Quantum computers can still be beaten by traditional PCs with new method
Classical computers can sometimes outperform quantum computers thanks to new algorithms, challenging the idea that quantum always prevails.
Researchers from New York University (NYU) have discovered that classical computers can, in some circumstances, keep up with, or even surpass, quantum computers. They found that by adopting a new innovative algorithmic method, classical computers can get a much-needed boost in speed and accuracy, which could mean that they still have a future should quantum computers ever take off.
Many experts believe that quantum computing represents a paradigm shift from classical computing. This is primarily because classical computers, as you are aware, process information using digital bits (0s and 1s), while quantum computers use quantum bits (qubits) to store information in values between 0 and 1.
This ability, so the story goes, enables quantum computers to process and store information in qubits and allows quantum algorithms to outperform classical counterparts. Additionally, quantum computers store information in values between 0 and 1, making it difficult for classical computers to imitate quantum ones perfectly.
However, as it turns out, quantum computers are delicate and prone to information loss. Furthermore, even if information is preserved, converting it to classical information necessary for practical computation isn’t easy.
There is hope for classical computers yet
Classical computers, conversely, don’t suffer from the problems of information loss and translation that quantum computers do. Additionally, classical algorithms can be designed to take advantage of these challenges and simulate a quantum computer with far fewer resources than previously believed, as explained in a recent research paper published in PRX Quantum.
The study’s results indicate that classical computing can perform faster and more accurate calculations than state-of-the-art quantum computers. This breakthrough was achieved with an algorithm that keeps only part of the information stored in the quantum state—and just enough to compute the outcome accurately.
“This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches,” explains Dries Sels, an assistant professor in New York University’s Department of Physics and one of the paper’s authors. “Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer,” he added.
To this end, Sels and his colleagues focused on a tensor network, which is believed to represent the interactions between qubits accurately. These networks have been challenging to work with, but recent advancements in the field now allow these networks to be optimized using tools borrowed from statistical inference.
Tensor networks are an old PC’s best friend
But, the new method focuses only on the most important pieces of information and ignores the rest, like when you compress a photo to make it smaller without losing the quality that matters to you. This method lets regular computers do some cool stuff quantum computers can do without all the fuss.
The researchers compare their method to compressing a photo into a JPEG file. Just like compressing a photo reduces its file size without making it incomprehensible. To this end, their technique simplifies the quantum computing problem so that a regular computer can handle it more efficiently.
“Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image,” says the Flatiron Institute’s Joseph Tindall, who led the project. “We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further.”
You can view the study for yourself in the journal PRX Quantum.
Study abstract:
We report an accurate and efficient classical simulation of a kicked Ising quantum system on the heavy hexagon lattice. A simulation of this system was recently performed on a 127-qubit quantum processor using noise-mitigation techniques to enhance accuracy [Y. Kim et al., Nature, 618, 500–5 (2023)]. Here we show that, by adopting a tensor network approach that reflects the geometry of the lattice and is approximately contracted using belief propagation, we can perform a classical simulation that is significantly more accurate and precise than the results obtained from the quantum processor and many other classical methods.
We quantify the treelike correlations of the wave function in order to explain the accuracy of our belief propagation-based approach. We also show how our method allows us to perform simulations of the system to long times in the thermodynamic limit, corresponding to a quantum computer with an infinite number of qubits. Our tensor network approach has broader applications for simulating the dynamics of quantum systems with treelike correlations.
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