{"id":117823,"date":"2025-09-03T22:40:09","date_gmt":"2025-09-03T22:40:09","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/117823\/"},"modified":"2025-09-03T22:40:09","modified_gmt":"2025-09-03T22:40:09","slug":"rice-algorithms-take-on-quantum-adversary","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/117823\/","title":{"rendered":"Rice algorithms take on quantum adversary"},"content":{"rendered":"

<\/p>\n

\"researchers\"\/<\/p>\n

<\/a><\/p>\n

image:\u00a0<\/p>\n

Maryam Aliakbarpour (from left), Yuhan Liu and Nai-Hui Chia<\/p>\n

view more\u00a0<\/a><\/p>\n

Credit: (Photo by Jeff Fitlow\/Rice University)<\/p>\n

HOUSTON \u2013 (Sept. 3, 2025) \u2013 Quantum computers promise enormous computational power, but the nature of quantum states makes computation and data inherently \u201cnoisy.\u201d Rice University computer scientists have developed algorithms that account for noise that is not just random but malicious. Their work could help make quantum computers more accurate and dependable.<\/p>\n

In a conventional computer, information is stored in bits \u23af a 0 or a 1 \u23af and can be read directly. In a quantum computer, information is stored in a quantum state, which entails a multiplicity of co-existing probabilities irreducible to any single value. Each time a quantum state is measured, this multiplicity collapses into a single, random outcome.<\/p>\n

\u201cAccording to the laws of quantum mechanics, observing a quantum state often \u2018destroys\u2019 it, resulting in a random measurement that only reveals partial information about that state,\u201d said Yuhan Liu, a Rice postdoctoral researcher and the lead author on a paper accepted to the Institute of Electrical and Electronics Engineers\u2019 flagship conference Symposium on Foundations of Computer Science, where it will be presented in December. \u201cQuantum state learning studies how to accurately translate quantum information by using multiple copies of the quantum state.\u201d<\/p>\n

Quantum state learning is sometimes also called quantum state tomography: Just as a medical scan pieces together cross sections to reconstruct an organ in three dimensions, tomography in quantum computing uses multiple copies of a quantum state to reconstruct it. The method is critical for benchmarking quantum hardware, validating quantum algorithms and ensuring the reliability of quantum technologies.<\/p>\n

Quantum devices are currently in a stage of development dubbed NISQ for \u201cnoisy intermediate-scale quantum,\u201d meaning they tend to be error prone: Tiny disturbances in the environment where a quantum device is located, unexpected disruptions at the physical hardware level or other calibration issues can easily corrupt quantum systems, introducing measurement errors. This means that for quantum state learning, handling quantum noise is one of the biggest challenges.<\/p>\n

\u201cScientists have tried to find different ways to model this noise,\u201d said Nai-Hui Chia<\/a>, assistant professor of computer science at Rice and co-corresponding author on the study.<\/p>\n

Many models assume noise occurs randomly or uniformly, but the Rice team introduced a more robust and realistic framework that considers not just random error but also the possibility of targeted interference.<\/p>\n

\u201cOur model is strong in the sense that it also considers nonphysical and potentially malicious factors that may affect the system,\u201d Chia said. \u201cOur goal here was to see if we could design a good algorithm to certify devices or do other tasks such that we would be secure against a deliberate attack by an adversary.\u201d<\/p>\n

The team introduced and investigated this new framework, showing it could deliver optimal results provided a \u201csufficiently large\u201d number of copies of a quantum state. The researchers also mapped the criteria for both peak performance and failure.<\/p>\n

\u201cWe wanted to understand the fundamental limits: What is the maximum level of corruption the algorithm can endure, beyond which there would be no hope to learn the information accurately?\u201d Liu said. \u201cThese questions are very important both in theory and in practice.\u201d<\/p>\n

The answer brings both good news and bad.<\/p>\n

\u201cThe bad news is that for some states, learning under adversarial noise is nearly impossible,\u201d Liu said. \u201cAn adversary only needs to change an exponentially small fraction of the states or measurements to totally fool any learning algorithm.\u201d<\/p>\n

But this happens only for states that \u201clook like pure noise,\u201d which would be useless for computation anyway.<\/p>\n

