Home AI News Unlocking the Potential: Advancements in Quantum Computing and Computational Applications

Unlocking the Potential: Advancements in Quantum Computing and Computational Applications

Unlocking the Potential: Advancements in Quantum Computing and Computational Applications

A full-scale quantum computer that can correct errors has the potential to solve problems that classical computers cannot. However, building such a computer is a massive undertaking and is still several years away. In the meantime, we are using our current noisy quantum processors for quantum experiments.

Experiments on noisy quantum processors are currently limited to a few thousand quantum operations before the noise degrades the quantum state. In 2019, we conducted an experiment called random circuit sampling and showed that our quantum processor outperformed classical supercomputers. Although we haven’t achieved beyond-classical capabilities yet, we have made other experimental discoveries, such as observing time crystals and Majorana edge modes.

Even in this noisy regime, we believe there are computational applications for quantum processors that can be performed faster than classical supercomputers. We are working on finding the best way to compare quantum experiments on our processors to classical applications. We have developed a framework for measuring the computational cost of a quantum experiment, which we call the “effective quantum volume.” We have used this framework to evaluate the computational cost of three recent experiments.

One experiment we are particularly excited about is the measurement of “out of time order correlators” (OTOCs). OTOCs provide a way to experimentally measure the effective quantum volume of a circuit, which is difficult for classical computers to do. OTOCs are also important in other scientific fields.

When running a quantum circuit on a noisy quantum processor, we have to consider two competing factors. On one hand, we want to achieve something that is difficult to accomplish classically, which increases the computational cost. On the other hand, each quantum gate introduces errors, so we prefer simpler circuits with a smaller effective volume, but these can be easily simulated by classical computers. Finding the balance between these considerations is called the “computational resource.”

One example of a quantum experiment we have conducted is random circuit sampling, which outperformed classical computers but is not particularly useful. We have also conducted experiments related to OTOCs and Floquet evolution, which have the potential to make significant contributions to quantum many-body physics.

In conclusion, we have made progress in our quantum experiments using noisy quantum processors. We are developing a framework to measure the computational cost of these experiments and have conducted several experiments to evaluate their effectiveness. We are particularly excited about the potential of OTOCs and Floquet evolution experiments in pushing the boundaries of quantum computing.

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