QuICS Researchers Have a Dozen Papers Accepted to QIP

Researchers from the Joint Center for Quantum Information and Computer Science (QuICS) have 12 talks accepted at the 25th Annual Conference on Quantum Information Processing (QIP), the premier international conference on theoretical topics in quantum information and quantum computation.

This year’s conference—hosted by the California Institute of Technology—will be held March 7–11 in Pasadena, California.

QuICS graduate students, Fellows, postdoctoral researchers and alumni are all represented in the conference program, which will feature more than 100 talks.

“Looking at all these new results—from our own research community and others attending this year’s conference—you can see both the intellectual excitement of this field, and the theoretical foundations that will lead to future quantum technologies,” says Yi-Kai Liu, co-director of QuICS.

Two out of the three highly competitive plenary talks this year will highlight research by QuICS members.

One of those talks features research from QuICS graduate student Seyed Sajjad Nezhadi, who, with two researchers from Columbia University, investigated decision problems related to nonlocal games and their connection to a family of computability classes. These theoretical games involve two cooperating parties who try to maximize their payoff by using quantum resources like entanglement.

In their paper, “Nonlocal Games, Compression Theorems, and the Arithmetical Hierarchy,” the authors prove that determining whether the payoff of a nonlocal game is equal to 1 allows for solutions to problems in the second level of the arithmetical hierarchy—a grouping of problems related to first-order logic. Crucially, this result implies that determining the exact payoff of nonlocal games is strictly harder than approximating it.

Another plenary talk features work by QuICS Fellow Victor Albert and colleagues that proves classical machine learning algorithms can be useful for predicting the properties of certain kinds of quantum systems—assuming they are fed the results of quantum measurements performed on similar systems.

Their paper, “Provably efficient machine learning for quantum many-body problems,” also proves that classical machine learning algorithms can efficiently classify a wide range of quantum phases of matter and supports the theoretical results with numerical experiments.

Albert is also an author on two other papers that will be presented at the conference, including one that will be discussed during a short plenary talk. That paper, titled “Chiral central charge from a single wave function,” introduces a formula for a key property of some many-body quantum systems: the chiral central charge. The authors derive the formula using relatively small pieces of a many-body ground state—the state of the system with the lowest total energy.

Yuan Su, a recent QuICS graduate student who is now a research scientist at Google Quantum AI, is an author of two papers that will be presented. On one, he collaborated with Albert and several other colleagues to prove that quantum computers can accurately simulate quantum systems composed of bosons and certain quantum field theories. In a second paper, Su joined QuICS Hartree Postdoctoral Fellow Dong An and colleagues to devise a quantum algorithm that solves linear systems of equations in an asymptotically optimal number of steps.

In addition to the papers noted above, QuICS researchers contributed to talks at this year’s conference focused on Hamiltonian complexity, quantum-secure cryptography, quantum neural networks, quantum celular automata, quantum simulation algorithms, and the behavior of random quantum circuits.

Cryptography and quantum machine learning will also be featured during the two days of tutorials that precede the conference. There will also be hundreds of posters presented in addition to the research talks, and organizers have even carved out time from a busy schedule for a quantum chess tournament.

Papers

Below is a complete list of the papers by QuICS researchers that are being presented at this year’s conference.

• “Provably efficient machine learning for quantum many-body problems” by Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V. Albert (QuICS/NIST) and John Preskill

• “Nonlocal Games, Compression Theorems, and the Arithmetical Hierarchy” by Hamoon Mousavi, Seyed Sajjad Nezhadi (QuICS/UMD) and Henry Yuen

• “Chiral central charge from a single wave function” by Isaac Kim, Bowen Shi, Kohtaro Kato and Victor V. Albert (QuICS/NIST)

• “Computational Complexity of the Ground State Energy Density Problem” by James Watson (UMD, QuICS postdoctoral scholar) and Toby S. Cubitt

• “Provably accurate simulation of gauge theories and bosonic systems” by Yu Tong, Victor V. Albert (QuICS/NIST), Jarrod McClean, John Preskill and Yuan Su (QuICS/UMD alumnus)

• “Post-Quantum Security of the Even-Mansour Cipher” by Gorjan Alagic (QuICS/NIST), Chen Bai, Jonanthan Katz (QuICS/UMD) and Christian Majenz

• “Analyzing the Loss Landscape of Quantum Neural Networks: Barren Plateaus and Overparametrization” by Martin Larocca, Marco Vinicio Sebastian de la Roca, Patrick Coles, Kunal Sharma (UMD, QuICS Hartree Postdoctoral Fellow), Piotr Czarnik, Gopikrishnan Muraleedharan, Diego Garcia-Martin and Nathan Ju (This work is a combination of two separate papers: “Diagnosing barren plateaus with tools from quantum optimal control” and “Theory of overparametrization in quantum neural networks”)

• “Optimal scaling quantum linear systems solver via discrete adiabatic theorem” by Pedro C.S. Costa, Dong An (UMD, QuICS Hartree Postdoctoral Fellow), Yuval R. Sanders, Yuan Su (QuICS/UMD alumnus), Ryan Babbush and Dominic W. Berry

• “Three-dimensional quantum cellular automata and chiral semion surface topological order” by Wilbur Shirley, Yu-An Chen (UMD, QuICS postdoctoral scholar), Arpit Dua, Tyler Ellison, Nathanan Tantivasadakarn and Dominic Williamson

• “Linear growth of quantum circuit complexity” by Jonas Haferkamp, Philippe Faist, Naga B. T. Kothakonda, Jens Eisert and Nicole Yunger Halpern (QuICS/NIST)

• “Hamiltonian simulation with random inputs” by Qi Zhao (QuICS, Hartree Postdoctoral Fellow), You Zhou, Alexander F. Shaw (QuICS, Lanczos Graduate Fellow), Tongyang Li (QuICS/UMD alumnus) and Andrew M. Childs (QuICS/UMD)

• “Tight bounds on the convergence of noisy random circuits to uniform” by Abhinav Deshpande (QuICS/UMD alumnus), Bill Fefferman (formerly a QuICS assistant research professor), Alexey Gorshkov (QuICS/NIST), Michael Gullans (QuICS/NIST), Pradeep Niroula (QuICS graduate student) and Oles Shtanko (former QuICS postdoctoral scholar)

—Story by Melissa Brachfeld and Chris Cesare