New algorithm could be quantum leap in search for gravitational waves

Credit: CC0 Public Domain

A new method of identifying gravitational wave signals using quantum computing could provide a valuable new tool for future astrophysicists.

A team from the University of Glasgow’s School of Physics & Astronomy have developed a to drastically cut down the time it takes to match gravitational wave signals against a vast databank of templates.

This process, known as matched filtering, is part of the methodology that underpins some of the gravitational wave signal discoveries from detectors like the Laser Interferometer Gravitational Observatory (LIGO) in America and Virgo in Italy.

Those detectors, the most sensitive sensors ever created, pick up the faint ripples in spacetime caused by massive astronomical events like the collision and merger of black holes.

Matched filtering allows computers to pick gravitational wave signals out of the noise of the data collected by the detector. It works by sifting through the data, searching for a signal which matches one out of potentially hundreds of trillions of templates—pieces of pre-created data which are likely to correlate with a genuine gravitational wave signal.

While the process has enabled numerous gravitational wave detections since LIGO picked up its first signal in September 2015, it is time-consuming and resource-intensive.

In a new paper published in the journal Physical Review Research, the team describe how the process could be greatly accelerated by a technique called Grover’s .

Grover’s algorithm, developed by computer scientist Lov Grover in 1996, harnesses the unusual capabilities and applications of quantum theory to make the process of searching through databases much faster.

While quantum computers capable of processing data using Grover’s algorithm are still a developing technology, conventional computers are capable of modeling their behavior, allowing researchers to develop techniques which can be adopted when the technology has matured and quantum computers are readily available.

The Glasgow team are the first to adapt Grover’s algorithm for the purposes of gravitational wave search. In the paper, they demonstrate how they have applied it to gravitational wave searches through software they developed using the Python programming language and Qiskit, a tool for simulating quantum computing processes.

The system the team developed is capable of a speed-up in the number of operations proportional to the square-root of the number of templates. Current quantum processors are much slower at performing basic operations than classical computers, but as the technology develops, their performance is expected to improve. This reduction in the number of calculations would translate into a speed up in time. In the best case that means that, for example, if a search using classical computing would take a year, the same search could take as little as a week with their quantum algorithm.

Dr. Scarlett Gao, from the University’s School of Physics & Astronomy, is one of the lead authors of the paper. Dr. Gao said: “Matched filtering is a problem that Grover’s algorithm seems well-placed to help solve, and we’ve been able to develop a system which shows that quantum computing could have valuable applications in gravitational wave astronomy.

“My co-author and I were Ph.D. students when we began this work, and we’re lucky to have had access to the support of some of the UK’s leading quantum computing and gravitational wave researchers during the process of developing this software.

“While we’ve concentrated on one type of search in this paper, it’s possible that it could also be adapted for other processes which, like this one, don’t require the database to be loaded into quantum .”

Fergus Hayes, a Ph.D. student in the School of Physics & Astronomy, is co-lead author of the paper. He added: “Researchers here in Glasgow have been working on gravitational wave physics for more than 50 years, and work in our Institute for Gravitational Research helped to underpin the development and data analysis sides of LIGO.

“The cross-disciplinary work that Dr. Gao and I led has demonstrated the potential of quantum computing in matched filtering. As quantum computers develop in the coming years, it’s possible that processes like these could be used in future gravitational wave detectors. It’s an exciting prospect, and we’re looking forward to developing this initial proof of concept in the future.”

The paper was co-written by Dr. Sarah Croke, Dr. Christopher Messenger and Dr. John Veitch, all from the University of Glasgow’s School of Physics & Astronomy.

The team’s paper, titled “A quantum algorithm for gravitational wave matched filtering,” is published in Physical Review Research.

More information:
A quantum algorithm for gravitational wave matched filtering, arXiv:2109.01535 [quant-ph]

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Hexbyte Glen Cove New algorithm uses online learning for massive cell data sets thumbnail

Hexbyte Glen Cove New algorithm uses online learning for massive cell data sets

Hexbyte Glen Cove

Credit: CC0 Public Domain

The fact that the human body is made up of cells is a basic, well-understood concept. Yet amazingly, scientists are still trying to determine the various types of cells that make up our organs and contribute to our health.

