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We investigate the effect of coupling between translational and internal degrees of freedom of composite quantum particles on their localization in a random potential. We show that entanglement between the two degrees of freedom weakens localization due to the upper bound imposed on the inverse participation ratio by purity of a quantum state. We perform numerical calculations for a two-particle system bound by a harmonic force in a 1D disordered lattice and a rigid rotor in a 2D disordered lattice. We illustrate that the coupling has a dramatic effect on localization properties, even with a small number of internal states participating in quantum dynamics.

arXiv:2011.06279

CAP Virtual Congress 2021

Localization of composite quantum particles in a random potential

quantum dynamics

ultracold molecules

anderson localization

decoherence

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technical paper

A crucial challenge in engineering modern, integrated systems is to produce robust designs. However, quantifying the robustness of a design is less straightforward than quantifying the robustness of products. For products, in particular engineering materials, intuitive, plain language terms of strong versus weak and brittle versus ductile take on precise, quantitative meaning in terms of stress–strain relationships. Here, we show that a “systems physics” framing of integrated system design produces stress–strain relationships in design space. From these stress–strain relationships, we find that both the mathematical and intuitive notions of strong versus weak and brittle versus directly characterize the robustness of designs. We use this to show that the relative robustness of designs against changes in problem objectives has a simple graphical representation. This graphical representation, and its underlying stress–strain foundation, provide new metrics that can be applied to classes of designs to assess robustness from feature- to system-level.

Robust Design from Systems Physics

Crowded soft-matter and biological systems organize locally into preferred motifs. Locally-organized motifs in soft systems can, paradoxically, arise from a drive to maximize overall system entropy. Entropy-driven local order has been directly confirmed in model, synthetic colloidal systems, however similar patterns of organization occur in crowded biological systems ranging from the contents of a cell to collections of cells. In biological settings, and in soft matter more broadly, it is unclear whether entropy generically promotes or inhibits local organization. Resolving this is difficult because entropic effects are intrinsically collective, complicating efforts to isolate them. Here, we employ minimal models that artificially restrict system entropy to show that entropy drives systems toward local organization, even when the model system entropy is below reasonable physical bounds. By establishing this bound, our results suggest that entropy generically promotes local organization in crowded soft and biological systems of rigid objects.

When does entropy promote local organization?

Accelerator Mass Spectrometry (AMS) provides high sensitivity measurements (typically at or below 1 part in $10^{12}$) for rare, long-lived radioisotopes when isobars (other elements with the same atomic weight as the isotope of interest) can be eliminated. In AMS laboratories, established techniques are used for the removal of the interfering isobars of some light isotopes. However, for smaller, lower-energy AMS systems separating the abundant isobars of many isotopes, such as the sulfur-36 in measurements of chlorine-36, remains a challenge. For some heavy isotopes, such as strontium-90 and cesium-135,137, even high energy accelerators are unable to separate the interfering isobars. 

The Isobar Separator for Anions (ISA), which has been integrated into a second injection line of the 3 MeV tandem accelerator system at the A. E. Lalonde AMS Laboratory, will provide a universal way to measure rare radioisotopes without the interference of abundant isobars. The ISA is a radiofrequency quadrupole (RFQ) reaction cell system, including a DC deceleration region, a combined cooling and reaction cell, and a DC acceleration region. The deceleration region accepts a mass analyzed beam from the ion source (with energy 20-35 keV) and reduces the energy to a level that the reaction cell can accept. RFQ segments along the length of the cell create a potential well which limits the divergence of the traversing ions. DC rod offset voltages on these RFQ segments maintain a controlled ion velocity through the cell. The cell is filled with an inert cooling gas that has been experimentally selected to provide the lowest ion energy and the highest transmission, and with nitrogen dioxide, a reaction gas chosen to preferentially react with the interfering isobar. In the case of chlorine-36, the sulfur-36 isobar has been shown to be reduced by over $10^{6}$. Preliminary characterization of the ISA and its incoming and outgoing ion beams will be presented.

