QuOp_MPI: a framework for parallel simulation of quantum variational
algorithms
- URL: http://arxiv.org/abs/2110.03963v2
- Date: Tue, 7 Jun 2022 04:54:02 GMT
- Title: QuOp_MPI: a framework for parallel simulation of quantum variational
algorithms
- Authors: Edric Matwiejew, Jingbo B. Wang
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 and utilises MPI-parallelised sparse-matrix
exponentiation, the fast Fourier transform and parallel gradient evaluation to
achieve the highly efficient simulation of the fundamental unitary dynamics on
massively parallel systems. In this article, we introduce QuOp_MPI and explore
its application to the simulation of quantum algorithms designed to solve
combinatorial optimisation algorithms including the Quantum Approximation
Optimisation Algorithm, the Quantum Alternating Operator Ansatz, and the
Quantum Walk-assisted Optimisation Algorithm.
Related papers
- Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems.
Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver.
Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
arXiv Detail & Related papers (2024-10-30T08:41:29Z) - Simulating Hamiltonian dynamics in a programmable photonic quantum
processor using linear combinations of unitary operations [4.353492002036882]
We modify the multi-product Trotterization and combine it with the oblivious amplitude amplification to simultaneously reach a high simulation precision and high success probability.
We experimentally implement the modified multi-product algorithm in an integrated-photonics programmable quantum simulator in silicon.
arXiv Detail & Related papers (2022-11-12T18:49:41Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - 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) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - 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) - Optimizing the Phase Estimation Algorithm Applied to the Quantum
Simulation of Heisenberg-Type Hamiltonians [0.0]
The phase estimation algorithm is a powerful quantum algorithm with applications in cryptography, number theory, and simulation of quantum systems.
We use this algorithm to simulate the time evolution of a system of two spin-1/2 particles under a Heisenberg Hamiltonian.
We introduce three optimizations to the algorithm: circular, iterative, and Bayesian.
arXiv Detail & Related papers (2021-05-07T21:41:08Z) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z) - Randomizing multi-product formulas for Hamiltonian simulation [2.2049183478692584]
We introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas on the other.
Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification.
Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth.
arXiv Detail & Related papers (2021-01-19T19:00:23Z) - 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.