Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping

• URL: http://arxiv.org/abs/2311.14624v3
• Date: Tue, 17 Sep 2024 14:31:56 GMT
• Title: Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping
• Authors: Yanjun Ji, Kathrin F. Koenig, Ilia Polian,
• Abstract summary: This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms. Our approach increases the approximation ratio by an average of 4.49$times$ without error mitigation. Our findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters.
• Score: 0.4681661603096333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms by cooptimizing gate sequences, algorithm parameters, and qubit mapping. Demonstrations on near-term quantum devices validate the effectiveness of these strategies, leveraging both algorithmic and hardware advantages. Our approach increases the approximation ratio by an average of 4.49$\times$ without error mitigation and 84.8% with error mitigation, while reducing CX gate count and circuit depth by 28.8% and 33.4%, respectively, compared to Qiskit and Tket. These findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters, with broader implications for enhancing algorithm performance on near-term quantum devices.

Related papers

• Systematic improvement of the quantum approximate optimisation ansatz for combinatorial optimisation using quantum subspace expansion [0.0]
  I study the enhancement of the quantum approximate optimisation ansatz (QAOA) with a generator coordinate method (GCM)<n>I achieve systematic performances improvements in the approximation ratio and fidelity for the maximal independent set on Erd"os-R'enyi graphs.
  arXiv  Detail & Related papers  (2025-06-23T12:54:06Z)
• TANGO: A Robust Qubit Mapping Algorithm via Two-Stage Search and Bidirectional Look [7.064817742048067]
  Current quantum devices lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices. We propose the TANGO algorithm, which balances the impact of qubit mapping on both mapped and unmapped nodes. We show that the algorithm achieves multi-objective co-optimization of gate count and circuit depth across various benchmarks and quantum devices.
  arXiv  Detail & Related papers  (2025-03-10T13:44:16Z)
• Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA [55.2480439325792]
  We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators. This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
  arXiv  Detail & Related papers  (2025-03-03T17:15:23Z)
• Graph-based identification of qubit network (GidNET) for qubit reuse [9.435498822573734]
  GidNET is an algorithm for optimizing qubit reuse in quantum circuits. It consistently outperforms Qiskit in circuit width reduction. It offers a solution for quantum computers with limited numbers of qubits.
  arXiv  Detail & Related papers  (2024-10-11T14:00:11Z)
• Quantum Circuit Optimization: Current trends and future direction [0.0]
  Recent advancements in quantum circuit optimization are explored. analytical algorithms, quantum algorithms, machine learning-based algorithms, and hybrid quantum-classical algorithms are discussed.
  arXiv  Detail & Related papers  (2024-08-16T15:07:51Z)
• 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)
• Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms [3.990724104767043]
  Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. We propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and scalable mapping methods.
  arXiv  Detail & Related papers  (2023-10-15T13:18:06Z)
• Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization [41.94295877935867]
  We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2023-08-29T17:53:08Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• 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)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Quantum Approximate Optimization Algorithm with Adaptive Bias Fields [4.03537866744963]
  The quantum approximate optimization algorithm (QAOA) transforms a simple many-qubit wavefunction into one which encodes a solution to a difficult classical optimization problem. In this paper, the QAOA is modified by updating the operators themselves to include local fields, using information from the measured wavefunction at the end of one step to improve the operators at later steps.
  arXiv  Detail & Related papers  (2021-05-25T13:51:09Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)

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.