Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Data Loading

• URL: http://arxiv.org/abs/2311.17798v1
• Date: Wed, 29 Nov 2023 16:47:31 GMT
• Title: Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Data Loading
• Authors: Chun-Tse Li, Hao-Chung Cheng
• Abstract summary: We present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM) Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state. Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding.
• Score: 7.88657961743755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the progress in the quantum algorithm in recent years, much of the existing literature claims the exponential quantum advantage against their classical counterpart. However, many of these successes hinge on the assumption that arbitrary states can be efficiently prepared in quantum circuits. In reality, crafting a circuit to prepare a generic $n$-qubit quantum state demands an operation count on the order of $\mathcal{O}(2^n)$, which is prohibitively demanding for the quantum algorithm to demonstrate its advantage against the classical one. To tackle this data-loading problem, numerous strategies have been put forward. Nonetheless, most of these approaches only consider a very simple and easy-to-implement circuit structure, which has been shown to suffer from serious optimization issues. In this study, we harness quantum circuits as Born machines to generate probability distributions. Drawing inspiration from methods used to investigate electronic structures in quantum chemistry and condensed matter physics, we present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM) that dynamically expands the ansatz circuit. Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state. Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding, demonstrating not only compliance with but also enhancement over the performance benchmarks set by previous research.

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)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Testing and Debugging Quantum Circuits [0.65268245109828]
  This paper focuses on three types of circuit blocks: Amplitude Permutation, Phase Modulation, and Amplitude Redistribution circuit blocks. We present a comprehensive unit testing tool (Cirquo) and debug approaches tailored to the unique demands of quantum computing.
  arXiv  Detail & Related papers  (2023-11-30T02:42:58Z)
• Predicting RNA Secondary Structure on Universal Quantum Computer [2.277461161767121]
  It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed. Traditional energy-based algorithms are short of precision, particularly for non-nested sequences. Gate model algorithms for universal quantum computing are not available.
  arXiv  Detail & Related papers  (2023-05-16T15:57:38Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Quantum amplitude damping for solving homogeneous linear differential equations: A noninterferometric algorithm [0.0]
  This work proposes a novel approach by using the Quantum Amplitude Damping operation as a resource, in order to construct an efficient quantum algorithm for solving homogeneous LDEs. We show that such an open quantum system-inspired circuitry allows for constructing the real exponential terms in the solution in a non-interferometric.
  arXiv  Detail & Related papers  (2021-11-10T11:25:32Z)
• Efficient realization of quantum algorithms with qudits [0.70224924046445]
  We propose a technique for an efficient implementation of quantum algorithms with multilevel quantum systems (qudits) Our method uses a transpilation of a circuit in the standard qubit form, which depends on the parameters of a qudit-based processor. We provide an explicit scheme of transpiling qubit circuits into sequences of single-qudit and two-qudit gates taken from a particular universal set.
  arXiv  Detail & Related papers  (2021-11-08T11:09:37Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications [1.6329956884407544]
  We introduce two ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. AQCEL is deployed on an iterative and efficient quantum algorithm designed to model final state radiation in high energy physics. Our technique is generic and can be useful for a wide variety of quantum algorithms.
  arXiv  Detail & Related papers  (2021-02-19T16:20:31Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.