18 research outputs found
Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor
Classifying many-body quantum states with distinct properties and phases of
matter is one of the most fundamental tasks in quantum many-body physics.
However, due to the exponential complexity that emerges from the enormous
numbers of interacting particles, classifying large-scale quantum states has
been extremely challenging for classical approaches. Here, we propose a new
approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting
quantum processor, we show that our scheme can efficiently classify two
different types of many-body phenomena: namely the ergodic and localized phases
of matter. Our quantum neuronal sensing process allows us to extract the
necessary information coming from the statistical characteristics of the
eigenspectrum to distinguish these phases of matter by measuring only one
qubit. Our work demonstrates the feasibility and scalability of quantum
neuronal sensing for near-term quantum processors and opens new avenues for
exploring quantum many-body phenomena in larger-scale systems.Comment: 7 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1
table in supplementary material
Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz
Simulation of quantum chemistry is one of the most promising applications of
quantum computing. While recent experimental works have demonstrated the
potential of solving electronic structures with variational quantum eigensolver
(VQE), the implementations are either restricted to nonscalable (hardware
efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few
qubits with large errors for the more accurate unitary coupled cluster (UCC)
ansatz. Here, integrating experimental and theoretical advancements of improved
operations and dedicated algorithm optimisations, we demonstrate an
implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining
error mitigation, we produce high-precision results of the ground-state energy
with error suppression by around two orders of magnitude. For the first time,
we achieve chemical accuracy for H_2 at all bond distances and LiH at small
bond distances in the experiment. Our work demonstrates a feasible path towards
a scalable solution to electronic structure calculation, validating the key
technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary
materials with 16 figure
Impact of a narrow coastal Bay of Bengal sea surface temperature front on an Indian summer monsoon simulation
A dry bias in climatological Central Indian rainfall plagues Indian summer monsoon (ISM) simulations in multiple generations of climate models. Here, using observations and regional climate modeling, we focus on a warm coastal Bay of Bengal sea surface temperature (SST) front and its impact on Central Indian rainfall. The SST front, featuring sharp gradients as large as 0.5 °C/100 km, is colocated with a mixed layer depth (MLD) front, in a region where salinity variations are known to control MLD. Regional climate simulations coupling a regional atmospheric model with an ocean mixed layer model are performed. A simulation with observed MLD climatology reproduces SST, rainfall, and atmospheric circulation associated with ISM reasonably well; it also eliminates the dry bias over Central India significantly. Perturbing MLD structure in the simulations, we isolate the SST front’s impact on the simulated ISM climate state. This experiment offers insights into ISM climatological biases in the coupled NCEP Climate Forecast System version-2. We suggest that the warm SST front is essential to Central Indian rainfall as it helps to sustain deep and intense convection in its vicinity, which may be a source for the vortex cores seeding the monsoon low-pressure systems.Published versio