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Quantum Computing

Quantum Computing Algorithm Could Lead to Design of New Materials



A team of researchers at Columbia University have developed a new algorithm that could help quantum computers calculate molecular energy and lead to the design of new materials. The algorithm uses the most quantum bits to date to calculate ground state energy, which is the lowest-energy state in a quantum mechanical system. 

The new study was published in Nature

Calculating Ground State Energy

The algorithm was developed by Columbia chemistry professor David Reichman and postdoc Joonho Lee, along with researchers at Google Quantum AI. It reduces the statistical errors that are produced by quantum bits in chemistry equations, and it uses up to 16 qubits on Google’s 53-qubit Sycamore computer to calculate ground state energy, which is the lowest energy state of a molecule. 

“These are the largest quantum chemistry calculations that have ever been done on a real quantum device,” Reichman said. 

By being able to accurately calculate ground state energy, chemists will be able to develop new materials. For example, the algorithm could be used to design materials that speed up nitrogen fixation for farming. This is just one of the many possible sustainability uses, according to Lee, who is a visiting researcher at Google Quantum AI.

The algorithm relies on a quantum Monte Carlo, which is a system of methods for calculating probability when there are many random, unknown variables. The researchers deployed the algorithm to determine the ground state energy of three types of molecules. 

There are many variables that can influence ground state energy, such as the number of electrons in a molecule, the direction of their spin, and the paths they take when orbiting a nucleus. The electronic energy is encoded in the Schrodinger equation, which becomes extremely hard to solve on a classical computer as molecules get bigger. With that said, there are methods for making this easier, and quantum computers could eventually bypass this exponential scaling problem. 

Handling Larger and More Complex Calculations

According to principle, it should be possible for quantum computers to handle larger and more complex calculations since the qubits take advantage of quantum states. Qubits are able to exist in two states simultaneously, which is not true for binary digits. At the same time, qubits are fragile, and as the number of qubits increases, accuracy in the final answer decreases. Lee developed the new algorithm to leverage the combined power of both classical and quantum computers to solve these complex equations more efficiently while also minimizing mistakes. 

“It’s the best of both worlds,” Lee said. “We leveraged tools that we already had as well as tools that are considered state-of-the-art in quantum information science to refine quantum computational chemistry,” Lee said. 

The previous record for solving ground state energy relied on 12 qubits and a method known as the variational quantum eigensolver (VQE). The problem with VQE is that it didn't’ take into account the effects of interacting electrons, which is crucial for calculating ground state energy. According to Lee, virtual correlation techniques from classic computers could be added to help chemists deal with even larger molecules. 

The new hybrid classical-quantum calculations demonstrated an accuracy on par with some of the best classical methods, suggesting that complex problems could be solved more accurately and quickly with a quantum computer. 

“The feasibility of solving larger and more challenging chemical problems will only increase with time,” Lee said. “This gives us hope that quantum technologies that are being developed will be practically useful.”

Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.