A case study against QSVT: assessment of quantum phase estimation improved by signal processing techniques

• URL: http://arxiv.org/abs/2404.01396v2
• Date: Wed, 17 Apr 2024 18:49:13 GMT
• Title: A case study against QSVT: assessment of quantum phase estimation improved by signal processing techniques
• Authors: Sean Greenaway, William Pol, Sukin Sim,
• Abstract summary: In recent years, quantum algorithms have been proposed which use quantum phase estimation coherently as a subroutine without measurement. We show that the use of the Kaiser window function is currently the most practical choice for realizing QPE with high success probability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, quantum algorithms have been proposed which use quantum phase estimation (QPE) coherently as a subroutine without measurement. In order to do this effectively, the routine must be able to distinguish eigenstates with success probability close to unity. In this paper, we provide the first systematic comparison between two approaches towards maximizing this success probability, one using the quantum singular value transform and the other leveraging window functions, which have been previously studied as priors of the phase value distribution. We find that the quantum singular value transform is significantly outclassed by the window function approach, with the latter able to achieve between 3 and 5 orders of magnitude improvement in the success probability with approximately 1/4 the query cost. Our circuit simulation results indicate that QPE is not a domain which benefits from the integration of QSVT and we show that the use of the Kaiser window function is currently the most practical choice for realizing QPE with high success probability.

Related papers

• Bayesian Quantum Amplitude Estimation [49.1574468325115]
  We introduce BAE, a noise-aware Bayesian algorithm for quantum amplitude estimation. We show that BAE achieves Heisenberg-limited estimation and benchmark it against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Curve-Fitted QPE: Extending Quantum Phase Estimation Results for a Higher Precision using Classical Post-Processing [0.0]
  We present a hybrid quantum-classical approach that consists of the standard QPE circuit and classical post-processing using curve-fitting. We show that our approach achieves high precision with optimal Cram'er-Rao lower bound performance and is comparable in error resolution with the Variational Quantum Eigensolver and Maximum Likelihood Amplitude Estimation algorithms.
  arXiv  Detail & Related papers  (2024-09-24T05:15:35Z)
• Unveiling quantum phase transitions from traps in variational quantum algorithms [0.0]
  We introduce a hybrid algorithm that combines quantum optimization with classical machine learning. We use LASSO for identifying conventional phase transitions and the Transformer model for topological transitions. Our protocol significantly enhances efficiency and precision, opening new avenues in the integration of quantum computing and machine learning.
  arXiv  Detail & Related papers  (2024-05-14T09:01:41Z)
• Calculating the expected value function of a two-stage stochastic optimization program with a quantum algorithm [0.0]
  Two-stage programming is a problem formulation for decision-making under uncertainty. This work uses a quantum algorithm to estimate the expected value function with a speedup.
  arXiv  Detail & Related papers  (2024-02-23T00:07:34Z)
• Federated Quantum Long Short-term Memory (FedQLSTM) [58.50321380769256]
  Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models. No prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions. A novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed.
  arXiv  Detail & Related papers  (2023-12-21T21:40:47Z)
• On Quantum Annealing Without a Physical Quantum Annealer [0.0]
  We propose and evaluate a hybrid quantum classical: Quantum Accelerated Simulated Annealing (QASA) Our simulation results show QASA performing comparably to SA but for a reduced amount of steps.
  arXiv  Detail & Related papers  (2023-07-19T00:37:34Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
  We present an approach where the quantum computation is supplemented by a classical result. Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative. In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
  arXiv  Detail & Related papers  (2022-09-12T15:47:44Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning [5.630204194930539]
  We present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations. We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit.
  arXiv  Detail & Related papers  (2021-11-16T22:29:43Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.