Researchers at Virginia Tech have devised a plan to make quantum computers more useful by developing an algorithm that can be tailored to different types of quantum computers and specific problems being solved.
Led by Ed Barnes, professor of physics, and Sophia Economou, director of the Virginia Tech Center for Quantum Information Science and Engineering, the team has received a five-year, $5 million grant from the Department of Energy to put this plan into action.
The goal is to overcome the current limitation of quantum computers, which are still not able to outperform today’s computers on problem-solving tasks. By leveraging an approach that focuses on improving algorithms for near-term quantum applications, the team aims to bring this next milestone closer.
The researchers will test their approach in three main application areas: chemistry, machine learning, and materials physics, using different types of quantum computers, including those based on superconducting materials, nanostructures fabricated in silicon, and atoms trapped and controlled by light.
Building a Quantum Computer that’s Actually Useful
Quantum computers have yet to outperform today’s computers on problem-solving tasks, but researchers at Virginia Tech are working towards bringing this next milestone closer. They plan to achieve this by applying an algorithm that can be tailored simultaneously to different types of quantum computers and specific problems being solved.
Quantum computers process information in a fundamentally new way, drawing on the counterintuitive behavior of physics at the quantum level. This allows algorithms for certain types of computations to run exponentially faster. However, despite rapid advancements in technology, quantum computing is still in its early days. Currently, moderately sized quantum processors can perform various tasks, but they are not yet capable of having a significant impact on important problems, such as drug design or energy harvesting.
The Limitations of Existing Algorithms
One major holdup in the development of practical quantum computers is the clunky algorithms currently available. Most existing quantum algorithms are designed to run on universal quantum machines, which may not be the most efficient approach. Researchers believe that a long-term middle ground will emerge, where different quantum computers are used depending on the problems being solved.
The Diversity of Quantum Computers
Quantum computers come in diverse forms and functions, each with its strengths and weaknesses. For instance, some quantum computers are based on electronic circuits made from superconducting materials, while others use nanostructures fabricated in silicon or atoms trapped and controlled by light. These distinct machines may be better suited for solving different types of problems, and different algorithms may run more efficiently on different machines.
The Virginia Tech Approach
To improve performance, researchers at Virginia Tech are leveraging an approach that focuses on improving algorithms for near-term quantum applications. This involves tailoring the algorithm to the specific problem being solved and the type of quantum computer being used. By doing so, the team aims to enable the computer to reach a solution faster. The researchers will be testing their approach in three main application areas: chemistry, machine learning, and materials physics.
The Future of Quantum Computing
The Department of Energy has awarded the interdisciplinary, multi-university team a five-year, $5 million grant to put this plan into action. With this funding, the team hopes to bring quantum computing closer to being a practical tool for solving real-world problems. By developing algorithms that can be tailored to different types of quantum computers and specific problems, researchers believe they can unlock the full potential of quantum computing and make it a valuable asset in various fields.
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