Usually, NMR spectroscopy is used to determine small molecules in biological samples such as blood and urine. It is a powerful diagnostic tool for medical professionals to help them identify biomarkers of specific diseases and disorders.
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But, this technique has its limitations, especially when scientists need to distinguish particles that haven’t been cataloged already — that is, by far most of them.
To make this complicated and time-consuming process a lot simpler, a trio of doctors and scientists from Brigham and Women’s Hospital and Harvard Medical School created a new algorithm for decoding signals from NMR readings that draws from both quantum computing and classical machine learning.
Scientists figured that since the basics of NMR, short for Nuclear Magnetic Resonance , is grounded in quantum mechanics, then maybe a quantum computer could help push the technique beyond the current limits set by utilizing ordinary computer processors to decipher the data.
The algorithm reduces the process that takes several days by classical computers into several minutes. As it uses only 50 to 100 quantum bits or qubits, the algorithm works on both quantum computers that already exist and the so-called “near-term” quantum computers now being developed.
Eugene Demler, professor of physics and one of the paper’s co-authors, said, “We should not just think of applications for perfect quantum computers. We should think of applications of quantum computers for the near future. It’s important to realize that we can use these non-perfect computers — these noisy, intermediate-scale quantum computers — to study already what’s important for biomedical research.”
Currently, the algorithm is in the proof-of-concept stage, but it has paved the way towards possibilities in chemical, medical, and biological research using NMR.
Paper co-author Samia Mora, an associate professor of medicine at the Medical School and a cardiovascular medicine specialist at the Brigham, said, “Take blood, for example. We know there are thousands of molecules in the bloodstream, but right now, with NMR, we probably only measure about 200 [of them]. In the future, ideally, we would be able to expand this algorithm to be able to solve for this problem of what are these molecules in the bloodstream beyond the ones that we already know.”
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“Having a better understanding of the molecular signatures of diseases or treatments is very impactful for many areas across many, many different disciplines.”
Scientists also wanted to crack at a problem that has real-world applications, is hard for classical computers yet could be solved using existing and near-term quantum computers. Quantum-assisted NMR spectroscopy checked all the boxes since the readings, called a spectrogram, are put together by measuring a complex set of quantum spins.
For example, to get a spectrogram, biological samples are placed inside a machine that has a magnetic field and is then bombarded with radio waves to excite the nuclear magnetic properties in the molecules. The NMR machine reads those spins as different signatures.[…]
read more – copyright by www.techexplorist.com
Usually, NMR spectroscopy is used to determine small molecules in biological samples such as blood and urine. It is a powerful diagnostic tool for medical professionals to help them identify biomarkers of specific diseases and disorders.
copyright by www.techexplorist.com
To make this complicated and time-consuming process a lot simpler, a trio of doctors and scientists from Brigham and Women’s Hospital and Harvard Medical School created a new algorithm for decoding signals from NMR readings that draws from both quantum computing and classical machine learning.
Scientists figured that since the basics of NMR, short for Nuclear Magnetic Resonance , is grounded in quantum mechanics, then maybe a quantum computer could help push the technique beyond the current limits set by utilizing ordinary computer processors to decipher the data.
The algorithm reduces the process that takes several days by classical computers into several minutes. As it uses only 50 to 100 quantum bits or qubits, the algorithm works on both quantum computers that already exist and the so-called “near-term” quantum computers now being developed.
Eugene Demler, professor of physics and one of the paper’s co-authors, said, “We should not just think of applications for perfect quantum computers. We should think of applications of quantum computers for the near future. It’s important to realize that we can use these non-perfect computers — these noisy, intermediate-scale quantum computers — to study already what’s important for biomedical research.”
Currently, the algorithm is in the proof-of-concept stage, but it has paved the way towards possibilities in chemical, medical, and biological research using NMR.
Paper co-author Samia Mora, an associate professor of medicine at the Medical School and a cardiovascular medicine specialist at the Brigham, said, “Take blood, for example. We know there are thousands of molecules in the bloodstream, but right now, with NMR, we probably only measure about 200 [of them]. In the future, ideally, we would be able to expand this algorithm to be able to solve for this problem of what are these molecules in the bloodstream beyond the ones that we already know.”
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
“Having a better understanding of the molecular signatures of diseases or treatments is very impactful for many areas across many, many different disciplines.”
Scientists also wanted to crack at a problem that has real-world applications, is hard for classical computers yet could be solved using existing and near-term quantum computers. Quantum-assisted NMR spectroscopy checked all the boxes since the readings, called a spectrogram, are put together by measuring a complex set of quantum spins.
For example, to get a spectrogram, biological samples are placed inside a machine that has a magnetic field and is then bombarded with radio waves to excite the nuclear magnetic properties in the molecules. The NMR machine reads those spins as different signatures.[…]
read more – copyright by www.techexplorist.com
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