Phase polynomials synthesis algorithms for NISQ architectures and beyond

• URL: http://arxiv.org/abs/2104.00934v1
• Date: Fri, 2 Apr 2021 08:26:36 GMT
• Title: Phase polynomials synthesis algorithms for NISQ architectures and beyond
• Authors: Vivien Vandaele, Simon Martiel, Timoth\'ee Goubault de Brugi\`ere
• Abstract summary: In most cases, our algorithms generate circuits with lower CNOT count and CNOT depth than the state of the art or have a significantly smaller running time for similar performances. We also provide methods that can be applied to our algorithms in order to trade an increase in the CNOT count for a decrease in execution time, thereby filling the gap between our algorithms and faster ones.
• Score: 3.3722008527102894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for the synthesis of phase polynomials that addresses both cases of full connectivity and partial connectivity for NISQ architectures. In most cases, our algorithms generate circuits with lower CNOT count and CNOT depth than the state of the art or have a significantly smaller running time for similar performances. We also provide methods that can be applied to our algorithms in order to trade an increase in the CNOT count for a decrease in execution time, thereby filling the gap between our algorithms and faster ones.

Related papers

• Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching [53.91395791840179]
  We present Unified Spectral Bundling with Sketching (USBS), a provably correct, fast and scalable algorithm for solving massive SDPs. USBS provides a 500x speed-up over the state-of-the-art scalable SDP solver on an instance with over 2 billion decision variables.
  arXiv  Detail & Related papers  (2023-12-19T02:27:22Z)
• Tractable Bounding of Counterfactual Queries by Knowledge Compilation [51.47174989680976]
  We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters. We show how a single symbolic knowledge compilation allows us to obtain the circuit structure with symbolic parameters to be replaced by their actual values.
  arXiv  Detail & Related papers  (2023-10-05T07:10:40Z)
• Improving Quantum Circuit Synthesis with Machine Learning [0.7894596908025954]
  We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries.
  arXiv  Detail & Related papers  (2023-06-09T01:53:56Z)
• Locality-aware Qubit Routing for the Grid Architecture [1.4459640831465588]
  We introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain "grid-like" architectures.
  arXiv  Detail & Related papers  (2022-03-21T20:46:39Z)
• Decoding techniques applied to the compilation of CNOT circuits for NISQ architectures [0.0]
  We present a new algorithm for the synthesis of CNOT circuits based on the solution of the syndrome decoding problem. Our method addresses the case of ideal hardware with an all-to-all qubit connectivity and the case of near-term quantum devices with restricted connectivity.
  arXiv  Detail & Related papers  (2022-01-17T15:11:36Z)
• AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization [159.75564904944707]
  We propose an asynchronous quasi-Newton (AsySQN) framework for vertical federated learning (VFL) The proposed algorithms make descent steps scaled by approximate without calculating the inverse Hessian matrix explicitly. We show that the adopted asynchronous computation can make better use of the computation resource.
  arXiv  Detail & Related papers  (2021-09-26T07:56:10Z)
• Efficient Computation of Expectations under Spanning Tree Distributions [67.71280539312536]
  We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms.
  arXiv  Detail & Related papers  (2020-08-29T14:58:26Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• Architecture-Aware Synthesis of Phase Polynomials for NISQ Devices [0.0]
  We propose a new algorithm toe quantum circuits for connectivitys, which takes into account the qubit of the quantum computer. Our algorithm generates circuits with a smaller CNOT depth than the algorithms currently used in Staq and tket.
  arXiv  Detail & Related papers  (2020-04-13T16:26:14Z)
• QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space [5.406226763868874]
  We present QFAST, a quantum synthesis tool designed to produce short circuits. We show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level.
  arXiv  Detail & Related papers  (2020-03-09T23:55:43Z)
• Lagrangian Decomposition for Neural Network Verification [148.0448557991349]
  A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. We propose a novel approach based on Lagrangian Decomposition. We show that we obtain bounds comparable with off-the-shelf solvers in a fraction of their running time.
  arXiv  Detail & Related papers  (2020-02-24T17:55:10Z)

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.