Bowtie VarQTE is a hybrid classical-quantum variational time evolution method that exploits causal light-cones to reduce quantum resource use for state preparation while achieving fidelities comparable to approximate quantum compilation.
Universal Quantum Computer Simulation of 50 Qubits on Europe`s First Exascale Supercomputer Harnessing Its Heterogeneous CPU-GPU Architecture
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We have developed a new version of the high-performance J\"ulich universal quantum computer simulator (JUQCS-50) that leverages key features of the GH200 superchips as used in the JUPITER supercomputer, enabling simulations of a 50-qubit universal quantum computer for the first time. JUQCS-50 achieves this through three key innovations: (1) extending usable memory beyond GPU limits via high-bandwidth CPU-GPU interconnects and LPDDR5 memory; (2) adaptive data encoding to reduce memory footprint with acceptable trade-offs in precision and compute effort; and (3) an on-the-fly network traffic optimizer. These advances result in a 16.6-fold speedup over the previous 48-qubit record on the K computer
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Introduces a toolbox for studying energy gaps, eigenstate structure, and optimization dynamics in quantum annealing formulations of database problems.
Exascale classical simulation validates noise-tolerant performance of a 98-qubit QPU up to 48 qubits for LR-QAOA, with statistical analysis showing coherent regime up to 93 qubits before outputs become indistinguishable from random.
Sparse qubit connectivity raises compiled depth in noisy IQP circuits, requiring lower effective noise to remain outside the classically simulatable regime compared to fully connected layouts.
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
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Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.