Randomized adaptive quantum state preparation
- URL: http://arxiv.org/abs/2301.04201v2
- Date: Fri, 6 Oct 2023 19:29:07 GMT
- Title: Randomized adaptive quantum state preparation
- Authors: Alicia B. Magann, Sophia E. Economou, Christian Arenz
- Abstract summary: A cost function is minimized to prepare a desired quantum state through an adaptively constructed quantum circuit.
We provide theoretical arguments and numerical evidence that convergence to the target state can be achieved for almost all initial states.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We develop an adaptive method for quantum state preparation that utilizes
randomness as an essential component and that does not require classical
optimization. Instead, a cost function is minimized to prepare a desired
quantum state through an adaptively constructed quantum circuit, where each
adaptive step is informed by feedback from gradient measurements in which the
associated tangent space directions are randomized. We provide theoretical
arguments and numerical evidence that convergence to the target state can be
achieved for almost all initial states. We investigate different randomization
procedures and develop lower bounds on the expected cost function change, which
allows for drawing connections to barren plateaus and for assessing the
applicability of the algorithm to large-scale problems.
Related papers
- Quantum automated learning with provable and explainable trainability [4.305036822025956]
We introduce quantum automated learning, where no variational parameter is involved and the training process is converted to quantum state preparation.
We show that such a training process can be understood from the perspective of preparing quantum states by imaginary time evolution.
Our results establish an unconventional quantum learning strategy that is gradient-free with provable and explainable trainability.
arXiv Detail & Related papers (2025-02-07T19:00:02Z) - Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement [39.58317527488534]
Quantum Circuits (PQCs) are still not fully understood outside the scope of their principal application.
We analyse the generation of random states in PQCs under restrictions on the qubits connectivities.
We place a connection between how steep is the increase on the uniformity of the distribution of the generated states and the generation of entanglement.
arXiv Detail & Related papers (2024-05-03T17:32:55Z) - Solving reaction dynamics with quantum computing algorithms [42.408991654684876]
We study quantum algorithms for response functions, relevant for describing different reactions governed by linear response.
We focus on nuclear-physics applications and consider a qubit-efficient mapping on the lattice, which can efficiently represent the large volumes required for realistic scattering simulations.
arXiv Detail & Related papers (2024-03-30T00:21:46Z) - Measurement-Based Quantum Approximate Optimization [0.24861619769660645]
We focus on measurement-based quantum computing protocols for approximate optimization.
We derive measurement patterns for applying QAOA to the broad and important class of QUBO problems.
We discuss the resource requirements and tradeoffs of our approach to that of more traditional quantum circuits.
arXiv Detail & Related papers (2024-03-18T06:59:23Z) - 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) - Improved iterative quantum algorithm for ground-state preparation [4.921552273745794]
We propose an improved iterative quantum algorithm to prepare the ground state of a Hamiltonian system.
Our approach has advantages including the higher success probability at each iteration, the measurement precision-independent sampling complexity, the lower gate complexity, and only quantum resources are required when the ancillary state is well prepared.
arXiv Detail & Related papers (2022-10-16T05:57:43Z) - Fundamental limitations on optimization in variational quantum
algorithms [7.165356904023871]
A leading paradigm to establish such near-term quantum applications is variational quantum algorithms (VQAs)
We prove that for a broad class of such random circuits, the variation range of the cost function vanishes exponentially in the number of qubits with a high probability.
This result can unify the restrictions on gradient-based and gradient-free optimizations in a natural manner and reveal extra harsh constraints on the training landscapes of VQAs.
arXiv Detail & Related papers (2022-05-10T17:14:57Z) - Dynamical learning of a photonics quantum-state engineering process [48.7576911714538]
Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols.
We implement an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states.
This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.
arXiv Detail & Related papers (2022-01-14T19:24:31Z) - FLIP: A flexible initializer for arbitrarily-sized parametrized quantum
circuits [105.54048699217668]
We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits.
FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters.
We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
arXiv Detail & Related papers (2021-03-15T17:38:33Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z) - 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.