Variational Quantum Algorithms

Many of the revolutionary advances expected from quantum computing assume the development of error-corrected computers containing thousands or millions of physical qubits. However, the realization of such devices has proven challenging, prompting the conception of a wide array of hybrid strategies which combine both quantum and classical computing resources, spanning a significant number of disciplines. Among these hybrid approaches, the first and most quintessential of these is the variational quantum eigensolver (VQE), followed soon after by the quantum approximate optimization algorithm (QAOA), and these approaches have inspired the emergence of an entire field that falls under the umbrella of variational quantum algorithms (VQA). VQAs have now been applied across several domain sciences, among which chemistry, physics, optimization, and machine learning are the most significant. While each of these areas has particular demands that translate into specific circuit, workload, software, and compilation designs, the corresponding VQAs also present striking similarities, even across distinct applications.

Variational quantum algorithms (VQA) aspire to significantly speed-up several crucial computational tasks by utilizing noisy intermediate-scale quantum (NISQ) computers while also circumventing the recurring limitations imposed by these devices as well as the disparity that rises from this heterogeneous computational model. Before realizing the computational advantages within this paradigm, several challenges must first be addressed across all levels along the computational workflow. Several of these can be cast into a VQA, such as; physical simulation, the search for suitable routes to encode/embed data, determining the set of rotation angles which implement a given quantum gate within a chosen circuit, and many others. Development and analyses of novel hybrid quantum-classical VQAs are required to assess the potential of achieving computational advantages and addressing real-world applications such as constrained optimization. Thus, it is of great interest to the quantum computing community at large to avail itself of a forum to report on the current state of VQAs for broad classes of problems.

The persistent challenges posed by quantum computers have served as the driving force in the search for amenable strategies which can impart computational advantages before fault-tolerant devices are available. Chief among these strategies are variational quantum algorithms (VQA), which in turn introduce a slew of both challenges and opportunities. Given the importance of VQA in the current quantum landscape, it is, therefore, the goal of this Research Topic to provide an avenue for the quantum computing community at large to share the latest findings in several aspects relevant to VQAs.

Topics of interest include, but are not limited to:

- Novel VQAs
- Domain science applications (novel domains are particularly encouraged, e.g., finance, human-centered computing, art, music, and games)
- Advances in the efficiency/scale/resource demand of existing VQAs
- Software that enables VQA simulation
- VQAs for pulse-level control and quantum compilation
- Methods that exploit problem-specific information
- QAOA applications, including extensions to problems beyond MaxCut

Publishing fees as well as fee support applications can be found online or by contacting the editors. https://www.frontiersin.org/journals/quantum-science-and-technology/for-authors/publishing-fees

Keywords: Variational, Quantum, Algorithms, Hybrid, NISQ, VQE, QAOA, Quantum Machine Learning

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.