Quantum Circuit Optimization: Current trends and future direction

• URL: http://arxiv.org/abs/2408.08941v1
• Date: Fri, 16 Aug 2024 15:07:51 GMT
• Title: Quantum Circuit Optimization: Current trends and future direction
• Authors: Geetha Karuppasamy, Varun Puram, Stevens Johnson, Johnson P Thomas,
• Abstract summary: Recent advancements in quantum circuit optimization are explored. analytical algorithms, quantum algorithms, machine learning-based algorithms, and hybrid quantum-classical algorithms are discussed.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Optimization of quantum circuits for a given problem is very important in order to achieve faster calculations as well as reduce errors due to noise. Optimization has to be achieved while ensuring correctness at all times. In this survey paper, recent advancements in quantum circuit optimization are explored. Both hardware independent as well as hardware dependent optimization are presented. State-of-the-art methods for optimizing quantum circuits, including analytical algorithms, heuristic algorithms, machine learning-based algorithms, and hybrid quantum-classical algorithms are discussed. Additionally, the advantages and disadvantages of each method and the challenges associated with them are highlighted. Moreover, the potential research opportunities in this field are also discussed.

Related papers

• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [0.0]
  In this survey, we explore the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, one can reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms [0.0]
  We adapt quantum algorithms to tackle robust optimization problems. We present two innovative methods for obtaining robust optimal solutions. Theses are applied on two use cases within the energy sector.
  arXiv  Detail & Related papers  (2024-04-25T17:32:55Z)
• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Challenges and Opportunities in Quantum Optimization [14.7608536260003]
  Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. Across computer science and physics, there are different approaches for major optimization problems.
  arXiv  Detail & Related papers  (2023-12-04T19:00:44Z)
• 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)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Optimization Applications as Quantum Performance Benchmarks [0.0]
  Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems. This is used to guide the development of an advanced benchmarking framework for quantum computers.
  arXiv  Detail & Related papers  (2023-02-05T01:56:06Z)
• 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)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• 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)

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.