9 research outputs found
Randomized adaptive quantum state preparation
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
Quantum tracking control of the orientation of symmetric top molecules
The goal of quantum tracking control is to identify shaped fields to steer
observable expectation values along designated time-dependent tracks. The
fields are determined via an iteration-free procedure, which is based on
inverting the underlying dynamical equations governing the controlled
observables. In this article, we generalize the ideas in Phys. Rev. A 98,
043429 (2018) to the task of orienting symmetric top molecules in 3D. To this
end, we derive equations for the control fields capable of directly tracking
the expected value of the 3D dipole orientation vector along a desired path in
time. We show this framework can be utilized for tracking the orientation of
linear molecules as well, and present numerical illustrations of these
principles for symmetric top tracking control problems
Spectral Gaps via Imaginary Time
The spectral gap occupies a role of central importance in many open problems
in physics. We present an approach for evaluating the spectral gap of a
Hamiltonian from a simple ratio of two expectation values, both of which are
evaluated using a quantum state that is evolved in imaginary time. In
principle, the only requirement is that the initial state is supported on both
the ground and first excited states. We demonstrate this approach for the
Fermi-Hubbard and transverse field Ising models through numerical simulation.Comment: 6 pages, 1 figure, 1 tabl
Sequential optical response suppression for chemical mixture characterization
The characterization of mixtures of non-interacting, spectroscopically similar quantum components has important applications in chemistry, biology, and materials science. We introduce an approach based on quantum tracking control that allows for determining the relative concentrations of constituents in a quantum mixture, using a single pulse which enhances the distinguishability of components of the mixture and has a length that scales linearly with the number of mixture constituents. To illustrate the method, we consider two very distinct model systems: mixtures of diatomic molecules in the gas phase, as well as solid-state materials composed of a mixture of components. A set of numerical analyses are presented, showing strong performance in both settings
HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware
In order to characterize and benchmark computational hardware, software, and
algorithms, it is essential to have many problem instances on-hand. This is no
less true for quantum computation, where a large collection of real-world
problem instances would allow for benchmarking studies that in turn help to
improve both algorithms and hardware designs. To this end, here we present a
large dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib
(for Hamiltonian Library), is freely available online and contains problem
sizes ranging from 2 to 1000 qubits. HamLib includes problem instances of the
Heisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronic
structure, molecular vibrational structure, MaxCut, Max-k-SAT, Max-k-Cut,
QMaxCut, and the traveling salesperson problem. The goals of this effort are
(a) to save researchers time by eliminating the need to prepare problem
instances and map them to qubit representations, (b) to allow for more thorough
tests of new algorithms and hardware, and (c) to allow for reproducibility and
standardization across research studies
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HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware
For a considerable time, large datasets containing problem instances have proven valuable for analyzing computer hardware, software, and algorithms. One notable example of the value of large datasets is ImageNet [1], a vast repository of images that has been instrumental in testing numerous deep learning packages. Similarly, in the domain of computational chemistry and materials science, the availability of extensive datasets such as the Protein Data Bank [2], the Materials Project [3], and QM9 [4] has greatly facilitated the evaluation of new algorithms and software approaches, while also promoting standardization within the field. These well-defined datasets and problem instances, in turn, serve as the foundation for creating benchmarking suites like MLPerf [5] and LINPACK [6], [7]. These suites enable fair and rigorous comparisons of different methodologies and solutions, fostering continuous advancements in various areas of computer science and beyond