• New method of studying large numbers of particles at quantum level developed by Universities of Warwick and Oxford
• Electrons and ions behave on vastly different timescales, making it prohibitive to simulate both on the same footing
• Ultrafast quantum simulation overcomes this limitation and allows for the study of the dynamics of the interactions between electron and ion
• The new approach offers insights into the behaviour of matter inside giant planets and in the highly compressed core during laser-driven nuclear fusion

Billions of tiny interactions occur between thousands of particles in every piece of matter in the blink of an eye. Simulating these interactions in their full dynamics was said to be elusive but has now been made possible by the new work of researchers from Oxford and Warwick.

In doing so, they have paved the way for new insights into the complex mutual interactions between the particles in extreme environments such as at the heart of large planets or laser nuclear fusion.

Researchers at the University of Warwick and the University of Oxford have developed a new way to simulate quantum systems of many particles, that allows for the investigation of the dynamic properties of quantum systems fully coupled to slowly moving ions.

Effectively, they have made the simulation of the quantum electrons so fast that it could run extremely long without restrictions and the effect of their motion on the movement of the slow ions would be visible.

Reported in the journal Science Advances, it is based on a long-known alternative formulation of quantum mechanics (Bohm dynamics) which the scientists have now empowered to allow the study of the dynamics of large quantum systems.

Many quantum phenomena have been studied for single or just a few interacting particles as large complex quantum systems overpower scientists’ theoretical and computational capabilities to make predictions. This is complicated by the vast difference in timescale the different particle species act on ions evolve thousands of times more slowly than electrons due to their larger mass. To overcome this problem, most methods involve decoupling electrons and ions and ignoring the dynamics of their interactions - but this severely limits our knowledge of quantum dynamics.

To develop a method that allows scientists to account for the full electron-ion interactions, the researchers revived an old alternative formulation of quantum mechanics developed by David Bohm. In quantum mechanics, one needs to know the wave function of a particle. It turns out that describing it by the mean trajectory and a phase, as done by Bohm, is very advantageous. However, it took an additional suit of approximations and many tests to speed up the calculations as dramatic as required. Indeed, the new methods demonstrated an increase of speed by more than a factor of 10,000 (four orders of magnitude) yet are still consistent with previous calculations for static properties of quantum systems.

The new approach was then applied to a simulation of warm dense matter, a state between solids and hot plasmas, that is known for its inherent coupling of all particle types and the need for a quantum description. In such systems, both the electrons and the ions can have excitations in the form of waves and both waves will influence each other. Here, the new approach can show its strength and determined the influence of the quantum electrons on the waves of the classical ions while the static properties were proven to agree with previous data.

Many-body quantum systems are the core of many scientific problems ranging from the complex biochemistry in our bodies to the behavior of matter inside of large planets or even technological challenges like high-temperature superconductivity or fusion energy which demonstrates the possible range of applications of the new approach.

Prof Gianluca Gregori (Oxford), who led the investigation, said: “Bohm quantum mechanics has often been treated with skepticism and controversy. In its original formulation, however, this is just a different reformulation of quantum mechanics. The advantage in employing this formalism is that different approximations become simpler to implement and this can increase the speed and accuracy of simulations involving many-body systems.”

Dr. Dirk Gericke from the University of Warwick, who assisted the design of the new supercomputer code, said: “With this huge increase of numerical efficiency, it is now possible to follow the full dynamics of fully interacting electron-ion systems. This new approach thus opens new classes of problems for efficient solutions, in particular, where either the system is evolving or where the quantum dynamics of the electrons has a significant effect on the heavier ions or the entire system.

“This new numerical tool will be a great asset when designing and interpreting experiments on warm dense matter. From its results, and especially when combined with designated experiments, we can learn much about the matter in large planets and for laser fusion research. However, I believe its true strength lies in its universality and possible applications in quantum chemistry or strongly driven solids.”

Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy.

And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both insides--and outside--the tumor, a signature of the lab's recent research.

"This is no flash in the pan--this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. {module INSIDE STORY}

Currently, only about 20% of all cancer patients will benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

Madabhushi said the recent work by his lab would help oncologists know which patients would benefit from the therapy, and who would not.

"Even though immunotherapy has changed the entire ecosystem of cancer, it also remains extremely expensive--about $200,000 per patient, per year," Madabhushi said. "That's part of the financial toxicity that comes along with cancer and results in about 42% of all newly diagnosed cancer patients losing their life savings within a year of diagnosis."

Having a tool based on the research being done now by his lab would go a long way toward "doing a better job of matching up which patients will respond to immunotherapy instead of throwing $800,000 down the drain," he added, referencing the four patients out of five who will not benefit, multiplied by annual estimated cost.

New research published

The new research, led by co-authors Mohammadhadi Khorrami and Prateek Prasanna, along with Madabhushi and 10 other collaborators from six different institutions was published this month in the journal Cancer Immunology Research.

Khorrami, a graduate student working at the CCIPD, said one of the more significant advances in the research was the ability of the computer program to note the changes in texture, volume, and shape of a given lesion, not just its size.

"This is important because when a doctor decides based on CT images alone whether a patient has responded to therapy, it is often based on the size of the lesion," Khorrami said. "We have found that textural change is a better predictor of whether the therapy is working.

"Sometimes, for example, the nodule may appear larger after therapy because of another reason, say a broken vessel inside the tumor--but the therapy is working. Now, we have a way of knowing that."

Prasanna, a postdoctoral research associate in Madabhushi's lab, said the study also showed that the results were consistent across scans of patients treated at two different sites and with three different types of immunotherapy agents.

"This is a demonstration of the fundamental value of the program, that our machine-learning model could predict response in patients treated with different immune checkpoint inhibitors," he said. "We are dealing with a fundamental biological principle."

Prasanna said the initial study used CT scans from 50 patients to train the computer and create a mathematical algorithm to identify the changes in the lesion. He said the next step will be to test the program on cases obtained from other sites and across different immunotherapy agents. This research recently won an ASCO 2019 Conquer Cancer Foundation Merit Award.

Additionally, Madabhushi said, researchers were able to show that the patterns on the CT scans which were most associated with a positive response to treatment and with overall patient survival were also later found to be closely associated with the arrangement of immune cells on the original diagnostic biopsies of those patients.

This suggests that those CT scans appear to capture the immune response elicited by the tumors against the invasion of cancer--and that the ones with the strongest immune response were showing the most significant textural change and most importantly, would best respond to the immunotherapy, he said.

Madabhushi established the CCIPD at Case Western Reserve in 2012. The lab now includes nearly 60 researchers.

Some of the lab's most recent work, in collaboration with New York University and Yale University, has used AI to predict which lung cancer patients would benefit from adjuvant chemotherapy based on tissue-slide images. That advancement was named by Prevention Magazine as one of the top 10 medical breakthroughs of 2018.