MOBILITY ENGINEERING Produced by SAE Media Group
MOBILITY ENGINEERING
Magazines
Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

Magazine cover

Current Issue

Archives

NRL Scientists Deliver Quantum Algorithm to Develop New Materials and Chemistry

U.S. Naval Research Laboratory scientists unveil the Cascaded Variational Quantum Eigensolver (CVQE) algorithm, which is expected to become a powerful tool to investigate the physical properties in electronic systems for disruptive defense technologies.

NRL researchers have developed the Cascaded Variational Quantum Eigensolver (CVQE) algorithm expected to become a powerful tool to investigate the physical properties in electronic systems featured in ships and other naval assets, platforms and technologies. (Image: NRL)

U.S. Naval Research Laboratory (NRL) scientists published the Cascaded Variational Quantum Eigensolver (CVQE) algorithm in a recent Physical Review Research article, and it's expected to become a powerful tool to investigate the physical properties in electronic systems.

The CVQE algorithm is a variant of the Variational Quantum Eigensolver (VQE) algorithm that only requires the execution of a set of quantum circuits once rather than at every iteration during the parameter optimization process, thereby increasing the computational throughput.

“Both algorithms produce a quantum state close to the ground state of a system, which is used to determine many of the system’s physical properties,” said John Stenger, Ph.D., a Theoretical Chemistry Section research physicist. “Calculations that previously took months can now be performed in hours.”

The CVQE algorithm uses a quantum computer to probe the needed probability mass functions and a classical computer to perform the remaining calculations, including the energy minimization.

“Finding the minimum energy is computationally hard as the size of the state space grows exponentially with the system size,” said Steve Hellberg, Ph.D., a Theory of Advanced Functional Materials Section Research Physicist. “Except for very small systems, even the world’s most powerful supercomputers are unable to find the exact ground state.”

To address this challenge, scientists use a quantum computer with a qubit register, whose state space also increases exponentially, in this case with qubits. By representing the states of a physical system on the state space of the register, a quantum computer can be used to simulate the states in the exponentially large representation space of the system.

Data can subsequently be extracted by quantum measurements. As quantum measurements are not deterministic, the quantum circuit executions must be repeated multiple times to estimate probability distributions describing the states, a process known as sampling. Variational quantum algorithms, including the CVQE algorithm, identify trial states by a set of parameters that are optimized to minimize the energy.

“The key difference between the original VQE method and the new CVQE method is that the sampling and optimization processes have been decoupled in the latter such that the sampling can be performed exclusively on the quantum computer and the parameters processed exclusively on a classical computer,” said Dan Gunlycke, D.Phil., Theoretical Chemistry Section Head, who also leads the NRL quantum computing effort. “The new approach also has other benefits. The form of the solution space does not have to comport with the symmetry requirements of the qubit register, and therefore, it is much easier to shape the solution space and implement symmetries of the system and other physically motivated constraints, which will ultimately lead to more accurate predictions of electronic system properties.”

Quantum computing is a component of quantum science, which has been designated as a Critical Technology Area by the Under Secretary of Defense for Research and Engineering, Heidi Shyu.

“Understanding the properties of quantum-mechanical systems is essential in the development of new materials and chemistry for the Navy and Marine Corps,” Gunlycke said. “Corrosion, for instance, is an omnipresent challenge costing the Department of Defense billions every year. The CVQE algorithm can be used to study the chemical reactions causing corrosion and provide critical information to our existing anticorrosion teams in their quest to develop better coatings and additives.”

This research was performed by John Stenger, Ph.D., a Theoretical Chemistry Section Research Physicist, for the Naval Research Laboratory (Washington D.C.). For more information, download the Technical Support Package (free white paper) below. ADTTSP-05243

Topics:
Aerospace Computers Electronic control systems Electronics & Computers Energy Materials Materials properties Mathematical models Research and development Simulation and modeling Test facilities Tools and equipment
This Brief includes a Technical Support Package (TSP).
Document cover
Cascaded Variational Quantum Eigensolver Algorithm

(reference ADTTSP-05243) is currently available for download from the TSP library.

Don't have an account?

More From SAE Media Group

    Magazine cover
    Aerospace & Defense Technology Magazine

    This article first appeared in the May, 2024 issue of Aerospace & Defense Technology Magazine (Vol. 9 No. 3).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The document presents a novel approach to quantum computing through the introduction of a Cascaded Variational Quantum Eigensolver (CVQE) algorithm. This algorithm aims to enhance computational efficiency in simulating quantum-mechanical systems, which is a significant challenge due to the exponential growth of the Hilbert space with system size. Traditional methods, such as the Variational Quantum Eigensolver (VQE), require numerous executions of quantum circuits on a quantum processing unit (QPU) for each iteration of parameter optimization, which can limit throughput and increase computational demands.

    The CVQE algorithm addresses this limitation by executing a set of quantum circuits only once rather than at every iteration. This change significantly increases computational throughput, as it reduces the number of required quantum circuit executions. The algorithm utilizes a QPU to probe necessary probability mass functions while a classical processing unit handles the remaining calculations, including energy minimization. This separation of tasks allows for a more efficient optimization process and helps mitigate errors introduced during quantum circuit executions.

    The document also discusses the mathematical framework underlying the CVQE algorithm, including the use of a two-site Hubbard model to test the closed-form expression of energy. The Hamiltonian for this model is presented, illustrating how the algorithm can be applied to calculate the energy of electronic systems. The authors emphasize the flexibility of the ansatz form used in the CVQE, which does not restrict the Fock space and allows for the implementation of symmetry and other physically motivated constraints.

    Additionally, the document highlights the importance of sampling in the CVQE algorithm. It explains how measurements in a specific basis can yield probability mass functions that are essential for approximating expectation values. The authors note that the choice of sample size is crucial for achieving the desired statistical accuracy.

    In summary, the CVQE algorithm represents a significant advancement in quantum computing, particularly for simulating complex quantum systems. By optimizing the execution of quantum circuits and leveraging both quantum and classical resources, this approach promises to improve computational efficiency and fidelity, making it a valuable tool for researchers in the field of quantum mechanics.

    Top Stories

    INSIDERManned Systems

    Feature Image New Copper Alloy Could Provide Breakthrough in Durability for Military Systems

    INSIDERManned Systems

    Feature Image Boeing to Develop New Space Force Nuclear Communications Satellites

    INSIDERAerospace

    Feature Image TEWS Introduces First VITA 93 QMC Modules

    INSIDERCommunications

    Feature Image Metamaterials Antenna Makes Generational Leap in Multi-Band Connectivity

    INSIDERAerospace

    Feature Image Regulation and Technology Changes Needed for Drone Detection, Mitigation in US...

    INSIDERRF & Microwave Electronics

    Feature Image Paris Air Show: New Aerospace Technologies, Updates and Research

    Webcasts