Surrogate optimization of variational quantum circuits
- URL: http://arxiv.org/abs/2404.02951v1
- Date: Wed, 3 Apr 2024 18:00:00 GMT
- Title: Surrogate optimization of variational quantum circuits
- Authors: Erik J. Gustafson, Juha Tiihonen, Diana Chamaki, Farshud Sorourifar, J. Wayne Mullinax, Andy C. Y. Li, Filip B. Maciejewski, Nicolas PD Sawaya, Jaron T. Krogel, David E. Bernal Neira, Norm M. Tubman,
- Abstract summary: Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications.
Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
- Score: 1.0546736060336612
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.
Related papers
- 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) - 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) - Parallel circuit implementation of variational quantum algorithms [0.0]
We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution.
We apply this specifically to optimization problems, where inherent structures from the problem can be identified.
We show that not only can our method address larger problems, but that it is also possible to run full VQA models while training parameters using only one slice.
arXiv Detail & Related papers (2023-04-06T12:52:29Z) - Faster variational quantum algorithms with quantum kernel-based
surrogate models [0.0]
We present a new method for small-to-intermediate scale variational algorithms on noisy quantum processors.
Our scheme shifts the computational burden onto the classical component of these hybrid algorithms, greatly reducing the number of queries to the quantum processor.
arXiv Detail & Related papers (2022-11-02T14:11:25Z) - 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) - Surrogate-based optimization for variational quantum algorithms [0.0]
Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers.
We introduce the idea of learning surrogate models for variational circuits using few experimental measurements.
We then perform parameter optimization using these models as opposed to the original data.
arXiv Detail & Related papers (2022-04-12T00:15:17Z) - 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) - Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
We propose that quantum circuits can be modeled as queuing networks.
Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
arXiv Detail & Related papers (2021-06-26T10:55:52Z) - 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) - 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) - Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial
Optimization [0.14755786263360526]
We explore which quantum algorithms for optimization might be most practical to try out on a small fault-tolerant quantum computer.
Our results discourage the notion that any quantum optimization realizing only a quadratic speedup will achieve an advantage over classical algorithms.
arXiv Detail & Related papers (2020-07-14T22:54:04Z)
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.