An burgeoning discipline at the nexus of computer science, physics, and engineering, Christian Arenz seeks to unlock the potential of quantum computing. Together with Sophia Economou, the T. Marshall Hahn professor of physics at Virginia Tech, the assistant professor of electrical engineering at Arizona State University’s School of Electrical, Computer and Energy Engineering is working to create new algorithms that will enable advanced quantum computing capabilities. An $800,000 National Science Foundation grant supporting the researchers’ work on the sophisticated algorithms names Economou as the senior major co-investigator. The ExpandQISE initiative also involves the creation of a textbook and training courses for teachers to help them communicate quantum scientific concepts to students in a way that is more user-friendly.

At extremely small scales, matter behaves in a manner that is inconsistent with the laws of quantum physics. Heisenberg’s famed uncertainty principle, which states that if we know a quantum mechanical object’s precise position, then its momentum must be uncertain, is a well-known illustration of this. Questions and concepts about what it would mean to build a computer using just quantum mechanical components—a quantum computer—started to surface in the 1980s. Early research revealed that such a computer might be effective for a variety of high-value tasks, including quantum simulation and cryptography. These results are ultimately responsible for the current upsurge in interest in developing quantum computer technology.

Bits, which might be zero or one or on or off, are the foundation of traditional computers, according to Arenz. “Qubits, the fundamental building blocks of quantum computing, are capable of existing in simultaneous zero-and-one combinations known as’superposition’ states. Additionally, qubits have the ability to entangle with one another, which results in correlations that defy simple explanation. Due to these peculiarly quantum mechanical characteristics, quantum computing opens the door to a new paradigm of computation. Prototype quantum computers are now being developed at a large number of organizations in business, government, and academia. It is challenging to scale up these prototype devices to address robust computational issues because of noise and defects in the system, which continue to limit their power. Hybrid quantum-classical algorithms have been created to address this problem. These algorithms try to maximize the performance of noisy quantum computers by linking them to a classical computer and employing both computers simultaneously to solve a problem. For a variety of applications, including machine learning, quantum simulation, and optimization, hybrid quantum-classical algorithms have been created.

In recent years, the quantum information science community has paid close attention to these algorithms in the hopes that they would offer a computational edge over conventional computers for the solution of important problems. The only calculations that quantum computers have so far been able to perform quicker than a conventional computer lack any immediate use. In order to optimize a set of parameters in a quantum computation, hybrid quantum-classical algorithms use a classical computer. The details of the parameters entering into each phase are often left open-ended, while the overall structure of a quantum computation is typically kept set. The idea behind these algorithms is to find answers to complex social issues, such optimizing the use of scarce resources like food or medication, more effectively than traditional computers and human brainpower. They do this by looking for the parameter values that best solve the problem at hand.

However, quantum computing poses a significant obstacle since, as the researchers point out, it’s frequently difficult to predict the parameters that need be entered into a quantum processor in order to solve problems that are important to us. According to Arenz, the standard procedure is to modify a quantum algorithm’s requirements before using it to compute a solution. “However, we can’t promise that doing that will result in the best solution.” Arenz compares the circumstance like climbing a mountain. A user wants a quantum computer to produce the best answer to a problem, much like a climber might want to reach the summit, the highest point of a mountain. A hiker finds it challenging to estimate the peak’s height by sight. Although they may believe to be at the highest mountain summit, they cannot be certain since other peaks appear to be of a comparable height.

Similarly, according to Arenz, a quantum computer’s solution can appear to be the best, but there may be additional ideal results the device can provide. In order to create adaptable quantum algorithms, Arenz and his associates have built on earlier work by Economou and her Virginia Tech colleagues. The size of a quantum circuit can be changed using adaptive quantum algorithms. Adding extra “gates,” or simply performing additional calculations based on results returned by a quantum computer, allows adaptive algorithms to be expanded. This increases the quantum circuit’s size to the extent required to obtain the most accurate calculation results. According to Economou, “These adaptive algorithms offer a simulation technique that is adapted to the system that is simulated.” Even the adaptive algorithms’ results are a “best estimate” at the moment when it comes to computing a solution. In order to guarantee the optimal result from quantum calculations, the team’s research aims to enhance adaptive algorithms by including a critical component. According to Alicia Magann, a Sandia National Laboratories Truman Fellow researcher and project collaborator, “once you start to think about adding unpredictability, that’s the key.”

Randomness, in essence, shakes up what the computer is doing, pushing the calculations in the direction of the best result. Another comparison to hiking is made by Arenz: a hiker who is determined to attain a peak will continue to ascend despite difficulties they confront. The hiker will keep going and overcoming challenges until they reach the highest peak. They know they’ve reached their target when there are no longer any challenges and the steep slopes begin to level off. The ASU-led Quantum Collaborative will be extremely helpful to this project. The Quantum Collaborative, a significant component of the IBM Quantum Network quantum computing knowledge collective, will provide student researchers with worthwhile chances to implement and test adaptive quantum algorithms on IBM’s cutting-edge quantum computers. According to Arenz, the challenging arithmetic required to study quantum information science, which includes quantum computing, scares away many potential students. To introduce potential students and teachers to the subject more easily, the research team is creating a new textbook and training materials. According to Arenz, the textbook outlines with illustrations the principles you must introduce to understand how quantum computing operates.

The Virginia Tech team led by Economou is overseeing the creation of this textbook and the course that goes along with it for first-year undergraduate students. Teachers in K–12 teacher preparation programs will learn in summer programs how to instruct pupils on quantum issues, and they will then introduce the subject to their students as part of their regular curricula. The K–12 quantum information science curriculum would concentrate on presenting the topic without initially emphasizing complicated arithmetic, similar to the college course. Arenz wants to help students of different backgrounds comprehend the basics of quantum theory. He claims that the “scary” arithmetic currently dominates the initial chapters of a textbook on quantum mechanics, scaring away many students from the challenging topic. Arenz is pleased about the chance for ASU to further understanding in the subject as he looks into the future of quantum computing. You should incorporate several subjects, and ASU, in Arenz’s opinion, is the perfect setting for doing so. Because quantum information science is interdisciplinary, this university has experts in almost every field.