Characterization of the Isobar Separator for Anions for Accelerator Mass Spectrometry measurements

The Feynman quantum-classical isomorphism between classical statistical mechanics in 3+1 dimensions and quantum statistical mechanics in 3 dimensions is used to relate classical polymer self-consistent field theory to quantum density functional theory. This allows the theorems of density functional theory, which connect single particle density descriptions of quantum systems to wave function descriptions, to relate non-relativistic quantum mechanics back to a classical statistical mechanical derivation of polymer self-consistent field theory for ring polymers. In turn, this allows for a quantum-classical isomorphic interpretation of quantum foundations which may require fewer postulates than standard approaches to quantum mechanics, while preserving all quantum predictions.

A Quantum-Classical Isomorphic Interpretation of Quantum Foundations Based on Density Functional Theory and Polymer Self-Consistent Field Theory

Objective: Dose distribution index (DDI) is a dose-volume parameter used in the treatment planning evaluation. DDI provides the dosimetric estimates on the target coverage, sparings of all organs-at-risk and remaining healthy tissue in the treated organ in a single parameter. In this study, the DDI value was predicted by machine learning model using different algorithms. 

Methods: The DDI were calculated using its original formula by definition. On the other hand, machine training was carried out to determine the DDI using the same data of 50 prostate volumetric modulated arc therapy (VMAT) plans from the Grand River Regional Cancer Centre, Kitchener, Ontario. Machine learning algorithms such as linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR) were used to predict the DDI value for each prostate VMAT treatment plan. For comparing the performance of the machine learning algorithms, root mean square error (RMSE), prediction time of the machine learning and training time were determined and compared.

Results: Comparing the RMSE values among all algorithms, only the DDI predicted by the medium and coarse tree regression algorithms showed a relatively large RMSE values in the range of 0.021 – 0.034. For other algorithms such as SVM and GRP, they all performed very well in predicting the DDI with smaller RMSE values ranging from 0.0038 to 0.0193. By considering other factors such as prediction speed and training time, the square exponential GPR algorithm had the smallest RMSE value of 0.0038, a relatively high prediction speed of 4,100 observation per second and a short machine training time of 0.18 second.

Conclusion: It is concluded that the family of GPR algorithms performed best in the dose distribution index prediction. It is expected that the accuracy of DDI prediction will increase with more plan data trained using such algorithm.

Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

Objective: We built a RT Bot, a chatbot with characterization for the patient, general public and radiation staff to provide educational information regarding radiotherapy using the artificial intelligence. The Bot was personalized by machine learning to detect the user’s temperament and intent in order to provide the best guidance to the user with a human-like response. 

Methods: The Bot was developed using the IBM Watson Assistant functionalities on the IBM cloud. Dataset of information was prepared for different user groups such as descriptions of all processes in radiotherapy, promotion of cancer screening especially high fatality and popular cancer sites, and basic cancer preventive measures such as how to maintain healthy life with suitable diet and exercise. To ensure correct information can be understood and digested by the users with their background (patient, general public and radiation staff), the Bot character was personalized through the IBM Watson Assistant functionalities such as natural language understanding, entities and slots.

Results: The Bot can be operated in a front-end window on any Internet-of-things such as smartphone, tablet, laptop and desktop. In the beginning, the Bot will communicate with the user intentionally with an introduction. The user can then type in any text to answer concerning their enquiry. The Bot usually begins by answering simple questions regarding radiotherapy and providing related information. If the Bot cannot understand the user’s wording, it will provide a guidance to help the user. 

Conclusion: A chatbot was built for interdisciplinary educational purpose for the patient, general public and radiation staff using artificial intelligence and machine learning. The Bot may be used by a cancer centres or some private sectors such as high school, community centres, volunteer group and charities, which promote cancer preventive measures and screening for a healthy life or educate user what is cancer and radiotherapy.

