From Swiss Federal Institute of Technology in Lausanne [EPFL-École Polytechnique Fédérale de Lausanne] (CH): “Running quantum software on a classical computer”

From Swiss Federal Institute of Technology in Lausanne [EPFL-École Polytechnique Fédérale de Lausanne] (CH)

03.08.21

Two physicists, from EPFL and Columbia University (US), have introduced an approach for simulating the quantum approximate optimization algorithm using a traditional computer. Instead of running the algorithm on advanced quantum processors, the new approach uses a classical machine-learning algorithm that closely mimics the behavior of near-term quantum computers.

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Credit: iStock photos.

In a paper published in Nature Quantum Information, EPFL professor Giuseppe Carleo and Matija Medvidović, a graduate student at Columbia University and at the Flatiron Institute (US) in New York, have found a way to execute a complex quantum computing algorithm on traditional computers instead of quantum ones.

The specific “quantum software” they are considering is known as “Quantum Approximate Optimization Algorithm” (QAOA) and is used to solve classical optimization problems in mathematics; it’s essentially a way of picking the best solution to a problem out of a set of possible solutions. “There is a lot of interest in understanding what problems can be solved efficiently by a quantum computer, and QAOA is one of the more prominent candidates,” says Carleo.

Ultimately, QAOA is meant to help us on the way to the famed “quantum speedup”, the predicted boost in processing speed that we can achieve with quantum computers instead of conventional ones. Understandably, QAOA has a number of proponents, including Google, who have their sights set on quantum technologies and computing in the near future: in 2019 they created Sycamore, a 53-qubit quantum processor, and used it to run a task it estimated it would take a state-of-the-art classical supercomputer around 10,000 years to complete. Sycamore ran the same task in 200 seconds.

Google 53-qubit “Sycamore” superconducting processor quantum computer.

“But the barrier of “quantum speedup” is all but rigid and it is being continuously reshaped by new research, also thanks to the progress in the development of more efficient classical algorithms,” says Carleo.

In their study, Carleo and Medvidović address a key open question in the field: can algorithms running on current and near-term quantum computers offer a significant advantage over classical algorithms for tasks of practical interest? “If we are to answer that question, we first need to understand the limits of classical computing in simulating quantum systems,” says Carleo. This is especially important since the current generation of quantum processors operate in a regime where they make errors when running quantum “software”, and can therefore only run algorithms of limited complexity.

Using conventional computers, the two researchers developed a method that can approximately simulate the behavior of a special class of algorithms known as variational quantum algorithms, which are ways of working out the lowest energy state, or “ground state” of a quantum system. QAOA is one important example of such family of quantum algorithms, that researchers believe are among the most promising candidates for “quantum advantage” in near-term quantum computers.

The approach is based on the idea that modern machine-learning tools, e.g. the ones used in learning complex games like Go, can also be used to learn and emulate the inner workings of a quantum computer. The key tool for these simulations are Neural Network Quantum States, an artificial neural network that Carleo developed in 2016 with Matthias Troyer, and that was now used for the first time to simulate QAOA. The results are considered the province of quantum computing, and set a new benchmark for the future development of quantum hardware.

“Our work shows that the QAOA you can run on current and near-term quantum computers can be simulated, with good accuracy, on a classical computer too,” says Carleo. “However, this does not mean that alluseful quantum algorithms that can be run on near-term quantum processors can be emulated classically. In fact, we hope that our approach will serve as a guide to devise new quantum algorithms that are both useful and hard to simulate for classical computers.”

See the full article here .

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The Swiss Federal Institute of Technology in Lausanne [EPFL-École polytechnique fédérale de Lausanne] (CH) is a research institute and university in Lausanne, Switzerland, that specializes in natural sciences and engineering. It is one of the two Swiss Federal Institutes of Technology, and it has three main missions: education, research and technology transfer.

The QS World University Rankings ranks EPFL(CH) 14th in the world across all fields in their 2020/2021 ranking, whereas Times Higher Education World University Rankings ranks EPFL(CH) as the world’s 19th best school for Engineering and Technology in 2020.

EPFL(CH) is located in the French-speaking part of Switzerland; the sister institution in the German-speaking part of Switzerland is the Swiss Federal Institute of Technology ETH Zürich [Eidgenössische Technische Hochschule Zürich)](CH) . Associated with several specialized research institutes, the two universities form the Domain of the Swiss Federal Institutes of Technology (ETH Domain) [ETH-Bereich; Domaine des Écoles polytechniques fédérales] (CH) which is directly dependent on the Federal Department of Economic Affairs, Education and Research. In connection with research and teaching activities, EPFL(CH) operates a nuclear reactor CROCUS; a Tokamak Fusion reactor; a Blue Gene/Q Supercomputer; and P3 bio-hazard facilities.

