Skip to content

Sound, Cavitation and the Quantum Question: A Critical Review of Histotripsy Mechanics and the Limits of Quantum Analogy in Biological Acoustics

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research) · 2 citations · 3 references
Ultrasound and Hyperthermia Applications

Abstract

Histotripsy is a non-invasive, non-thermal and non-ionising focused-ultrasound technique that destroys soft tissue mechanically by generating dense, controlled clouds of cavitation bubbles. Developed at the University of Michigan from the early 2000s, it received United States Food and Drug Administration De Novo authorisation in October 2023 for the destruction of liver tumours, following the #HOPE4LIVER trial. This article began as a four-part question-and-answer exchange with a conversational artificial-intelligence system on four themes: the mechanics of histotripsy; quantum computing, resonance, entanglement and supersymmetry; possible bridges between sound and quantum effects; and the difference between "classical" and "quantum" histotripsy. Here that exchange is reviewed critically and expanded into a structured paper. Each claim is sorted into one of three tiers: established physics and medicine, laboratory-established physics that is not clinically relevant, and speculation. The well-supported core is intrinsic-threshold and shock-scattering cavitation, boiling histotripsy, threshold statistics and the liver indication. Quantum acoustics, in which superconducting qubits control single phonons in engineered resonators at millikelvin temperatures, is real laboratory physics, but it works in a regime separated from clinical ultrasound by many orders of magnitude in thermal phonon occupation. A calculation from first principles shows a mean thermal occupation of about 6.5 million phonons per mode at 1 MHz and body temperature, compared with about 3 × 10⁻¹³ for a 6 GHz resonator at 10 mK. "Quantum histotripsy" has no physical basis at present, and no link between supersymmetry and biological acoustics is supported by evidence. The paper closes with a research agenda in which every item can be falsified, and with a statement of limitations. Nothing in it is medical advice.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Oct 20, 2026

MPS in PyTorch 2.15

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.