Quantum machine learning for molecular spectra, run on an ion trap.
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Updated Sept 23, 2026
Quantum computing systems and platforms covers quantum hardware, and the algorithms run on it. On Halo, the solutions cover quantum machine learning for molecular spectra, quantum algorithms for spectroscopy, privacy and secure computation, and cryogenic hardware. Sign up to search the full network and post your specific need.
Quantum machine learning for molecular spectra

The same question on a superconducting architecture.
National Taiwan University
Hybrid neural networks predicting infrared spectra specifically.
University of Toronto
Quantum-enhanced learning aimed at molecular spectra prediction.
University of Toronto
A hybrid quantum-classical route to the same spectroscopic properties.
Qunova Computing Inc.
Quantum graph neural networks, using the molecule's own structure.
Equivariant density networks, built to respect molecular symmetry.
Multiverse Computing S.L
Time-dependent quantum machine learning for spectral properties.
XL
University of Washington
A quantum convolutional network predicting a molecular spectrum.
A hybrid quantum-classical model for the same prediction.
Sesallab R&D and Consultancy Company
Quantum machine learning applied to spectral property prediction.
University of Calgary
Hybrid circuit learning for the spectral property problem.
McMaster University
Classical shadow tomography with AI assistance, for the same properties.
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Who is working on quantum computing systems & platforms
Organizations in quantum computing systems & platforms
Universities, startups, and suppliers with solutions in quantum computing systems & platforms.
Quantum computing organizationsInnovators in quantum computing systems & platforms
Researchers and inventors advancing quantum computing technologies.
Quantum computing expertsAbout quantum computing systems & platforms
Twenty of the entries on Halo answer a single question: can a quantum computer predict a molecular spectrum better than a classical one. Thirteen approach it through quantum machine learning and six through explicit quantum algorithms, from twenty institutions on five continents. On Halo it spans four groups, including quantum machine learning for molecular spectra, quantum algorithms for spectroscopy, and privacy and secure computation.
Stage of development. Work on quantum computing systems & platforms on Halo comes mostly from companies. 85% are past proof of concept (TRL 4 or higher). Licensing and co-development are the usual partnering routes, and about 50% of the solutions that state terms offer sponsored research.
Three trends in quantum computing technologies on Halo
Twenty answers to one question. Predicting molecular spectral properties on quantum hardware drew twenty entries from twenty different institutions, across the United States, Canada, Australia, Taiwan, Korea, Spain, Kazakhstan, India and Turkey. Thirteen use quantum machine learning and six write an explicit algorithm. No other request in the taxonomy produced a response this uniform.
Hybrid, with almost no exceptions. Nearly every entry is quantum-classical, and hardly any is purely quantum: hybrid circuit learning, hybrid neural networks, classical shadow tomography with AI assistance, quantum-enhanced classical models. Current hardware cannot carry the whole calculation, and every group has arrived at the same division of labor.
Two entries build hardware. Ferroelectric SQUID memory with heater cryotron selection, and voltage-controlled Boolean logic from the same devices, both for cryogenic operation. Against twenty algorithm entries, the hardware side amounts to two entries from one university. The algorithms are being written well ahead of the machines.
Frequently asked questions
What are quantum computing technologies?
Quantum computing systems and platforms covers quantum hardware and the algorithms run on it. Almost all of the work is one problem: predicting molecular spectral properties, approached through quantum machine learning, quantum algorithms for spectroscopy, privacy-preserving methods, and the cryogenic hardware underneath. Chemical and pharmaceutical companies, and quantum hardware vendors, are the buyers.
What are examples of quantum computing technologies?
Four examples of quantum computing technologies on Halo include:
- Quantum machine learning for molecular spectra, run on an ion trap. (Duke University)
- An explicit quantum algorithm for molecular spectroscopy simulation. (University of Sydney)
- Federated learning on molecular spectral data with quantum privacy. (Purdue University)
- A ferroelectric SQUID memory array with cryotron selection. (University of Tennessee, Knoxville)
What are the latest quantum computing innovations?
The most recently updated quantum computing technologies on Halo include:
Which companies and suppliers are developing quantum computing technologies?
There are 192 organizations with quantum computing technologies on Halo, and companies outnumber university labs. Among them are Duke University, Syracuse University, National Taiwan University, and University of Toronto. Most offer licensing 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 computing research partners?
Browsing quantum computing technologies 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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