Tequila: A platform for rapid development of quantum algorithms

• URL: http://arxiv.org/abs/2011.03057v2
• Date: Thu, 25 Feb 2021 15:29:58 GMT
• Title: Tequila: A platform for rapid development of quantum algorithms
• Authors: Jakob S. Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Tzu-Ching Yen, Vladyslav Verteletskyi, Philipp Schleich, Abhinav Anand, Matthias Degroote, Skylar Chaney, Maha Kesibi, Naomi Grace Curnow, Brandon Solo, Georgios Tsilimigkounakis, Claudia Zendejas-Morales, Artur F. Izmaylov, Al\'an Aspuru-Guzik
• Abstract summary: Tequila is a development package for quantum algorithms in python. It is designed for fast and flexible implementation, prototyping, and deployment of novel quantum algorithms in electronic structure and other fields.
• Score: 0.3248699949578586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods in quantum algorithmic development yet, and the field continues to evolve rapidly. As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in high demand for flexible and reliable ways to implement, test, and share new ideas. Inspired by this demand, we introduce tequila, a development package for quantum algorithms in python, designed for fast and flexible implementation, prototyping, and deployment of novel quantum algorithms in electronic structure and other fields. Tequila operates with abstract expectation values which can be combined, transformed, differentiated, and optimized. On evaluation, the abstract data structures are compiled to run on state-of-the-art quantum simulators or interfaces.

Related papers

• MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework [20.585698216552892]
  We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on noisy intermediate-scale quantum (NISQ) algorithms. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine.
  arXiv  Detail & Related papers  (2024-06-25T03:28:40Z)
• 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)
• Hamiltonian Encoding for Quantum Approximate Time Evolution of Kinetic Energy Operator [2.184775414778289]
  The time evolution operator plays a crucial role in the precise computation of chemical experiments on quantum computers. We have proposed a new encoding method, namely quantum approximate time evolution (QATE) for the quantum implementation of the kinetic energy operator.
  arXiv  Detail & Related papers  (2023-10-05T05:25:38Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Quantum Algorithm Cards: Streamlining the development of hybrid classical-quantum applications [0.0]
  The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations implies better performance and efficiency for certain algorithmic tasks than current computers provide. To gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward.
  arXiv  Detail & Related papers  (2023-10-04T06:02:59Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Variational Quantum Algorithms for Computational Fluid Dynamics [0.0]
  Variational quantum algorithms are particularly promising since they are comparatively noise tolerant. We show how variational quantum algorithms can be utilized in computational fluid dynamics. We argue that a quantum advantage over classical computing methods could be achieved by the end of this decade.
  arXiv  Detail & Related papers  (2022-09-11T18:49:22Z)
• Parametrized Complexity of Quantum Inspired Algorithms [0.0]
  Two promising areas of quantum algorithms are quantum machine learning and quantum optimization. Motivated by recent progress in quantum technologies and in particular quantum software, research and industrial communities have been trying to discover new applications of quantum algorithms.
  arXiv  Detail & Related papers  (2021-12-22T06:19:36Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• 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.