A quantum algorithm for model independent searches for new physics

• URL: http://arxiv.org/abs/2003.02181v2
• Date: Sun, 2 Aug 2020 20:24:49 GMT
• Title: A quantum algorithm for model independent searches for new physics
• Authors: Konstantin T. Matchev, Prasanth Shyamsundar and Jordan Smolinsky
• Abstract summary: We propose a novel quantum technique to search for unmodelled anomalies in multi-dimensional binned collider data. We associate an Ising lattice spin site with each bin, with the Ising Hamiltonian suitably constructed from the observed data. In order to capture spatially correlated anomalies in the data, we introduce spin-spin interactions between neighboring sites.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel quantum technique to search for unmodelled anomalies in multi-dimensional binned collider data. We propose to associate an Ising lattice spin site with each bin, with the Ising Hamiltonian suitably constructed from the observed data and a corresponding theoretical expectation. In order to capture spatially correlated anomalies in the data, we introduce spin-spin interactions between neighboring sites, as well as self-interactions. The ground state energy of the resulting Ising Hamiltonian can be used as a new test statistic, which can be computed either classically or via adiabatic quantum optimization. We demonstrate that our test statistic outperforms some of the most commonly used goodness-of-fit tests. The new approach greatly reduces the look-elsewhere effect by exploiting the typical differences between statistical noise and genuine new physics signals.

Related papers

• ShadowGPT: Learning to Solve Quantum Many-Body Problems from Randomized Measurements [2.1946359779523332]
  We propose ShadowGPT, a novel approach for solving quantum many-body problems by learning from randomized measurement data collected from quantum experiments. The model is a generative pretrained transformer (GPT) trained on simulated classical shadow data of ground states of quantum Hamiltonians.
  arXiv  Detail & Related papers  (2024-11-05T17:34:03Z)
• Learning the dynamics of Markovian open quantum systems from experimental data [3.7127285734321203]
  The algorithm is based on a Markov Chain Monte Carlo approach. We benchmark our algorithm on quantum optics experiments performed on single and pairs of quantum emitters.
  arXiv  Detail & Related papers  (2024-10-23T15:14:59Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere [0.6249768559720122]
  We show that existing noise models fail to properly capture the aggregation of noise effects over an algorithm's runtime. We develop noise model for the readout probabilities as a function of the number of gate operations. We superimpose a second random walk at the scale of multiple readouts to account for overdispersion.
  arXiv  Detail & Related papers  (2024-07-04T14:44:53Z)
• Tailored and Externally Corrected Coupled Cluster with Quantum Inputs [0.0]
  We propose to use wavefunction overlaps obtained from a quantum computer as inputs for the classical split-amplitude techniques. We provide insights into which wavefunction preparation schemes have a chance of yielding quantum advantage.
  arXiv  Detail & Related papers  (2023-12-13T12:57:39Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Validation tests of GBS quantum computers give evidence for quantum advantage with a decoherent target [62.997667081978825]
  We use positive-P phase-space simulations of grouped count probabilities as a fingerprint for verifying multi-mode data. We show how one can disprove faked data, and apply this to a classical count algorithm.
  arXiv  Detail & Related papers  (2022-11-07T12:00:45Z)
• Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
  Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. We propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
  arXiv  Detail & Related papers  (2022-07-21T12:11:18Z)
• State preparation and measurement in a quantum simulation of the O(3) sigma model [65.01359242860215]
  We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes. We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
  arXiv  Detail & Related papers  (2020-06-28T23:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.