Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms. Present-day photonic quantum computers have been limited either to non-deterministic operation, low photon numbers and rates, or fixed random gate sequences. Here we introduce a full-stack hardware-software system for executing many-photon quantum circuit operations using integrated nanophotonics: a programmable chip, operating at room temperature and interfaced with a fully automated control system. The system enables remote users to execute quantum algorithms that require up to eight modes of strongly squeezed vacuum initialized as two-mode squeezed states in single temporal modes, a fully general and programmable four-mode interferometer, and photon number-resolving readout on all outputs. Detection of multi-photon events with photon numbers and rates exceeding any previous programmable quantum optical demonstration is made possible by strong squeezing and high sampling rates. We verify the non-classicality of the device output, and use the platform to carry out proof-of-principle demonstrations of three quantum algorithms: Gaussian boson sampling, molecular vibronic spectra and graph similarity. These demonstrations validate the platform as a launchpad for scaling photonic technologies for quantum information processing.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11008968 | PMC |
| http://dx.doi.org/10.1038/s41586-021-03202-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.
Nat Biotechnol
January 2025
Insilico Medicine AI Limited, Abu Dhabi, UAE.
We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.
View Article and Find Full Text PDFA noise-robust post-processing pipeline for accelerated phase-cycled Na Multi-Quantum Coherences MRI.
Z Med Phys
January 2025
Aix-Marseille Univ, CNRS, CRMBM, Marseille, France; APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Purpose: To develop an improved post-processing pipeline for noise-robust accelerated phase-cycled Cartesian Single (SQ) and Triple Quantum (TQ) sodium (Na) Magnetic Resonance Imaging (MRI) of in vivo human brain at 7 T.
Theory And Methods: Our pipeline aims to tackle the challenges of Na Multi-Quantum Coherences (MQC) MRI including low Signal-to-Noise Ratio (SNR) and time-consuming Radiofrequency (RF) phase-cycling. Our method combines low-rank k-space denoising for SNR enhancement with Dynamic Mode Decomposition (DMD) to robustly separate SQ and TQ signal components.
Advancing efficiency in deep-blue OLEDs: Exploring a machine learning-driven multiresonance TADF molecular design.
Sci Adv
January 2025
Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, 744 Motooka, Nishi, Fukuoka 819-0395, Japan.
The pursuit of boron-based organic compounds with multiresonance (MR)-induced thermally activated delayed fluorescence (TADF) is propelled by their potential as narrowband blue emitters for wide-gamut displays. Although boron-doped polycyclic aromatic hydrocarbons in MR compounds share common structural features, their molecular design traditionally involves iterative approaches with repeated attempts until success. To address this, we implemented machine learning algorithms to establish quantitative structure-property relationship models, predicting key optoelectronic characteristics, such as full width at half maximum (FWHM) and main peak wavelength, for deep-blue MR candidates.
View Article and Find Full Text PDFTensor Train Optimization for Conformational Sampling of Organic Molecules.
J Chem Theory Comput
January 2025
Institute of Physical Chemistry, RWTH Aachen University, Aachen 52074, Germany.
Exploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach, called TTConf, to enable conformational sampling of large organic molecules where the combinatorial explosion of possible conformers prevents the use of a brute-force systematic conformer search. We employ tensor trains as a highly efficient dimensionality reduction algorithm, effectively reducing the scaling from exponential to polynomial.
View Article and Find Full Text PDFVariational Quantum Algorithm for Non-Markovian Quantum Dynamics Using an Ensemble of Ehrenfest Trajectories.
J Phys Chem Lett
January 2025
Department of Chemistry and Biochemistry, George Mason University, Fairfax, Virginia 22030, United States.
The simulation of non-Markovian quantum dynamics plays an important role in the understanding of charge and exciton dynamics in the condensed phase environment, yet such a simulation remains computationally expensive on classical computers. In this work, we develop a variational quantum algorithm that is capable of simulating non-Markovian quantum dynamics on quantum computers. The algorithm captures the non-Markovian effect by employing the Ehrenfest trajectories and Monte Carlo sampling of their thermal distribution.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!