Adaptive variational quantum eigensolvers for highly excited states
- URL: http://arxiv.org/abs/2104.12636v2
- Date: Wed, 16 Feb 2022 15:37:09 GMT
- Title: Adaptive variational quantum eigensolvers for highly excited states
- Authors: Feng Zhang, Niladri Gomes, Yongxin Yao, Peter P. Orth, and Thomas
Iadecola
- Abstract summary: Highly excited states of quantum many-body systems are central objects in the study of quantum dynamics and thermalization.
We propose an adaptive variational algorithm, adaptive VQE-X, that self-generates a variational ansatz for arbitrary eigenstates of a many-body Hamiltonian $H$.
- Score: 4.038971004196936
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Highly excited states of quantum many-body systems are central objects in the
study of quantum dynamics and thermalization that challenge classical
computational methods due to their volume-law entanglement content. In this
work, we explore the potential of variational quantum algorithms to approximate
such states. We propose an adaptive variational algorithm, adaptive VQE-X, that
self-generates a variational ansatz for arbitrary eigenstates of a many-body
Hamiltonian $H$ by attempting to minimize the energy variance with respect to
$H$. We benchmark the method by applying it to an Ising spin chain with
integrable and nonintegrable regimes, where we calculate various quantities of
interest, including the total energy, magnetization density, and entanglement
entropy. We also compare the performance of adaptive VQE-X to an adaptive
variant of the folded-spectrum method. For both methods, we find a strong
dependence of the algorithm's performance on the choice of operator pool used
for the adaptive construction of the ansatz. In particular, an operator pool
including long-range two-body gates accelerates the convergence of both
algorithms in the nonintegrable regime. We also study the scaling of the number
of variational parameters with system size, finding that an exponentially large
number of parameters may be necessary to approximate individual highly excited
states. Nevertheless, we argue that these methods lay a foundation for the use
of quantum algorithms to study finite-energy-density properties of many-body
systems.
Related papers
- Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - GRAPE optimization for open quantum systems with time-dependent
decoherence rates driven by coherent and incoherent controls [77.34726150561087]
The GRadient Ascent Pulse Engineering (GRAPE) method is widely used for optimization in quantum control.
We adopt GRAPE method for optimizing objective functionals for open quantum systems driven by both coherent and incoherent controls.
The efficiency of the algorithm is demonstrated through numerical simulations for the state-to-state transition problem.
arXiv Detail & Related papers (2023-07-17T13:37:18Z) - Variational quantum algorithms for scanning the complex spectrum of
non-Hermitian systems [0.0]
We propose a variational method for solving the non-Hermitian Hamiltonian on a quantum computer.
The energy is set as a parameter in the cost function and can be tuned to obtain the whole spectrum.
Our work suggests an avenue for solving non-Hermitian quantum many-body systems with variational quantum algorithms on near-term noisy quantum computers.
arXiv Detail & Related papers (2023-05-31T12:50:22Z) - Exploring the role of parameters in variational quantum algorithms [59.20947681019466]
We introduce a quantum-control-inspired method for the characterization of variational quantum circuits using the rank of the dynamical Lie algebra.
A promising connection is found between the Lie rank, the accuracy of calculated energies, and the requisite depth to attain target states via a given circuit architecture.
arXiv Detail & Related papers (2022-09-28T20:24:53Z) - Variational quantum iterative power algorithms for global optimization [2.526320329485241]
We introduce a family of variational quantum algorithms called quantum iterative power algorithms (QIPA)
QIPA outperforms existing hybrid near-term quantum algorithms of the same kind.
We anticipate large-scale implementation and adoption of the proposed algorithm across current major quantum hardware.
arXiv Detail & Related papers (2022-08-22T17:45:14Z) - Twisted hybrid algorithms for combinatorial optimization [68.8204255655161]
Proposed hybrid algorithms encode a cost function into a problem Hamiltonian and optimize its energy by varying over a set of states with low circuit complexity.
We show that for levels $p=2,ldots, 6$, the level $p$ can be reduced by one while roughly maintaining the expected approximation ratio.
arXiv Detail & Related papers (2022-03-01T19:47:16Z) - Adaptive variational preparation of the Fermi-Hubbard eigenstates [0.0]
We prepare highly accurate ground states of the Fermi-Hubbard model for small grids up to 6 sites (12bits)
We show this adaptive method outperforms the non-adaptive counterpart in terms of fewer variational parameters, short gate depth, and scaling with the system size.
We also demonstrate the application of adaptive variational methods by preparing excited states and Green functions using a proposed ADAPT-SSVQE algorithm.
arXiv Detail & Related papers (2021-09-24T18:00:05Z) - 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) - 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) - Simulating Many-Body Systems with a Projective Quantum Eigensolver [0.0]
We present a new hybrid quantum-classical algorithm for optimizing unitary coupled-cluster wave functions.
We also introduce a selected variant of PQE (SPQE) that uses an adaptive ansatz built from arbitrary-order particle-hole operators.
arXiv Detail & Related papers (2021-01-31T00:31:12Z) - 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)
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.