The Most Powerful Quantum Computer Ever

A team of physicists from the Harvard-MIT Center for Ultracold Atoms and other universities has developed a special type of quantum computer known as a programmable quantum simulator capable of operating with 256 quantum bits, or “qubits.” The system marks a major step toward building large-scale quantum machines that could be used to shed light on a host of complex quantum processes and eventually help bring about real-world breakthroughs in material science, communication technologies, finance, and many other fields, overcoming research hurdles that are beyond the capabilities of even the fastest supercomputers today. Qubits are the fundamental building blocks on which quantum computers run and the source of their massive processing power.

This moves the field into a new domain where no one has ever been to thus far,” said Mikhail Lukin, the George Vasmer Leverett Professor of Physics, co-director of the Harvard Quantum Initiative, and one of the senior authors of the study published today in the journal Nature. “We are entering a completely new part of the quantum world.” 

According to Sepehr Ebadi, a physics student in the Graduate School of Arts and Sciences and the study’s lead author, it is the combination of system’s unprecedented size and programmability that puts it at the cutting edge of the race for a quantum computer, which harnesses the mysterious properties of matter at extremely small scales to greatly advance processing power. Under the right circumstances, the increase in qubits means the system can store and process exponentially more information than the classical bits on which standard computers run

The number of quantum states that are possible with only 256 qubits exceeds the number of atoms in the solar system,” Ebadi said, explaining the system’s vast size.

Already, the simulator has allowed researchers to observe several exotic quantum states of matter that had never before been realized experimentally, and to perform a quantum phase transition study so precise that it serves as the textbook example of how magnetism works at the quantum level.


Quantum Supremacy

Researchers in UC Santa Barbara/Google scientist John Martinis’ group have made good on their claim to quantum supremacy. Using 53 entangled quantum bits (“qubits”), their Sycamore computer has taken on — and solved — a problem considered intractable for classical computers.

Google’s quantum supreme cryostat with Sycamore inside

A computation that would take 10,000 years on a classical supercomputer took 200 seconds on our quantum computer,” said Brooks Foxen, a graduate student researcher in the Martinis Group. “It is likely that the classical simulation time, currently estimated at 10,000 years, will be reduced by improved classical hardware and algorithms, but, since we are currently 1.5 billion times faster, we feel comfortable laying claim to this achievement.

The feat is outlined in a paper in the journal Nature.

The milestone comes after roughly two decades of quantum computing research conducted by Martinis and his group, from the development of a single superconducting qubit to systems including architectures of 72 and, with Sycamore, 54 qubits (one didn’t perform) that take advantage of the both awe-inspiring and bizarre properties of quantum mechanics.

The algorithm was chosen to emphasize the strengths of the quantum computer by leveraging the natural dynamics of the device,” said Ben Chiaro, another graduate student researcher in the Martinis Group. That is, the researchers wanted to test the computer’s ability to hold and rapidly manipulate a vast amount of complex, unstructured data.

We basically wanted to produce an entangled state involving all of our qubits as quickly as we can,” Foxen said, “and so we settled on a sequence of operations that produced a complicated superposition state that, when measured, returned output (“bitstring”) with a probability determined by the specific sequence of operations used to prepare that particular superposition.” The exercise, which was to verify that the circuit’s output correspond to the sequence used to prepare the state, sampled the quantum circuit a million times in just a few minutes, exploring all possibilities — before the system could lose its quantum coherence. “We performed a fixed set of operations that entangles 53 qubits into a complex superposition state,” Chiaro explained. “This superposition state encodes the probability distribution. For the quantum computer, preparing this superposition state is accomplished by applying a sequence of tens of control pulses to each qubit in a matter of microseconds. We can prepare and then sample from this distribution by measuring the qubits a million times in 200 seconds.” “For classical computers, it is much more difficult to compute the outcome of these operations because it requires computing the probability of being in any one of the 2^53 possible states, where the 53 comes from the number of qubits — the exponential scaling is why people are interested in quantum computing to begin with,” Foxen said. “This is done by matrix multiplication, which is expensive for classical computers as the matrices become large.”

According to the new paper, the researchers used a method called cross-entropy benchmarking to compare the quantum circuit’s bitstring to its “corresponding ideal probability computed via simulation on a classical computer” to ascertain that the quantum computer was working correctly. “We made a lot of design choices in the development of our processor that are really advantageous,” said Chiaro. Among these advantages, he said, are the ability to experimentally tune the parameters of the individual qubits as well as their interactions.


Quantum Computer Can See 16 Different Futures Simultaneously

When Mile Gu boots up his new computer, he can see the future. At least, 16 possible versions of it — all at the same time. Gu, an assistant professor of physics at Nanyang Technological University in Singapore, works in quantum computing. This branch of science uses the weird laws that govern the universe’s smallest particles to help computers calculate more efficiently.

Tiny particles of light can travel in a superposition of many different states at the same time. Researchers used this quantum quirk to design a prototype computer that can predict 16 different futures at once.

Unlike classical computers, which store information as bits (binary digits of either 0 or 1), quantum computers code information into quantum bits, or qubits. These subatomic particles, thanks to the weird laws of quantum mechanics, can exist in a superposition of two different states at the same time.

Just as Schrödinger‘s hypothetical cat was simultaneously dead and alive until someone opened the box, a qubit in a superposition can equal both 0 and 1 until it’s measured. Storing multiple different outcomes into a single qubit could save a ton of memory compared to traditional computers, especially when it comes to making complicated predictions.

In a study published April 9 in the journal Nature Communications, Gu and his colleagues demonstrated this idea using a new quantum simulator that can predict the outcomes of 16 different futures (the equivalent of, say, flipping a coin four times in a row) in a quantum superposition. These possible futures were encoded in a single photon (a quantum particle of light) which moved down multiple paths simultaneously while passing through several sensors. Then, the researchers went one step further, firing two photons side-by-side and tracking how each photon’s potential futures diverged under slightly different conditions.

It’s sort of like Doctor Strange in the ‘Avengers: Infinity War‘” movie, Gu told Live Science. Before a climactic battle in that film, the clairvoyant doctor looks forward in time to see 14 million different futures, hoping to find the one where the heroes defeat the big baddie. “He does a combined computation of all these possibilities to say, ‘OK, if I changed my decision in this small way, how much will the future change?’ This is the direction our simulation is moving forwards to.