The work developing and improving quantum processors
continues to accelerate as companies
big and small push to reach the point of fault-tolerant quantum systems. We
have written about a number of the advancements, such as breakthroughs over the
past year or more by Google,
Microsoft,
and Amazon
Web Services to create quantum chips that can address the issue of error
correction, a significant hurdle that needs to be cleared before commercial,
fault-tolerant quantum systems can become a reality.
More recent efforts have been undertaken in quantum
processor development. A group of scientists at the International Quantum
Academy in Shenzhen, China, this week published a paper in Nature
Nanotechnology unveiling
a superconductor-based quantum chip to address such issues as errors from
“environmental noise” like noise, light, and movement – which can lead to
errors – as well as the amount of resources needed when managing and encoding
logical qubits. The scientists addressed this by using dual-rail encoding that
uses two physical superconducting qubits to create a single logical qubit.
In a paper earlier this month published in Nature
Electronics, SEEQC showed how digital superconducting digital control
circuits can reliably
integrate with quantum chips at millikelvin temperatures, a step forward in
addressing the challenge of scaling superconducting quantum architectures.
Scientists with Bangledash, India-based QpiA this week said they
created
a scalable error correction system – a decoder – that uses a rotated
surface code architecture to allow for fast and scalable error correction. It
can operate in real time next to superconducting qubits and is another step on
the way to fault-tolerant quantum systems, according to the company.
The Challenge Of Simulating Quantum Materials
However, while quantum chip development is going at a rapid
pace, a question has been whether today’s quantum processors and their
relatively limited number of qubits can realistically simulate quantum
materials, a key goal in quantum computing that will drive the ability to do
what proponents say such systems need to do. This includes simulating the
structures of molecules, proteins, and catalysts for drug discovery, the
behavior of materials for better superconductors, and even their own error
correction codes to maintain qubit coherence – the ability to keep a fixed
phase relationship – in noisy environments.
To do that, they need to understand quantum behavior,
something that can’t always be done using classical computing methods.
A group of scientists from
IBM, the US Department of Energy-funded Quantum Science Center at Oak
Ridge National Laboratory, Purdue University, the University of Illinois at Urbana-Champaign, Los Alamos National Laboratory, and the University of
Tennessee showed that a superconducting 50-qubit IBM Heron r2 quantum processor
can simulate real magnetic materials that match the results of experiments run
on classical computers.
They wrote in a 49-page preprint paper on arXiv that this
means that quantum hardware available now, used with new algorithms and
quantum-centric supercomputing workloads, can simulate the properties of
materials, putting them on the path to being useful tools for scientific
discovery.
Advances in methods used by classical computers has allowed
them to accurately predict properties of quantum materials, the scientists
wrote, but that “strongly correlated systems with long-range entanglement and
complex dynamics remain beyond the reach of these approaches. Quantum computers
offer a potential alternative to addressing this.”
That’s because quantum systems align with the physical rules
of such materials, making them a natural fit for simulating quantum systems.
It’s something that physicist Richard Feynman proffered years ago.
However, while in quantum computing, the growing numbers of
qubits and improve gate fidelity have led to studies of static and dynamical
properties of many-body systems at scales beyond what classical systems can do,
the feeling has been that today’s quantum systems don’t have the necessary
capabilities – such as circuit depths and error rates – to simulate such
realistic materials, they wrote.
“It has therefore been unclear whether current, pre-fault
tolerant quantum computers can ever perform quantitatively reliable many-body
simulations of quantum materials that can be closely compared with laboratory
measurements,” the scientists wrote.
Running The Test
For the experiment, they targeted a sample of KCuF₃, a
magnetic compound whose measurements had been captured through neutron scatting
experiments to show the internal behavior of materials. Neutron scattering
works, but it’s difficult to compute classically, according to the scientists.
The work was run on the Heron processor, with the
experimental data gathered from neutron sources at the Spallation Neutron
Source at Oak Ridge (shown in the feature image at the top of this story) and
the UK’s Rutherford Appleton Laboratory. To reduce the circuit depth of the
quantum circuits, the scientists also used a noise-robust algorithm and
classical computing resources at the Illinois Campus Cluster.
The simulation of the KCuF₃ sample run on the IBM quantum
system (on the right, below) matched the results of the neutron scattering
experiments (left).
It falls in line with what IBM and other quantum vendors see
as the likely scenario of quantum computers working
with classical HPC systems in a hybrid-computing
fashion, with the quantum systems taking on workloads and equations that
are beyond the reach of their classical brethren.
The plan for the researchers moving forward is to use this
type of simulation with quantum materials that are more dimensioned and more
complex than KCuF₃.
The scientists wrote that their work demonstrates that “quantum
simulation of real materials on pre-fault-tolerant, programmable quantum
hardware is no longer an elusive goal. … These quantum simulations faithfully
capture key emergent quantum phenomena observed in real materials, including
the two-spinon continuum and the manner in which anisotropy and realistic
next-nearest-neighbor couplings reshape this continuum. Together, these results
establish that quantum computers are moving beyond proof-of-principle testbeds
and are beginning to function as practical scientific tools for the study of
quantum materials.”
A Roadmap For Hybrid ModSim And Quantum Systems
It also gives quantum computing vendors a roadmap for
assessing quantum simulations in increasingly complex settings. There
eventually will be a time in which quantum simulation shifts from a
benchmarking practice to a key tool for “condensed-matter physics,” allowing
scientists to use quantum devices to delve into areas that are beyond the reach
of classical simulation, they wrote.
“Looking ahead, as quantum devices scale to larger lattices,
support longer time evolutions, and address generic two-dimensional
non-integrable models, this framework positions quantum simulation as an engine
for constructing comprehensive equilibrium descriptions of quantum materials,”
the scientists wrote.
The quantum simulation work is the latest in a string of
quantum-related announcements by IBM this month. The vendor joined with The
University of Manchester, Oxford University, ETH Zurich, EPFL and the
University of Regensburg to create
a half-Möbius electronic topology in a single molecule – something never
seen before – and then used an IBM quantum system to determine why it worked, a
job that would be difficult for classical computers.
Earlier this week, IBM announced that in research run with
the Cleveland Clinic, they used a Heron quantum chip and a quantum-centric
supercomputing workflow to simulate
the electronic structure of a protein.
