Potential Uses for ALALI - QC-at-Davis/ALALI GitHub Wiki

ALALI is a package that can map qubits to atoms of biomolecules and chemical systems. To actually complete interesting work with this package, it is still up to you, the developer, to decide how to use the qubits for something meaningful. This would involve the use of quantum algorithms on the actual qubits in order to provide real value. To provide the most use on top of the constraint analysis that ALALI completes, the two algorithms that are simplest to set up on top of using ALALI would be the Grover search algorithm and the Quantum Fourier Transform. That being said, any quantum algorithm that uses CNOTs can be used on top of ALALI; working with algorithms that require SWAP gates would require changes to the topological constraint testing to track the atom placements per qubit. To see a whole list of potential algorithms and ways to implement them easily using IBM’s Qiskit, we highly recommend anyone to check out the “Quantum Algorithm Implementation for Beginners” written by the Los Alamos National Laboratory.

Grover Search Algorithm

The Grover search algorithm was first written by Lov Grover of Bell Labs in the late 1990s as a new way to speed up search across a database. Moreover, it proved to be a novel way that, given an output, one could find its input. Thus, it can provide a quick, precise, and simple way to search amongst atoms for important characteristics, such as polarity.

Moreover, Grover’s search has been thought to be an interesting way to model some of the electron holes within a molecule. Completing this accurately would require additional checking on top of ALALI in order to ensure that the electron holes are accurately wrapped into the atomic model. However, this paper, compared to the Grover example provided in “Quantum Algorithm Implementation for Beginners” does not require an oracle function to prepare the qubits, and suggests one could treat one of the other qubits as the hole. This could provide a new way to simplify Grover’s search, yet still uncover some very interesting information.

Quantum Fourier Transform

The fourier transform is a method of problem solving that is already quite ubiquitous in the field of signal processing, especially in image processing, where it is used to decompose images down to their sine and cosine components.

The Quantum Fourier Transform completes the same task as the classical Fourier transform, but over a set of quantum states rather than on a set of classical states. It is therefore a great tool when you have a set of complex quantum states and you are decomposing those for further observation or calculation.

One great system that would benefit from the use of this algorithm would be in the development of quantum computation based protein docking. Many classical protein docking solutions, such as ZDOCK, use Fast Fourier Transform to deconstruct the topological and electrostatic formulas across all potential ligands and serve as a simple way to determine changes between them and to score the overall binding arrangement. Thus, the use of a quantum fourier transform across quantum states created between atoms due to their electrostatic behavior should prove to be more accurate and potentially even faster, if all atoms are placed on the quantum computer.

Other Quantum Applications

In the field of chemistry, much of the focus in terms of the use of quantum computation is in the development of improvements to the Hartree-Fock model of quantum chemistry calculations, in which the main area of interest are the quantum states within the atoms and how they lead to electron difference that can affect binding. ALALI as a software package does not take the exacting electrons into consideration, and as such is not perfectly fit for quantum chemistry work. However, we do think that it may be possible to represent parts of the Hartree-Fock model per atom, as was completed by Rost, Jones, et. al. in their study of quantum beats using a quantum computer. To do this though, we highly recommend parallelization of quantum algorithms; that is, use the variational quantum eigensolver (VQE) or similar to complete base energy calculations, and then use the gate frequencies gained from that along with the atomic models provided by ALALI to begin quantum chemistry models.

In the field of biology, the work that we have seen has included ProteinQure and IBM investigating protein folding, ProteinQure, Menten AI, and Iff are looking into protein-protein and/or protein-ligand binding, and the Oregon Health and Science University looking at quantum algorithms for genetics applications. There is definitely room for improvement to all of these applications, especially with regards to scale of atoms used, the precision to which quantum features are kept, and the speed of these algorithms. We hope ALALI serves as a way to get started on solving these problems more quickly.

There is also much to be said about the use of ALALI to more easily and intuitively model the equations found within quantum biology, which is the study of quantum effects in biological systems above the electronic level. For those who are a bit more advanced in their understanding quantum mechanics and quantum gate setup, we would definitely recommend taking a look at Dr. Philip Kurian’s work in quantum entanglement in DNA, Dr. Daniel Manzano’s work in quantum reaction chemistry and quantum transport in biological systems, and Dr. Johnjoe McFadden’s work in quantum biology as whole to then lead into direct applications of quantum simulations for biological systems beyond the electronic level, especially in pigments and in energy generation pathways.