Scalable Quantum Algorithms for Noisy Quantum Computers

• URL: http://arxiv.org/abs/2403.00940v1
• Date: Fri, 1 Mar 2024 19:36:35 GMT
• Title: Scalable Quantum Algorithms for Noisy Quantum Computers
• Authors: Julien Gacon
• Abstract summary: This thesis develops two main techniques to reduce the quantum computational resource requirements. The aim is to scale up application sizes on current quantum processors. While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. However, due to the noise and the limited scale of current quantum computers, may prominent quantum algorithms are currently infeasible to run for problem sizes of practical interest. This doctoral thesis develops two main techniques to reduce the quantum computational resource requirements, with the goal of scaling up application sizes on current quantum processors. The first approach is based on stochastic approximations of computationally costly quantities, such as quantum circuit gradients or the quantum geometric tensor (QGT). The second method takes a different perspective on the QGT, leading to a potentially more efficient description of time evolution on current quantum computers. While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning. Our algorithms are benchmarked on a range of representative models, such as Ising or Heisenberg spin models, both in numerical simulations and experiments on the hardware. In combination with error mitigation techniques, the latter is scaled up to 27 qubits; into a regime that variational quantum algorithms are challenging to scale to on noisy quantum computers without our algorithms.

Related papers

• 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)
• A Quantum Algorithm Based Heuristic to Hide Sensitive Itemsets [1.8419202109872088]
  We present a quantum approach to solve a well-studied problem in the context of data sharing. We present results on experiments involving small datasets to illustrate how the problem could be solved using quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-12T20:44:46Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• 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)
• Quantum Computing Quantum Monte Carlo [8.69884453265578]
  We propose a hybrid quantum-classical algorithm that integrates quantum computing and quantum Monte Carlo. Our work paves the way to solving practical problems with intermediatescale and early-fault tolerant quantum computers.
  arXiv  Detail & Related papers  (2022-06-21T14:26:24Z)
• Resource-efficient encoding algorithm for variational bosonic quantum simulations [0.0]
  In the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, quantum resources are limited. We present a resource-efficient quantum algorithm for bosonic ground and excited state computations.
  arXiv  Detail & Related papers  (2021-02-23T19:00:05Z)
• 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)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)
• Simulating quantum chemistry in the seniority-zero space on qubit-based quantum computers [0.0]
  We combine the so-called seniority-zero, or paired-electron, approximation of computational quantum chemistry with techniques for simulating molecular chemistry on gate-based quantum computers. We show that using the freed-up quantum resources for increasing the basis set can lead to more accurate results and reductions in the necessary number of quantum computing runs.
  arXiv  Detail & Related papers  (2020-01-31T19:44:37Z)

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.