Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA
- URL: http://arxiv.org/abs/2503.01748v1
- Date: Mon, 03 Mar 2025 17:15:23 GMT
- Title: Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA
- Authors: Alejandro Mata Ali,
- Abstract summary: We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators.<n>This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
- Score: 55.2480439325792
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, 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 allows to perform this calculation in a single quantum circuit measuring a single qubit, which speeds up the computation process. This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms, both in real devices and in simulations. We analyze its application to the parameter optimization process of the Quantum Approximate Optimization Algorithm (QAOA) and the case of having degenerate values in the matrix of the Ising problem. Finally, we apply it to several Quadratic Unconstrained Binary Optimization (QUBO) problems to analyze the speedup of the method in circuit simulators.
Related papers
- A quantum gradient descent algorithm for optimizing Gaussian Process models [28.16587217223671]
We propose a quantum gradient descent algorithm to optimize the Gaussian Process model.
Our algorithm achieves exponential speedup in computing the gradients of the log marginal likelihood.
arXiv Detail & Related papers (2025-03-22T14:14:31Z) - Dynamic Circuits for the Quantum Lattice-Boltzmann Method [0.0]
We propose a quantum algorithm for the linear advection-diffusion equation (ADE) Lattice-Boltzmann method (LBM)<n> Dynamic quantum circuits allow for an optimized collision-operator quantum algorithm, introducing partial measurements as an integral step.
arXiv Detail & Related papers (2025-02-04T09:04:24Z) - 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) - Noisy Tensor Ring approximation for computing gradients of Variational
Quantum Eigensolver for Combinatorial Optimization [33.12181620473604]
Variational Quantum algorithms have established their potential to provide computational advantage in the realm of optimization.
These algorithms suffer from classically intractable gradients limiting the scalability.
This work proposes a classical gradient method which utilizes the parameter shift rule but computes the expected values from the circuits using a tensor ring approximation.
arXiv Detail & Related papers (2023-07-08T03:14:28Z) - Quantum algorithm for stochastic optimal stopping problems with
applications in finance [60.54699116238087]
The famous least squares Monte Carlo (LSM) algorithm combines linear least square regression with Monte Carlo simulation to approximately solve problems in optimal stopping theory.
We propose a quantum LSM based on quantum access to a process, on quantum circuits for computing the optimal stopping times, and on quantum techniques for Monte Carlo.
arXiv Detail & Related papers (2021-11-30T12:21:41Z) - QuOp_MPI: a framework for parallel simulation of quantum variational
algorithms [0.0]
QuOp_MPI is a Python package designed for parallel simulation of quantum variational algorithms.
It presents an object-orientated approach to quantum variational algorithm design.
arXiv Detail & Related papers (2021-10-08T08:26:09Z) - Behavior of Analog Quantum Algorithms [0.0]
We show that different analog quantum algorithms can emulate the optimal protocol under different limits and approximations.
We present a new algorithm for better approximating the optimal protocol using the analytic and numeric insights from the rest of the paper.
arXiv Detail & Related papers (2021-07-02T18:00:07Z) - 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) - Approximating the quantum approximate optimization algorithm with
digital-analog interactions [0.0]
We show that the digital-analog paradigm is suited to the variational quantum approximate optimisation algorithm.
We observe regimes of single-qubit operation speed in which the considered variational algorithm provides a significant improvement over non-variational counterparts.
arXiv Detail & Related papers (2020-02-27T16:01:40Z)
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.