The roots of modern-day EPFL(CH) can be traced back to the foundation of a private school under the name École spéciale de Lausanne in 1853 at the initiative of Lois Rivier, a graduate of the École Centrale Paris (FR) and John Gay the then professor and rector of the Académie de Lausanne. At its inception it had only 11 students and the offices was located at Rue du Valentin in Lausanne. In 1869, it became the technical department of the public Académie de Lausanne. When the Académie was reorganised and acquired the status of a university in 1890, the technical faculty changed its name to École d’ingénieurs de l’Université de Lausanne. In 1946, it was renamed the École polytechnique de l’Université de Lausanne (EPUL). In 1969, the EPUL was separated from the rest of the University of Lausanne and became a federal institute under its current name. EPFL(CH), like ETH Zürich(CH), is thus directly controlled by the Swiss federal government. In contrast, all other universities in Switzerland are controlled by their respective cantonal governments. Following the nomination of Patrick Aebischer as president in 2000, EPFL(CH) has started to develop into the field of life sciences. It absorbed the Swiss Institute for Experimental Cancer Research (ISREC) in 2008.

In 1946, there were 360 students. In 1969, EPFL(CH) had 1,400 students and 55 professors. In the past two decades the university has grown rapidly and as of 2012 roughly 14,000 people study or work on campus, about 9,300 of these being Bachelor, Master or PhD students. The environment at modern day EPFL(CH) is highly international with the school attracting students and researchers from all over the world. More than 125 countries are represented on the campus and the university has two official languages, French and English.

Organization

EPFL is organised into eight schools, themselves formed of institutes that group research units (laboratories or chairs) around common themes:

School of Basic Sciences (SB, Jan S. Hesthaven)

Institute of Mathematics (MATH, Victor Panaretos)
Institute of Chemical Sciences and Engineering (ISIC, Emsley Lyndon)
Institute of Physics (IPHYS, Harald Brune)
European Centre of Atomic and Molecular Computations (CECAM, Ignacio Pagonabarraga Mora)
Bernoulli Center (CIB, Nicolas Monod)
Biomedical Imaging Research Center (CIBM, Rolf Gruetter)
Interdisciplinary Center for Electron Microscopy (CIME, Cécile Hébert)
Max Planck-EPFL Centre for Molecular Nanosciences and Technology (CMNT, Thomas Rizzo)
Swiss Plasma Center (SPC, Ambrogio Fasoli)
Laboratory of Astrophysics (LASTRO, Jean-Paul Kneib)

School of Engineering (STI, Ali Sayed)

Institute of Electrical Engineering (IEL, Giovanni De Micheli)
Institute of Mechanical Engineering (IGM, Thomas Gmür)
Institute of Materials (IMX, Michaud Véronique)
Institute of Microengineering (IMT, Olivier Martin)
Institute of Bioengineering (IBI, Matthias Lütolf)

School of Architecture, Civil and Environmental Engineering (ENAC, Claudia R. Binder)

Institute of Architecture (IA, Luca Ortelli)
Civil Engineering Institute (IIC, Eugen Brühwiler)
Institute of Urban and Regional Sciences (INTER, Philippe Thalmann)
Environmental Engineering Institute (IIE, David Andrew Barry)

School of Computer and Communication Sciences (IC, James Larus)

Algorithms & Theoretical Computer Science
Artificial Intelligence & Machine Learning
Computational Biology
Computer Architecture & Integrated Systems
Data Management & Information Retrieval
Graphics & Vision
Human-Computer Interaction
Information & Communication Theory
Networking
Programming Languages & Formal Methods
Security & Cryptography
Signal & Image Processing
Systems

School of Life Sciences (SV, Gisou van der Goot)

Bachelor-Master Teaching Section in Life Sciences and Technologies (SSV)
Brain Mind Institute (BMI, Carmen Sandi)
Institute of Bioengineering (IBI, Melody Swartz)
Swiss Institute for Experimental Cancer Research (ISREC, Douglas Hanahan)
Global Health Institute (GHI, Bruno Lemaitre)
Ten Technology Platforms & Core Facilities (PTECH)
Center for Phenogenomics (CPG)
NCCR Synaptic Bases of Mental Diseases (NCCR-SYNAPSY)

College of Management of Technology (CDM)

Swiss Finance Institute at EPFL (CDM-SFI, Damir Filipovic)
Section of Management of Technology and Entrepreneurship (CDM-PMTE, Daniel Kuhn)
Institute of Technology and Public Policy (CDM-ITPP, Matthias Finger)
Institute of Management of Technology and Entrepreneurship (CDM-MTEI, Ralf Seifert)
Section of Financial Engineering (CDM-IF, Julien Hugonnier)

College of Humanities (CDH, Thomas David)

Human and social sciences teaching program (CDH-SHS, Thomas David)

EPFL Middle East (EME, Dr. Franco Vigliotti)[62]

Section of Energy Management and Sustainability (MES, Prof. Maher Kayal)

In addition to the eight schools there are seven closely related institutions

Swiss Cancer Centre
Center for Biomedical Imaging (CIBM)
Centre for Advanced Modelling Science (CADMOS)
École cantonale d’art de Lausanne (ECAL)
Campus Biotech
Wyss Center for Bio- and Neuro-engineering
Swiss National Supercomputing Centre