Characterization of variational quantum algorithms using free fermions
- URL: http://arxiv.org/abs/2206.06400v2
- Date: Tue, 23 Aug 2022 17:54:06 GMT
- Title: Characterization of variational quantum algorithms using free fermions
- Authors: Gabriel Matos, Chris N. Self, Zlatko Papi\'c, Konstantinos
Meichanetzidis, and Henrik Dreyer
- Abstract summary: We numerically study the interplay between these symmetries and the locality of the target state.
We find that the number of iterations to converge to the solution scales linearly with system size.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study variational quantum algorithms from the perspective of free
fermions. By deriving the explicit structure of the associated Lie algebras, we
show that the Quantum Approximate Optimization Algorithm (QAOA) on a
one-dimensional lattice -- with and without decoupled angles -- is able to
prepare all fermionic Gaussian states respecting the symmetries of the circuit.
Leveraging these results, we numerically study the interplay between these
symmetries and the locality of the target state, and find that an absence of
symmetries makes nonlocal states easier to prepare. An efficient classical
simulation of Gaussian states, with system sizes up to $80$ and deep circuits,
is employed to study the behavior of the circuit when it is overparameterized.
In this regime of optimization, we find that the number of iterations to
converge to the solution scales linearly with system size. Moreover, we observe
that the number of iterations to converge to the solution decreases
exponentially with the depth of the circuit, until it saturates at a depth
which is quadratic in system size. Finally, we conclude that the improvement in
the optimization can be explained in terms of of better local linear
approximations provided by the gradients.
Related papers
- A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval [56.67706781191521]
In this work, we present a robust phase retrieval problem where the task is to recover an unknown signal.
Our proposed oracle avoids the need for computationally spectral descent, using a simple gradient step and outliers.
arXiv Detail & Related papers (2024-09-07T06:37:23Z) - Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method [0.0]
We present quantum algorithms for the variational optimization of wavefunctions correlated by products of unitary operators.
While an implementation on classical computing hardware would require exponentially growing compute cost, the cost (number of circuits and shots) of our quantum algorithms is in system size.
arXiv Detail & Related papers (2024-08-03T17:53:35Z) - Stochastic Gradient Descent for Gaussian Processes Done Right [86.83678041846971]
We show that when emphdone right -- by which we mean using specific insights from optimisation and kernel communities -- gradient descent is highly effective.
We introduce a emphstochastic dual descent algorithm, explain its design in an intuitive manner and illustrate the design choices.
Our method places Gaussian process regression on par with state-of-the-art graph neural networks for molecular binding affinity prediction.
arXiv Detail & Related papers (2023-10-31T16:15:13Z) - 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) - Parsimonious Optimisation of Parameters in Variational Quantum Circuits [1.303764728768944]
We propose a novel Quantum-Gradient Sampling that requires the execution of at most two circuits per iteration to update the optimisable parameters.
Our proposed method achieves similar convergence rates to classical gradient descent, and empirically outperforms gradient coordinate descent, and SPSA.
arXiv Detail & Related papers (2023-06-20T18:50:18Z) - Optimizing quantum circuits with Riemannian gradient flow [0.5524804393257919]
Variational quantum algorithms are a promising class algorithms that can be performed on currently available quantum computers.
We consider an alternative optimization perspective that depends on the structure of the special unitary group.
arXiv Detail & Related papers (2022-02-14T19:00:06Z) - Progress towards analytically optimal angles in quantum approximate
optimisation [0.0]
The Quantum Approximate optimisation algorithm is a $p$ layer, time-variable split operator method executed on a quantum processor.
We prove that optimal parameters for $p=1$ layer reduce to one free variable and in the thermodynamic limit, we recover optimal angles.
We moreover demonstrate that conditions for vanishing gradients of the overlap function share a similar form which leads to a linear relation between circuit parameters, independent on the number of qubits.
arXiv Detail & Related papers (2021-09-23T18:00:13Z) - 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) - 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) - Local optimization on pure Gaussian state manifolds [63.76263875368856]
We exploit insights into the geometry of bosonic and fermionic Gaussian states to develop an efficient local optimization algorithm.
The method is based on notions of descent gradient attuned to the local geometry.
We use the presented methods to collect numerical and analytical evidence for the conjecture that Gaussian purifications are sufficient to compute the entanglement of purification of arbitrary mixed Gaussian states.
arXiv Detail & Related papers (2020-09-24T18:00:36Z) - Measuring Analytic Gradients of General Quantum Evolution with the
Stochastic Parameter Shift Rule [0.0]
We study the problem of estimating the gradient of the function to be optimized directly from quantum measurements.
We derive a mathematically exact formula that provides an algorithm for estimating the gradient of any multi-qubit parametric quantum evolution.
Our algorithm continues to work, although with some approximations, even when all the available quantum gates are noisy.
arXiv Detail & Related papers (2020-05-20T18:24:11Z)
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.