Efficient inference of quantum system parameters by Approximate Bayesian Computation

• URL: http://arxiv.org/abs/2407.00724v2
• Date: Fri, 2 Aug 2024 14:28:39 GMT
• Title: Efficient inference of quantum system parameters by Approximate Bayesian Computation
• Authors: Lewis A. Clark, Jan Kolodynski,
• Abstract summary: We propose the Approximate Bayesian Computation (ABC) algorithm, which evades likelihood by sampling from a library of measurement data. We apply ABC to interpret photodetection click-patterns arising when probing in real time a two-level atom and an optomechanical system. Our work demonstrates that fast parameter inference may be possible no matter the complexity of a quantum device and the measurement scheme involved.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to efficiently infer system parameters is essential in any signal-processing task that requires fast operation. Dealing with quantum systems, a serious challenge arises due to substantial growth of the underlying Hilbert space with the system size. As the statistics of the measurement data observed, i.e. the likelihood, can no longer be easily computed, common approaches such as maximum-likelihood estimators or particle filters become impractical. To address this issue, we propose the use of the Approximate Bayesian Computation (ABC) algorithm, which evades likelihood computation by sampling from a library of measurement data -- a priori prepared for a given quantum device. We apply ABC to interpret photodetection click-patterns arising when probing in real time a two-level atom and an optomechanical system. For the latter, we consider both linear and non-linear regimes, in order to show how to tailor the ABC algorithm by understanding the quantum measurement statistics. Our work demonstrates that fast parameter inference may be possible no matter the complexity of a quantum device and the measurement scheme involved.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Effect of the readout efficiency of quantum measurement on the system entanglement [44.99833362998488]
  We quantify the entanglement for a particle on a 1d quantum random walk under inefficient monitoring. We find that the system's maximal mean entanglement at the measurement-induced quantum-to-classical crossover is in different ways by the measurement strength and inefficiency.
  arXiv  Detail & Related papers  (2024-02-29T18:10:05Z)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Learning the tensor network model of a quantum state using a few single-qubit measurements [0.0]
  The constantly increasing dimensionality of artificial quantum systems demands highly efficient methods for their characterization and benchmarking. Here we present a constructive and numerically efficient protocol which learns a tensor network model of an unknown quantum system.
  arXiv  Detail & Related papers  (2023-09-01T11:11:52Z)
• Determining the ability for universal quantum computing: Testing controllability via dimensional expressivity [39.58317527488534]
  Controllability tests can be used in the design of quantum devices to reduce the number of external controls. We devise a hybrid quantum-classical algorithm based on a parametrized quantum circuit.
  arXiv  Detail & Related papers  (2023-08-01T15:33:41Z)
• Quantum Search Approaches to Sampling-Based Motion Planning [0.0]
  We present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections.
  arXiv  Detail & Related papers  (2023-04-10T19:12:25Z)
• Dispersive qubit readout with machine learning [0.08399688944263842]
  Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. A recently introduced measurement protocol uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9%. We use machine learning-based classification algorithms to extract information from this critical dynamics.
  arXiv  Detail & Related papers  (2021-12-10T04:25:43Z)
• Quantum Causal Unravelling [44.356294905844834]
  We develop the first efficient method for unravelling the causal structure of the interactions in a multipartite quantum process. Our algorithms can be used to identify processes that can be characterized efficiently with the technique of quantum process tomography.
  arXiv  Detail & Related papers  (2021-09-27T16:28:06Z)
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer [0.0]
  We propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation. The algorithm is used to extract the phase diagram of a system with no prior physical knowledge. We show that it can be used with readily accessible devices nowadays and perform the algorithm on a real quantum computer.
  arXiv  Detail & Related papers  (2021-06-15T06:54:47Z)
• Reservoir Computing Approach to Quantum State Measurement [0.0]
  Reservoir computing is a resource-efficient solution to quantum measurement of superconducting multi-qubit systems. We show how to operate this device to perform two-qubit state tomography and continuous parity monitoring.
  arXiv  Detail & Related papers  (2020-11-19T04:46:15Z)
• Statistical Limits of Supervised Quantum Learning [90.0289160657379]
  We show that if the bound on the accuracy is taken into account, quantum machine learning algorithms for supervised learning cannot achieve polylogarithmic runtimes in the input dimension. We conclude that, when no further assumptions on the problem are made, quantum machine learning algorithms for supervised learning can have at most speedups over efficient classical algorithms.
  arXiv  Detail & Related papers  (2020-01-28T17:35:32Z)

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.