Machine Learning Aided Dimensionality Reduction towards a Resource
Efficient Projective Quantum Eigensolver
- URL: http://arxiv.org/abs/2303.11266v1
- Date: Mon, 20 Mar 2023 16:49:56 GMT
- Title: Machine Learning Aided Dimensionality Reduction towards a Resource
Efficient Projective Quantum Eigensolver
- Authors: Sonaldeep Halder, Chayan Patra, Dibyendu Mondal and Rahul Maitra
- Abstract summary: Recently developed Projective Quantum Eigensolver (PQE) has been demonstrated as an elegant methodology to compute the ground state energy of molecular systems.
We have exploited the collective interplay of these two sets of parameters via machine learning techniques to bring out the synergistic inter-relationship.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The recently developed Projective Quantum Eigensolver (PQE) has been
demonstrated as an elegant methodology to compute the ground state energy of
molecular systems in Noisy Intermdiate Scale Quantum (NISQ) devices. The
iterative optimization of the ansatz parameters involves repeated construction
of residues on a quantum device. The quintessential pattern of the iteration
dynamics, when projected as a time discrete map, suggests a hierarchical
structure in the timescale of convergence, effectively partitioning the
parameters into two distinct classes. In this work, we have exploited the
collective interplay of these two sets of parameters via machine learning
techniques to bring out the synergistic inter-relationship among them that
triggers a drastic reduction in the number of quantum measurements necessary
for the parameter updates while maintaining the characteristic accuracy of PQE.
Furthermore the machine learning model may be tuned to capture the noisy data
of NISQ devices and thus the predicted energy is shown to be resilient under a
given noise model.
Related papers
- Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware.
We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
arXiv Detail & Related papers (2024-03-13T13:27:40Z) - Adaptive variational quantum minimally entangled typical thermal states
for finite temperature simulations [0.0]
We describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm.
The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits.
arXiv Detail & Related papers (2023-01-06T16:40:06Z) - Resource-frugal Hamiltonian eigenstate preparation via repeated quantum
phase estimation measurements [0.0]
Preparation of Hamiltonian eigenstates is essential for many applications in quantum computing.
We adopt ideas from variants of this method to implement a resource-frugal iterative scheme.
We characterise an extension involving a modification of the target Hamiltonian to increase overall efficiency.
arXiv Detail & Related papers (2022-12-01T20:07:36Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - Symmetric Pruning in Quantum Neural Networks [111.438286016951]
Quantum neural networks (QNNs) exert the power of modern quantum machines.
QNNs with handcraft symmetric ansatzes generally experience better trainability than those with asymmetric ansatzes.
We propose the effective quantum neural tangent kernel (EQNTK) to quantify the convergence of QNNs towards the global optima.
arXiv Detail & Related papers (2022-08-30T08:17:55Z) - Direct parameter estimations from machine-learning enhanced quantum
state tomography [3.459382629188014]
Machine-learning enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states.
We develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly.
Such a characteristic model-based ML-QST can avoid the problem of dealing with large Hilbert space, but keep feature extractions with high precision.
arXiv Detail & Related papers (2022-03-30T15:16:02Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - A variational quantum eigensolver for dynamic correlation functions [0.9176056742068814]
We show how the calculation of zero-temperature dynamic correlation functions can be recast into a modified VQE algorithm.
This allows for important physical expectation values describing the dynamics of the system to be directly converged on the frequency axis.
We believe the approach shows potential for the extraction of frequency dynamics of correlated systems on near-term quantum processors.
arXiv Detail & Related papers (2021-05-04T18:52:45Z) - 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) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
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.