[ExI] Why Google’s Quantum Supremacy Milestone Matters

[John Clark]

Quantum Computer expert Scott Aaronson wrote a editorial in the October 30
2019 New York Times:

Why Google’s Quantum Supremacy Milestone Matters
<https://www.nytimes.com/2019/10/30/opinion/google-quantum-computer-sycamore.html>


*   Why Google’s Quantum Supremacy Milestone Matters*
*     By Scott Aaronson*

Google officially announced last week in the journal Nature that it
achieved the milestone of “quantum supremacy.” This phrase, coined by the
physicist John Preskill in 2012, refers to the first use of a quantum
computer to make a calculation much faster than we know how to do it with
even the fastest supercomputers available. The calculation doesn’t need to
be useful: much like the Wright Flyer in 1903, or Enrico Fermi’s nuclear
chain reaction in 1942, it only needs to prove a point.

Over the last decade, together with students and colleagues, I helped
develop much of the theoretical underpinning for quantum supremacy
experiments like Google’s. I reviewed Google’s paper before it was
published. So the least I can do is to try to explain what it means.

Until recently, every computer on the planet — from a 1960s mainframe to
your iPhone, and even inventions as superficially exotic as “neuromorphic
computers” and DNA computers — has operated on the same rules. These were
rules that Charles Babbage understood in the 1830s and that Alan Turing
codified in the 1930s. Through the course of the computer revolution, all
that has changed at the lowest level are the numbers: speed, amount of RAM
and hard disk, number of parallel processors.

But quantum computing is different. It’s the first computing paradigm since
Turing that’s expected to change the fundamental scaling behavior of
algorithms, making certain tasks feasible that had previously been
exponentially hard. Of these, the most famous examples are simulating
quantum physics and chemistry, and breaking much of the encryption that
currently secures the internet.

In my view, the Google demonstration was a critical milestone on the way to
this vision. At a lab in Santa Barbara, Calif., a Google team led by John
Martinis built a microchip called “Sycamore,” which uses 53 loops of wire
around which current can flow at two different energies, representing a 0
or a 1. The chip is placed into a dilution refrigerator the size of a
closet, which cools the wires to a hundredth of a degree above absolute
zero, causing them to superconduct. For a moment — a few tens of millionths
of a second — this makes the energy levels behave as quantum bits or
“qubits,” entities that can be in so-called superpositions of the 0 and 1
states.

This is the part that’s famously hard to explain. Many writers fall back on
boilerplate that makes physicists howl in agony: “imagine a qubit as just a
bit that can be both 0 and 1 at the same time, exploring both possibilities
simultaneously.” If I had room for the honest version, I’d tell you all
about amplitudes, the central concept of quantum mechanics since Werner
Heisenberg, Erwin Schrödinger and others discovered it in the 1920s.

Here’s a short version: In everyday life, the probability of an event can
range only from 0 percent to 100 percent (there’s a reason you never hear
about a negative 30 percent chance of rain). But the building blocks of the
world, like electrons and photons, obey different, alien rules of
probability, involving numbers — the amplitudes — that can be positive,
negative, or even complex (involving the square root of -1). Furthermore,
if an event — say, a photon hitting a certain spot on a screen — could
happen one way with positive amplitude and another way with negative
amplitude, the two possibilities can cancel, so that the total amplitude is
zero and the event never happens at all. This is “quantum interference,”
and is behind everything else you’ve ever heard about the weirdness of the
quantum world.

Now, a qubit is just a bit that has some amplitude for being 0 and some
other amplitude for being 1. If you look at the qubit, you force it to
decide, randomly, whether to “collapse” to 0 or 1. But if you don’t look,
the two amplitudes can undergo interference — producing effects that depend
on both amplitudes, and that you can’t explain by the qubit’s having been 0
or by its having been 1.

Crucially, if you have, say, a thousand qubits, and they can interact (to
form so-called “entangled” states), the rules of quantum mechanics are
unequivocal that you need an amplitude for every possible configuration of
all thousand bits. That’s 2 to the 1,000 amplitudes, much more than the
number of atoms in the observable universe. If you have a mere 53 qubits,
as in Google’s Sycamore chip, that’s still 2 to the 53 amplitudes, or about
9 quadrillion.

The goal, with Google’s quantum supremacy experiment, was to perform a
contrived calculation involving 53 qubits that computer scientists could be
as confident as possible really would take something like 9 quadrillion
steps to simulate with a conventional computer. The qubits in Sycamore are
laid out in a roughly rectangular grid, with each qubit able to interact
with its neighbors. Control signals, sent by wire from classical computers
outside the dilution refrigerator, tell each qubit how to behave, including
which of its neighbors to interact with and when.

In other words, the device is fully programmable — that’s why it’s called a
“computer.” At the end, the qubits are all measured, yielding a random
string of 53 bits. Whatever sequence of interactions was used to produce
that string — in the case of Google’s experiment, the interactions were
simply picked at random — you can then rerun the exact same sequence again,
to sample another random 53-bit string in exactly the same way, and so on
as often as desired.

