The emergence of quantum computing is widely perceived as a paradigm change from traditional, or classical, computing. While quantum computers use quantum bits, or qubits, to store quantum information in values between 0 and 1, conventional computers process information in the form of digital bits (0s and 1s).
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Under specific circumstances, the capacity of qubits to process and store data can be leveraged to create quantum algorithms that significantly surpass the performance of classical algorithms. Notably, classical computers find it challenging to emulate quantum ones perfectly due to quantum's capacity to store information in values between 0 and 1.
Quantum computers, however, are erratic and prone to information loss. Furthermore, converting information loss into classical information - which is required to produce a useful computation—is challenging even in cases where it can be prevented.
Neither of those two issues affects classical computers. Furthermore, as recently reported in a research paper published in the journal PRX Quantum, well-designed classical algorithms can further take advantage of the twin challenges of information loss and translation to emulate a quantum computer with far fewer resources than previously believed.
The findings demonstrate that modern quantum computers cannot match the speed and accuracy of reconfigured classical computing.
This innovation was made possible by an algorithm that retains only the minimal amount of data in the quantum state - just enough to compute the result accurately.
This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches. Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer.
Dries Sels, Study Author and Assistant Professor, Department of Physics, New York University
Sels and colleagues at the Simons Foundation concentrated on a particular kind of tensor network that accurately captures the interactions between the qubits to find ways to optimize classical computing. Although those networks have a reputation for being difficult to manage, recent developments in the field have made it possible to optimize these networks using techniques taken from statistical inference.
The algorithm’s output is compared by the authors to the process of compressing an image into a JPEG file, which saves space by removing information from large images while hardly affecting the image's quality.
Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image. 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.
Joseph Tindall, Project Lead, Flatiron Institute
The work was supported by the Flatiron Institute and a grant from the Air Force Office of Scientific Research.
Journal Reference
Tindall, J., et.al. (2024) Efficient Tensor Network Simulation of IBM’s Eagle Kicked Ising Experiment. American Physical Society.doi.org/10.1103/PRXQuantum.5.010308
Source: https://www.nyu.edu/