Quantum machine learning for molecular spectra, run on an ion trap.
In-development
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Updated Nov 2, 2026
Quantum simulation and modeling covers using quantum computation to predict what a molecule will do. On Halo, the solutions cover quantum machine learning for spectra, and algorithms for wavefunctions and spectra. Every solution comes from the team that developed it, so you can reach the people behind the work directly. Sign up to search the full network and post your specific need.
Quantum machine learning for spectra
Alex Jones
The same question on a superconducting architecture.
Early-stage
LM
University of Toronto
Quantum-enhanced learning aimed at molecular spectra prediction.
Early-stage
PN
Qunova Computing Inc. · Startup
Quantum graph neural networks, using the molecule's own structure.
Early-stage
JL
QC Ware Corp · Startup
Equivariant density networks, built to respect molecular symmetry.
Early-stage
WC
Multiverse Computing S.L · Supplier
Time-dependent quantum machine learning for spectral properties.
Early-stage
XL
University of Washington
A quantum convolutional network predicting a molecular spectrum.
Early-stage
Algorithms for wavefunctions and spectra
IK
University of Sydney
An explicit quantum algorithm for molecular spectroscopy simulation.
Early-stage
Dennis Wayo
Nazarbayev University
A quantum algorithm for vibrational and electronic spectra together.
Early-stage
VN
R.V. College of Engineering · University
An algorithm targeted at isomeric molecules, where classical methods struggle.
Early-stage
Daniel Sierra-Sosa
Catholic University of America
Wavefunction estimation from pre-optimized templates.
Early-stage
BM
Silicon Quantum Computing Pty Ltd · Startup
Molecular interaction predicted on precision atomic-scale quantum processors.
In-development
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Who is working on quantum simulation & modeling
Organizations in quantum simulation & modeling
Universities, startups, and suppliers with solutions in quantum simulation and modeling.
Quantum simulation and modeling organizationsAbout quantum simulation & modeling
Every entry on Halo addresses the same target: molecular spectral properties. Eight use quantum machine learning, on graph networks, convolutional networks or time-dependent models, and six write an algorithm for the wavefunction or the spectrum directly. They arrived together, from fourteen institutions. On Halo it spans two groups: quantum machine learning for spectra, and algorithms for wavefunctions and spectra.
Stage of development. Work on quantum simulation & modeling on Halo comes mostly from university programs. 76% are past proof of concept (TRL 4 or higher). Fee-for-service work and co-development are the usual partnering routes, and about 33% of the solutions that state terms offer sponsored research.
Three trends in quantum simulation and modeling on Halo
One target, fourteen approaches. Every entry predicts molecular spectral properties, and the disagreement is entirely about method: graph neural networks that use molecular structure, equivariant networks that respect its symmetry, convolutional networks, time-dependent models, classical shadow tomography, and template-based wavefunction estimation. The chemistry problem is agreed and the computer science is not.
Symmetry is the design choice that separates them. The entries that distinguish themselves technically do it through structure. Graph networks encode the molecule as a graph, equivariant density networks build in rotational symmetry, and template methods start from a pre-optimized wavefunction. Each is an argument about how much chemistry to give the model before it starts learning.
Two architectures, the same question. One pair of entries runs quantum machine learning for molecular spectra on an ion trap, then puts the same question on a superconducting architecture. Comparing hardware on a fixed problem is unusual in this set, where most entries vary the algorithm and leave the machine unstated.
83organizations
87solutions listed
64%from university labs
76%past proof of concept
Frequently asked questions
What is quantum simulation and modeling?
Quantum simulation and modeling covers using quantum computation to predict what a molecule will do. Almost all of it addresses molecular spectral properties, split between quantum machine learning approaches and explicit quantum algorithms for wavefunctions and spectroscopy. Chemical and pharmaceutical companies are the buyers. On Halo the work leans toward quantum machine learning for spectra, and algorithms for wavefunctions and spectra.
What are examples of quantum simulation and modeling?
Two examples of quantum simulation and modeling solutions on Halo include:
What are the latest quantum simulation and modeling innovations?
The most recently updated quantum simulation and modeling solutions on Halo include:
Which companies and suppliers are developing quantum simulation and modeling?
There are 83 organizations with quantum simulation and modeling solutions on Halo, 19 of them universities and research institutions. Among them are Duke University, Syracuse University, and University of Toronto. Most offer fee-for-service work or co-development. Sign up to see every organization working in the area and to send them your specific need.
How do I find quantum simulation and modeling research partners?
Browsing quantum simulation and modeling solutions on Halo is free. Sign up to search the full network and save the ones you want. Post your specific need to get exact matches. Organizations reply directly on the platform.
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