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New quantum machines make first discoveries

Each horizontal line shows the magnetic state of a particular atom, giving researchers a snapshot of the quantum system. Data: J. Zhang et al. / University of Maryland. Graphic: E. Edwards / University of Maryland

Two teams of physicists have built the largest controlled quantum simulators yet, which allowed them to determine how matter can behave at the quantum level, something conventional supercomputers struggle to do.

Why it matters: IBM, Google, Microsoft and other companies, as well as university researchers are racing to build quantum computers. It's still an open playing field with each approach so far offering its own advantages and disadvantages. The designs used in these simulators could be one way to get there and, while restricted versions of a quantum computer, they are a step into the territory where quantum devices are thought to be able to outperform classical ones.

How it works:

  • Classical computers can handle two states of information (the "0s" and "1s" of binary code). Quantum bits or qubits analogous to the bits in classical computers are made of atoms, silicon or superconducting materials and can represent multiple states of information at the same time.
  • A quantum computer can perform computations on these different states simultaneously, but in the end gives one solution. (A 50+ qubit system can collectively store more than a quadrillion possible states in the atoms at the same time.)
  • Increasing the number of qubits exponentially increases the number of possible configurations and therefore the machine's processing power. Researchers are trying to build ever-bigger but still controlled systems in hopes of eventually putting together tens of thousands or millions of qubits that can solve a range of problems exponentially faster than a conventional computer.

Reality check: The recent demonstrations are simulations of specific problems not computations. The researchers solved some arguably esoteric physics problems, says Christopher Monroe, a quantum physicist at the University of Maryland who led work on one of the simulators, but are an illustration of the types of hard problems quantum computers can take on.

The big picture: The goal of quantum computing is to create a machine that can solve a range of problems a classical computer cannot. The reality is currently just a handful of qubits can be controlled well enough to perform computations. One approach then is to first build quantum simulators that can use a larger number of well-controlled quantum qubits to solve specific problems in order to figure out how best to then try to design a universal quantum computer.

And, along the way, being able to build quantum systems that can be completely controlled may allow researchers to understand the physics of systems we don't have access to, says Stephen Bartlett, a quantum physicist at the University of Sydney who wasn't involved in either of the studies. That could prove useful in designing drugs or optimizing chemical processes — problems that currently occupy supercomputers.

"What's exciting about these two new experiment is they have managed to get up to 50 qubits while keeping a high level of control over the system. It's not everything you would want and the control is different between the two but it is much more than we had a few years ago," says Bartlett.

The experiments:

  • Hannes Bernein and his colleagues at MIT, Harvard and Caltech used 51 uncharged atomic qubits. They built their system atom by atom in a vacuum chamber using lasers to hold each atom in place. One advantage of their approach is that because they can adjust the lasers independently, they have control over the atoms to encode the problems they are trying to solve. Another plus is they suspect it may be easier to add more qubits to their design.
  • A team from the University of Maryland and National Institute of Standards and Technology (NIST) led by Monroe used 53 electrically charged atoms as their qubits and applied a force via a laser so the atoms either align with others nearby to create a magnet or point in random directions. The researchers can then change the strength of the laser and determine the boundary of conditions where matter becomes magnetic. Because the atoms are interacting with one another, the researchers have a sort of global knob of control over them. One advantage of this setup is the qubits can be stable for several hours rather than milliseconds.
  • While IBM and Google are working with superconducting materials — which benefit from years of research and development related to their use in today's computers— Monroe is an advocate for the atom approach in part because they are all identical. "Good quantum hardware is composed of systems you can replicate," he says.

A big question: Once you get a result, how do you corroborate it is right if you can't compare it to a classical computer? asks Alexander Keesling from the Harvard team. It's an active area of discussion and one to watch for as quantum computers move out of university labs and into the commercial space where a buyer will want to know they are getting what is advertised. Some possibilities for validating include cross-checking simulations in smaller quantum systems with conventional computers or using multiple different simulators to see if they agree on the results.

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Early humans innovated tools earlier than thought

Archaeologist Rick Potts squats in the Olorgesailie Basin in Kenya with various surprisingly sophisticated tools found from 320,000 years ago.
Richard Potts surveys assortment of Early Stone Age handaxes discovered in the Olorgesailie Basin, Kenya. Photo: Human Origins Program, Smithsonian

Unpredictable climate and natural disasters like earthquakes may have spurred early humans to create innovative tools and ways to communicate earlier than previously thought, according to 3 studies published Thursday in Science.

What they found: Evidence that around 320,000 years ago — near the start of the Middle Stone Age (MSA) and tens of thousands of years earlier than previous evidence has shown — early humans in East Africa may have created projectile hunting tools, developed ways to communicate using colors for mapping or identification purposes, and traveled longer distances to trade, hunt or obtain valuable materials.

"It's not just humans changing but really the entire ecosystem. It's a picture that's bigger than just the human ancestors themselves."
— Smithsonian's Richard Potts, who spearheaded the studies
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Yejin Choi: Trying to give AI some common sense

A photo of Yejin Choi from the University of Washington and the Allen Institute for Artificial Intelligence.
Photo illustration: Axios Visuals

Artificial intelligence researchers have tried unsuccessfully for decades to give machines the common sense needed to converse with humans and seamlessly navigate our always-changing world. Last month, Paul Allen announced he is investing another $125 million into his Allen Institute for Artificial Intelligence (AI2) in a renewed effort to solve one of the field's grand challenges.

Axios spoke with Yejin Choi, an AI researcher from the University of Washington and AI2 who studies how machines process and generate language. She talked about how they're defining common sense, their approach to the problem and how it's connected to bias.