\u201cThe good news is that for a large class of well-structured states that are frequently used in many quantum algorithms, it is possible to achieve reasonably good accuracy, even when the noise is added maliciously,\u201d Liu said.<\/p>\n

While it was designed to deal with a problem in quantum computing, the new adversarially robust framework relied heavily on nonquantum statistical and algorithmic tools. For Liu, the work builds on the insight that while some problems may look like quantum problems, \u201cwhen you dig inside, the core difficulty relies on techniques from classical statistics and algorithms.\u201d<\/p>\n

Maryam Aliakbarpour<\/a>, a Rice computer scientist who studies learning theory<\/a> \u23af i.e. how machine learning works as a computational process \u23af contributed guidance and expertise on the classical side of the research.<\/p>\n

\u201cI design algorithms that solve statistical problems under realistic constraints, and some of the things I consider in my work were directly relevant here,\u201d said Aliakbarpour, Rice\u2019s Michael B. Yuen and Sandra A. Tsai Assistant Professor in the Department of Computer Science. \u201cThis problem was a true collaboration.\u201d<\/p>\n

To move past the NISQ stage, quantum technology will have to continue to improve strategies for effectively handling noise. One avenue for doing so is to fine-tune the underlying physical systems that host quantum states.<\/p>\n

Promising new discoveries in two-dimensional materials are edging closer to \u201cquieter\u201d and more stable quantum hardware, and Rice researchers are at the forefront of this exploration: Just a few examples of recent advances include wrinkles with stable electron spin texture<\/a>, strategic 2D metal layers that stage phonon interference<\/a> and carefully tuned 3D structures known as optical cavities that coax exotic quantum behaviors<\/a> into being.<\/p>\n

Another front for tackling the issue is at the software level: Quantum algorithms that are attuned to the complex and delicate nature of quantum states are critical for advancing quantum computing. The new adversarially robust framework developed by Aliakbarpour, Chia and Liu, together with Vladimir Braverman, an adjunct professor of computer science at Rice, offers a creative algorithmic approach to dealing with quantum computers\u2019 noisiness.<\/p>\n

The research was supported by the National Science Foundation (2528780, 2243659, 2339116), the U.S. Office of Naval Research (N00014-23-1-2737), the Department of Energy (DE-SC0024301) and Rice. The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations and institutions.<\/p>\n

-30-<\/p>\n

This news release can be found online at news.rice.edu<\/a>.<\/p>\n

Follow Rice News and Media Relations via Twitter @RiceUNews<\/a>.<\/p>\n

Peer-reviewed paper:<\/p>\n

Adversarially robust quantum state learning and testing | 2025 IEEE Symposium on Foundations of Computer Science<\/p>\n

Authors: Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia and Yuhan Liu<\/p>\n

Access associated media files:<\/p>\n

https:\/\/rice.box.com\/s\/6ynwyd0d33ye2it89v3x5492nzc5iv4o<\/a><\/p>\n

(Photos by Jeff Fitlow\/Rice University)<\/p>\n

About Rice:<\/p>\n

Located on a 300-acre forested campus in Houston, Texas, Rice University is consistently ranked among the nation\u2019s top 20 universities by U.S. News & World Report. Rice has highly respected schools of architecture, business, continuing studies, engineering and computing, humanities, music, natural sciences and social sciences and is home to the Baker Institute for Public Policy. Internationally, the university maintains the Rice Global Paris Center, a hub for innovative collaboration, research and inspired teaching located in the heart of Paris. With 4,776 undergraduates and 4,104 graduate students, Rice\u2019s undergraduate student-to-faculty ratio is just under 6-to-1. Its residential college system builds close-knit communities and lifelong friendships, just one reason why Rice is ranked No. 1 for lots of race\/class interaction and No. 7 for best-run colleges by the Princeton Review. Rice is also rated as a best value among private universities by the Wall Street Journal and is included on Forbes\u2019 exclusive list of \u201cNew Ivies.\u201d<\/p>\n

If you do not wish to receive news releases from Rice University, reply to this email and write \u201cunsubscribe\u201d in the subject line. Office of News and Media Relations \u2013 MS 300, Rice University, 6100 Main St., Houston, TX 77005.<\/p>\n

Article Title<\/p>\n

Adversarially robust quantum state learning and testing<\/p>\n

COI Statement<\/p>\n

The authors declare no competing interest.<\/p>\n