Quantum computers // it’s not about hpc, it’s something else
Quantum computer is a mind-blowing paradigm, as a whole quantum physics, but in practice it’s infeasible to make useful quantum computers.
Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.
Classical computers can outperform quantum computers for certain calculations, challenging the notion that quantum always supersedes classical.
Quantum computers use qubits but face issues like information loss and translation difficulties.
Researchers devised a classical algorithm that selectively kept some quantum info, enabling faster and more accurate calculations than leading quantum computers.
They optimized a tensor network depicting qubit interactions, likening their algorithm’s effect to JPEG image compression.
The study highlights paths to enhance computations and underscores the difficulties of attaining quantum advantage with error-prone quantum computers.
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.
