary optimization) or an Ising form. This is done by mapping a problem onto the physical qubits and couplers of the quantum chip, from which a solution is read after a process called quantum annealing. However, this process is subject to multiple sources of biases, including poor calibration, leakage between adjacent qubits, control biases, etc., which might negatively influence the quality of the annealing results. In this work, we aim at mitigating the effect of such biases for solving constrained optimization problems, by offering a two-step method, and apply it to Graph Partitioning. In the first step, we measure and reduce any biases that result from implementing the constraints of the problem. In the second, we add the objective function to the resulting bias-corrected implementation of the constraints, and send the problem to the quantum annealer. We apply this concept to Graph Partitioning, an important NP-hard problem, which asks to find a partition of the vertices of a graph that is balanced (the constraint) and minimizes the cut size (the objective). We first quantify the bias of the implementation of the constraint on the quantum annealer, that is, we require, in an unbiased implementation, that any two vertices have the same likelihood of being assigned to the same or to different parts of the partition. We then propose an iterative method to correct any such biases. We demonstrate that, after adding the objective, solving the resulting bias-corrected Ising problem on the quantum annealer results in a higher solution accuracy.
Quantum annealing algorithms were introduced to solve combinatorial optimization problems by taking advantage of quantum fluctuations to escape local minima in complex energy landscapes typical of NP - hard problems. In this work, we propose using quantum annealing for the theory of cuts, a field of paramount importance in theoretical computer science. We have proposed a method to formulate the Minimum Multicut Problem into the QUBO representation, and the technical difficulties faced when embedding and submitting a problem to the quantum annealer processor. We show two constructions of the quadratic unconstrained binary optimization functions for the Minimum Multicut Problem and we review several tradeoffs between the two mappings and provide numerical scaling analysis results from several classical approaches. Furthermore, we discuss some of the expected challenges and tradeoffs in the implementation of our mapping in the current generation of D-Wave machines.