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High-throughput viscometry via machine-learning from videos of inverted vials
Authors:
Ignacio Arretche,
Mohammad Tanver Hossain,
Ramdas Tiwari,
Abbie Kim,
Mya G. Mills,
Connor D. Armstrong,
Jacob J. Lessard,
Sameh H. Tawfick,
Randy H. Ewoldt
Abstract:
Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted vial test and enables quantitative visco…
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Although the inverted vial test has been widely used as a qualitative method for estimating fluid viscosity, quantitative rheological characterization has remained limited due to its complex, uncontrolled flow - driven by gravity, surface tension, inertia, and initial conditions. Here, we present a computer vision (CV) viscometer that automates the inverted vial test and enables quantitative viscosity inference across nearly five orders of magnitude (0.01-1000 Pas), without requiring direct velocity field measurements. The system simultaneously inverts multiple vials and records videos of the evolving fluid, which are fed into a neural network that approximates the inverse function from visual features and known fluid density. Despite the complex, multi-regime flow within the vial, our approach achieves relative errors below 25%, improving to 15% for viscosities above 0.1 Pas. When tested on non-Newtonian polymer solutions, the method reliably estimates zero-shear viscosity as long as viscoelastic or shear-thinning behaviors remain negligible within the flow regime. Moreover, high standard deviations in the inferred values may serve as a proxy for identifying fluids with strong non-Newtonian behavior. The CV viscometer requires only one camera and one motor, is contactless and low-cost, and can be easily integrated into high-throughput experimental automated and manual workflows. Transcending traditional characterization paradigms, our method leverages uncontrolled flows and visual features to achieve simplicity and scalability, enabling high-throughput viscosity inference that can meet the growing demand of data-driven material models while remaining accessible to lower resource environments.
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Submitted 30 May, 2025;
originally announced June 2025.
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Certified randomness using a trapped-ion quantum processor
Authors:
Minzhao Liu,
Ruslan Shaydulin,
Pradeep Niroula,
Matthew DeCross,
Shih-Han Hung,
Wen Yu Kon,
Enrique Cervero-Martín,
Kaushik Chakraborty,
Omar Amer,
Scott Aaronson,
Atithi Acharya,
Yuri Alexeev,
K. Jordan Berg,
Shouvanik Chakrabarti,
Florian J. Curchod,
Joan M. Dreiling,
Neal Erickson,
Cameron Foltz,
Michael Foss-Feig,
David Hayes,
Travis S. Humble,
Niraj Kumar,
Jeffrey Larson,
Danylo Lykov,
Michael Mills
, et al. (7 additional authors not shown)
Abstract:
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is fundamentally impossi…
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While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a challenge. One such task is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is fundamentally impossible to achieve solely by classical computation. In this work, we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum "challenge" circuits using a small randomness seed, sends them to an untrusted quantum server to execute, and verifies the server's results. We analyze the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of $1.1\times10^{18}$ floating-point operations per second across multiple supercomputers, we certify $71,313$ bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of today's quantum computers.
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Submitted 26 March, 2025;
originally announced March 2025.
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From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries
Authors:
Hugh Van Deventer,
Mark Mills,
August Evrard
Abstract:
Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. The curricular environment is also dynamic, and poor communication and search fun…
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Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. The curricular environment is also dynamic, and poor communication and search functions on campus can limit a student's ability to discover new courses of interest. To support both students and their advisers in such a setting, we explore a novel Large Language Model (LLM) course recommendation system that applies a Retrieval Augmented Generation (RAG) method to the corpus of course descriptions. The system first generates an 'ideal' course description based on the user's query. This description is converted into a search vector using embeddings, which is then used to find actual courses with similar content by comparing embedding similarities. We describe the method and assess the quality and fairness of some example prompts. Steps to deploy a pilot system on campus are discussed.
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Submitted 30 December, 2024; v1 submitted 26 December, 2024;
originally announced December 2024.
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Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Authors:
Ruslan Shaydulin,
Changhao Li,
Shouvanik Chakrabarti,
Matthew DeCross,
Dylan Herman,
Niraj Kumar,
Jeffrey Larson,
Danylo Lykov,
Pierre Minssen,
Yue Sun,
Yuri Alexeev,
Joan M. Dreiling,
John P. Gaebler,
Thomas M. Gatterman,
Justin A. Gerber,
Kevin Gilmore,
Dan Gresh,
Nathan Hewitt,
Chandler V. Horst,
Shaohan Hu,
Jacob Johansen,
Mitchell Matheny,
Tanner Mengle,
Michael Mills,
Steven A. Moses
, et al. (4 additional authors not shown)
Abstract:
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for mo…
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The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.
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Submitted 2 June, 2024; v1 submitted 4 August, 2023;
originally announced August 2023.