In its Nature paper, Google estimated that its sampling calculation — the
one that takes 3 minutes and 20 seconds on Sycamore — would take 10,000
years for 100,000 conventional computers, running the fastest algorithms
currently known. Indeed the task was so hard, Google said, that even
directly verifying the full range of the results on classical computers was
out of reach for its team. Thus, to check the quantum computer’s work in
the hardest cases, Google relied on plausible extrapolations from easier
cases.

IBM, which has built its own 53-qubit processor, posted a rebuttal. The
company estimated that it could simulate Google’s device in a mere 2.5
days, a millionfold improvement over Google’s 10,000 years. To do so, it
said, it would only need to commandeer the Oak Ridge Summit, the largest
supercomputer that currently exists on earth — which IBM installed last
year at Oak Ridge National Laboratory, filling an area the size of two
basketball courts. (And which Google used for some of its simulations in
verifying the Sycamore results.) Using this supercomputer’s eye-popping 250
petabytes of hard disk space, IBM says it could explicitly write down all 9
quadrillion of the amplitudes. Tellingly, not even IBM thinks the
simulation would be especially easy — nor, as of this writing, has IBM
actually carried it out. (The Oak Ridge supercomputer isn’t just sitting
around waiting for jobs.)

We’re now in an era where, with heroic effort, the biggest supercomputers
on earth can still maybe, almost simulate quantum computers doing their
thing. But the very fact that the race is close today suggests that it
won’t remain close for long. If Google’s chip had used 60 qubits rather
than 53, then simulating its results with IBM’s approach would require 30
Oak Ridge supercomputers. With 70 qubits, it would require enough
supercomputers to fill a city. And so on.

Is there real science behind the spectacle of these two tech titans locking
antlers? Is “quantum supremacy,” divorced from practical applications, an
important milestone at all? When should we expect those practical
applications, anyway? Assuming Google has achieved quantum supremacy, what
exactly has it proved — and is it something anyone doubted in the first
place?

Let’s start with applications. A protocol that I came up with a couple
years ago uses a sampling process, just like in Google’s quantum supremacy
experiment, to generate random bits. While by itself that’s unimpressive,
the key is that these bits can be demonstrated to be random even to a
faraway skeptic, by using the telltale biases that come from quantum
interference. Trusted random bits are needed for various cryptographic
applications, such as proof-of-stake cryptocurrencies (environmentally
friendlier alternatives to Bitcoin). Google is now working toward
demonstrating my protocol; it bought the non-exclusive intellectual
property rights last year.

Other applications will almost certainly require more qubits, and of a
higher quality — things that Google, IBM and the other players are racing
to build. One major milestone to watch for next will be the first use of
small quantum computers to simulate the quantum physics of chemicals and
materials in a way that’s actually useful to chemists and materials
scientists. Simulating quantum mechanics — that is, overcoming the
exponential explosion of amplitudes in nature via a computer equipped with
the same power — was the original application that the physicist Richard
Feynman envisioned when he proposed the idea of a quantum computer in the
early 1980s. It’s still the most important application we know — one that
could aid in the design of everything from batteries and solar cells to
fertilizers and lifesaving drugs.

An even bigger milestone will be the first practical demonstration of
quantum error correction — a technology that, in theory, should be able to
keep qubits alive for vastly longer amounts of time by cleverly encoding
them across many physical qubits. Quantum computing researchers think that
quantum error correction is what will ultimately let quantum computers
scale beyond a couple hundred qubits, to the million- or billion-qubit
machines that would fully realize Feynman’s dream. But this hasn’t been
demonstrated yet, and no one knows when it will be.

In the meantime, Google’s demonstration is a crucial proof of concept.
Building a quantum computer is so hard that, ever since serious efforts
began in the mid-1990s, some distinguished scientists have argued that the
task would be impossible. Qubits, they said, will always prove too fragile
to control. If quantum mechanics seems to predict that you can harness an
exponential number of amplitudes for computation, then so much the worse
for our present understanding of quantum mechanics.

Google’s demonstration should give these skeptics pause. To all
appearances, a 53-qubit device really was able to harness 9 quadrillion
amplitudes for computation, surpassing (albeit for a special, useless task)
all the supercomputers on earth. Quantum mechanics worked: an outcome
that’s at once expected and mind-boggling, conservative and radical.

The computer revolution was enabled, in large part, by a single invention:
the transistor. Before transistors, we were stuck with failure-prone vacuum
tubes. Yet vacuum tubes kind of, sort of worked: they translated abstract
Boolean logic into electrical signals reliably enough to be useful. We
don’t yet have the quantum computing version of the transistor — that would
be quantum error correction. Getting there will surely require immense
engineering, and probably further insights as well. In the meantime,
though, the significance of Google’s quantum supremacy demonstration is
this: after a quarter century of effort, we are now, finally, in the early
vacuum tube era of quantum computing.

*Scott Aaronson is David J. Bruton Centennial Professor of Computer Science
at the University of Texas at Austin, and the founding director of UT’s
Quantum Information Center. He’s the author of “Quantum Computing Since
Democritus,” and blogs about quantum computing and other topics at
Shtetl-Optimized.*
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