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Today's Quantum Computers

What are the capabilities of today’s systems, and how far will we get in the near future? Noisy, Intermediate-Scale Quantum (NISQ) systems are building on today’s technology.

Defining a “Winning” Quantum Computer

The term quantum supremacy has been used to describe demonstration of a quantum computer that can solve problems that a classical computer can’t. Some people are concerned about the impression that “supremacy” gives, but the term seems to have caught on.

An obvious question to ask is, “What’s the smallest useful quantum computer?” Of course, to answer the question, we must have both a machine and a problem to solve. The earliest algorithm to capture widespread attention was Shor’s factoring algorithm, but we now know that executing it requires a lot of high-fidelity qubits, so that’s not it. Many researchers now believe that the first application to demonstrate quantum supremacy will be in quantum chemistry, machine learning, optimization problems, or certain physics problems that are difficult to solve.

It is worth asking the question in the opposite direction: simulating the behavior of a quantum computer using a classical computer is hard. What’s the largest quantum computer we can simulate? We have talked about the exponential growth in the size of our state vector with the number of qubits. This can serve as a rough guide to what is possible:

number of qubits memory needed reading
\(10\) \(16\times 2^{10} \approx 16\times 10^{3}\) 16 kilobytes
\(20\) \(16\times 2^{20} \approx 17\times 10^{6}\) 16 megabytes
\(30\) \(16\times 2^{30} \approx 17\times 10^{9}\) 16 gigabytes
\(40\) \(16\times 2^{40} \approx 18\times 10^{12}\) 16 terabytes
\(50\) \(16\times 2^{50} \approx 18\times 10^{15}\) 16 petabytes
\(60\) \(16\times 2^{60} \approx 18\times 10^{18}\) 16 exabytes

(If this looks familiar, it should remind you of the table we saw back in Step 2.07.)

From this kind of table, and experience with large-scale quantum simulations, researchers reasoned that classical computers would struggle to simulate quantum computers larger than fifty qubits.
Therefore, a quantum computer of fifty qubits or more should be able to do things that a classical computer can’t.

Moreover, recent advances in simulation techniques have allowed researchers at IBM to fully simulate 49 qubits for limited types of circuits (algorithms), and to partially simulate 56 qubits with similar restrictions. The best a classical computer, then, is still a moving target, though we believe that real quantum computers will exceed this size soon.

NISQ and Today’s Bleeding-Edge Machines

Unfortunately, it’s not quite that simple. We have discussed the need for quantum error correction (QEC), but existing quantum devices are far too noisy to use QEC effectively. (Soon we will get into the details of QEC.) This leaves researchers with the question of how to best utilize noisy quantum computers. Professor John Preskill of Caltech coined the term noisy, intermediate-scale quantum (NISQ) to describe the era we are entering: with tens to low hundreds of qubits available in devices that can perform tens to hundreds of gates before noise completely destroys the state, what can we accomplish? Preskill lays out the argument beautifully, and we encourage you to read his paper.

As of March 2018, the following machines (in no particular order), which we might call the first-generation NISQ machines, are known:

  • IBM has three quantum computers with 5, 5, and 16 qubits accessible via the web and publicly available, and a 20-qubit device available to members of the IBM Q Network. (Keio University hosts the only hub in Asia, and provides access to our students and researchers from companies that are members of the hub.) A 50-qubit device, as of this writing, is under experimental testing, with no publicly announced results. All of these are transmon (superconducting) systems.
  • Google published results on a 9-qubit transmon chip, and is now testing a 72-qubit transmon chip named Bristlecone.
  • Intel, working with the Technical University of Delft, has created a 17-qubit transmon chip (under testing in Delft as of October 2017) and a 49-qubit transmon chip (announced in January 2018).
  • Rigetti released data on their chip with 19 transmon qubits (December 2017).
  • The research group of Misha Lukin (Harvard) has published a paper describing experiments with 51 atoms using cold, trapped atoms (similar to but somewhat different from ion traps), and
  • the research group of Chris Monroe (Maryland) published, in the same issue of Nature, a paper on a 53-qubit ion trap experiment. Both of these experiments demonstrated behavior that is difficult to model classically, and so sit right on the edge of representing quantum supremacy.

We realize that this description is little more than a list, and that it is incomplete and will be out of date quickly, but it seemed important to give learners a sense of the dynamic nature of the race toward useful, desirable quantum computers.

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This article is from the free online course:

Understanding Quantum Computers

Keio University