An AI-assisted Chatbot for Education in Radiotherapy

Negative Ion Source Development for Accelerator Mass Spectrometry
CJ Tiessen, WE Kieser, and XL Zhao
Accelerator mass spectrometry (AMS) is a highly sensitive technique used for the analysis of long-lived radioisotopes. While carbon-14 dating is the most well known application, AMS can be used to measure other isotopes such as beryllium-10, aluminum-26, iodine-129, and uranium-236 which are useful in geology, archeology, environmental tracer and chronology studies, nuclear waste monitoring, and nuclear forensics. The technique uses a combination of electrostatic analyzers, mass-separation magnets, electrostatic lenses, as well as a tandem accelerator. In the accelerator, an electron stripping gas canal is used to convert incoming negative ions to positive ions while simultaneously disintegrating molecular isobars. Ions from the samples are injected into the accelerator using a cesium sputter negative-ion source. The focus of this work is to model the electrodynamics within the ion source, including the effects of the more intense positive cesium ion beam and the sputtered sample negative-ion beam. Simulations using Integrated Engineering Software’s Lorentz 2E ion optics software will guide the design of a new ion source with the goal of increasing the emitted sample ion current while also improving the emittance of this beam. Following a short overview of the AMS system, details of the ion source, including the mutual space-charge interaction of the two beams, will be presented.

Negative Ion Source Development for Accelerator Mass Spectrometry

Cooling atomic gases to ultracold temperatures revolutionized the field of atomic physics, connecting with and impacting many other areas in physics. Recent advances in producing ultracold molecules suggest similarly dramatic discoveries are on the horizon. I will review the physics of ultracold molecules, including our work bringing a new class of molecules to ultracold temperatures. Chemistry at these temperatures has a very different character than at room temperature. I will describe two striking effects: Spin-dependent reactivity and molecular Feshbach resonances.

Ultracold chemistry with triplet molecules

Over the past decade, significant progress has been made in the commercialization of quantum sensors based on ultra-cold atoms and matter-wave interferometry. Nowadays, the first absolute quantum gravimeters have reached the market and there is even a cold-atom machine on the International Space Station. Matter-wave interferometers utilize the wave nature of atoms and their interaction with laser light to create interference between different quantum-mechanical states. Compared to an optical interferometer, the roles of matter and light are reversed: light is used as the "optic" to split, reflect, and recombine matter-waves. The resulting interference contains precise information about the atom's motion, such as its acceleration, as well as the electro-magnetic fields that permeate its environment. These atom interferometers can be designed as extremely sensitive instruments and have already led to breakthroughs in time-keeping, gravimetry, and tests of fundamental physics. In this talk, I will give an overview of laser-cooling and matter-wave interferometry and its applications as a versatile tool, for example, to test Einstein's Equivalence Principle, map the Earth's gravitational field, or aid future navigation systems.

Quantum Sensing with Matter-Wave Interferometers

One of the most famous tidbits of received wisdom about quantum mechanics is that “you can’t ask” which path a photon took in an interferometer once it reaches the screen, or in general, that only questions about the specific things you finally measure are well-posed at all. Much work over the past decades has aimed to chip away at this blanket renunciation, and investigate “quantum retrodiction.” Particularly in light of modern experiments in which we can trap and control individual quantum systems for an extended time, and quantum information protocols which rely on “postselection,” these become more and more timely issues.

All the same, the principal experiment I wish to tell you about addresses a century-old controversy: that of the tunneling time. Since the 1930s, and more heatedly since the 1980s, the question of how long a particle spends in a classically forbidden region on those occasions when quantum uncertainty permits it to appear on the far side has been a subject of debate. Using Bose-condensed Rubidium atoms cooled down below a billionth of a degree above absolute zero, we have now measured just how long they spend inside an optical beam which acts as a “tunnel barrier” for them. I will describe these ongoing experiments, as well as proposals we are now refining to study exactly how long it would take to “collapse” an atom to be in the barrier.

I will also say a few words about a more recent experiment, which looks back at the common picture that when light slows down in glass, or a cloud of atoms, it is because the photons “get virtually absorbed” before being sent back along their way. It turns out that although it is possible to measure “the average time a photon spends as an atomic excitation,” there seems to be no prior work which directly addresses this, especially in the resonant situation. We carry out an experiment that lets us distinguish between the time spent by transmitted photons and by photons which are eventually absorbed, asking the question “how much time are atoms caused to spend in the excited state by photons which are not absorbed?”

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Localization of composite quantum particles in a random potential

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