A relatively recent technique called single-cell sequencing is enabling researchers to recognize and categorize by characteristics such as which genes they express. But this type of research generates enormous amounts of data, with datasets of hundreds of thousands to millions of .

A developed by Joshua Welch, Ph.D., of the Department of Computational Medicine and Bioinformatics, Ph.D. candidate Chao Gao and their team uses , greatly speeding up this process and providing a way for researchers world-wide to analyze using the amount of memory found on a standard laptop computer. The findings are described in the journal Nature Biotechnology.

“Our technique allows anyone with a computer to perform analyses at the scale of an entire organism,” says Welch. “That’s really what the field is moving towards.”

The team demonstrated their proof of principle using data sets from the National Institute of Health’s Brain Initiative, a project aimed at understanding the human brain by mapping every cell, with investigative teams throughout the country, including Welch’s lab.

Typically, explains Welch, for projects like this one, each single-cell data set that is submitted must be re-analyzed with the previous in the order they arrive. Their new approach allows new datasets to the be added to existing ones, without reprocessing the older datasets. It also enables researchers to break up datasets into so-called mini-batches to reduce the amount of memory needed to process them.

“This is crucial for the sets increasingly generated with millions of cells,” Welch says. “This year, there have been five to six papers with two million cells or more and the amount of memory you need just to store the raw data is significantly more than anyone has on their computer.”

Welch likens the online technique to the continuous data processing done by like Facebook and Twitter, which must process continuously-generated data from users and serve up relevant posts to people’s feeds. “Here, instead of people writing tweets, we have labs around the world performing experiments and releasing their data.”

The finding has the potential to greatly improve efficiency for other ambitious projects like the Human Body Map and Human Cell Atlas. Says Welch, “Understanding the normal compliment of cells in the body is the first step towards understanding how they go wrong in disease.”

More information:
Chao Gao et al, Iterative single-cell multi-omic integration using online learning, Nature Biotechnology (2021). DOI: 10.1038/s41587-021-00867-x

New algorithm uses online learning for massive cell data sets (2021, April 19)
retrieved 20 April 2021

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Hexbyte Glen Cove CCNY team in quantum algorithm breakthrough thumbnail

Hexbyte Glen Cove CCNY team in quantum algorithm breakthrough

Hexbyte Glen Cove

The Google Quantum Computer. Credit: Google Quantum AI

Researchers led by City College of New York physicist Pouyan Ghaemi report the development of a quantum algorithm with the potential to study a class of many-electron quantums system using quantum computers. Their paper, entitled “Creating and Manipulating a Laughlin-Type ν=1/3 Fractional Quantum Hall State on a Quantum Computer with Linear Depth Circuits,” appears in the December issue of PRX Quantum, a journal of the American Physical Society.

“Quantum physics is the fundamental theory of nature which leads to formation of molecules and the resulting matter around us,” said Ghaemi, assistant professor in CCNY’s Division of Science. “It is already known that when we have a macroscopic number of quantum particles, such as electrons in the metal, which interact with each other, novel phenomena such as superconductivity emerge.”

However, until now, according to Ghaemi, tools to study systems with large numbers of interacting quantum particles and their novel properties have been extremely limited.

“Our research has developed a which can be used to study a class of many-electron quantum systems using quantum computers. Our algorithm opens a new venue to use the new quantum devices to study problems which are quite challenging to study using classical computers. Our results are new and motivate many follow up studies,” added Ghaemi.

On for this advancement, Ghaemi, who’s also affiliated with the Graduate Center, CUNY noted: “Quantum computers have witnessed extensive developments during the last few years. Development of new quantum algorithms, regardless of their direct application, will contribute to realize applications of quantum computers.

“I believe the direct application of our results is to provide tools to improve devices. Their direct real-life application would emerge when quantum computers can be used for daily life applications.”

More information:
Armin Rahmani et al. Creating and Manipulating a Laughlin-Type ν=1/3 Fractional Quantum Hall State on a Quantum Computer with Linear Depth Circuits, PRX Quantum (2020). DOI: 10.1103/PRXQuantum.1.020309

CCNY team in quantum algorithm breakthrough (2020, November 13)
retrieved 16 November 2020

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