The technical realities of functional quantum computers - is Google’s ten-year plan for Quantum Computing viable?

Profile picture for user Neil Raden By Neil Raden June 12, 2020
Google's quantum computing exploits have garnered plenty of attention in recent months. But how might they apply this commercially? A contrasting technical view comes by way of Itamar Sivan, CEO of Quantum Machines.


In March, I explored the enterprise readiness of quantum computing in Quantum computing is right around the corner, but cooling is a problem. What are the options?  I also detailed potential industry use cases, from supply chain to banking and finance. But what are the industry giants pursuing?

Recently, I listened to two somewhat different perspectives on quantum computing. One is Google’s (public) ten-year plan.

Google plans to search for commercially viable applications in the short term, but they don’t think there will be many for another ten years - a time frame I've heard one referred to as “bound but loose.” What that meant was, no more than ten, maybe sooner. In the industry, the term for the current state of the art is NISQ – Noisy, Interim Scale Quantum Computing.

The largest quantum computers are in the 50-70 qubit range, and Google feels NISQ has a ceiling of maybe two hundred. The "noisy" part of NISQ is because the qubits need to interact and be nearby. That generates noise. The more qubits, the more noise, and the more challenging it is to control the noise.

But Google suggests the real unsolved problems in fields like optimization, materials science, chemistry, drug discovery, finance, and electronics will take machines with thousands of qubits and even envision one million on a planar array etched in aluminum. Major problems need solving such noise elimination, coherence, and lifetime (a qubit holds its position in a tiny time slice).

In the meantime, Google is seeking customers to work with them to find applications working with Google researchers. Quantum computing needs algorithms as much as it needs qubits. It requires customers with a strong in-house science team and a commitment of three years. Whatever is discovered will be published as open source.

In summary, Google does not see commercial value in NISQ. They are using NISQ to discover what quantum computing can do that has any commercial capability.

A different point of view: Dr, Itamar Sivan, co-founder of Quantum Machines

First of all, if you have a picture in your mind of a quantum computer, chances are you are not including an essential element – a conventional computer. According to Quantum Computing, Progress, and Prospects: 

Although reports in the popular press tend to focus on the development of qubits and the number of qubits in the current prototypical quantum computing chip, any quantum computer requires an integrated hardware approach using significant conventional hardware to enable qubits to be controlled, programmed, and read out.

The author is undoubtedly correct. Most material about quantum computers never mentions this, and it raises quite a few issues that can potentially dilute the gee-whiz aspect. I'd heard this first from Itamar Sivan, Ph.D., CEO, Quantum Machines. He followed with the quip that technically, quantum computers aren't computers. It’s that simple. They are not Turing Machines. File this under the category of "You're Not Too Old to Learn Something New.”  

From (Hindi) Theory of Computation - Turing Machine:

A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm's logic can be constructed.

Dr. Sivan clarified this as follows:

Any computer to ever be used, from the early-days computers, to massive HPCs, are all “Turing-machines”, and are therefore equivalent to one another. All computers developed and manufactured in the last decades, are all merely “bigger” and more “compact” variations of one another. A quantum computer however is not MERELY a more advanced Turing machine, it is a different type of machine, and classical Turing machines are not equivalent to quantum computers as they are equivalent to one another.

Therefore, the complexity of running particular algorithms on quantum computers is different from the complexity of running them on classical machines. Just to make it clear, a quantum computer can be degenerated to behave like a classical computer, but NOT vice-versa.

There is a lot more to this concept, but most computers you've ever seen or heard of are Turing Machines, except Quantum computers. This should come as no surprise because anything about quantum mechanics is weird and counter-intuitive, so why would a quantum computer be any different?

According to Sivan, a quantum computer needs three elements to perform: a quantum computer and an orchestration platform of (conventional) hardware and software. There is no software in a quantum computer. The platform manages the progress of their algorithm through, mostly laser beams pulses. The logic needed to operate the quantum computer resides with and is controlled by the orchestration platform. 

The crucial difference in Google's and Quantum Machines' strategy is that Google views the current NISQ state of affairs as a testbed for finding algorithms and applications for future development. At the same time, Sivan and his company produced an orchestration platform to put the current technology in play. Their platform is quantum computer agnostic – it can operate with any of them. Sivan feels that focusing solely on the number of qubits is just part of the equation. According to Dr. Sivan:

While today's most advanced quantum computers only have a relatively small number of available qubits (53 for IBM's latest generation and 54 for Google's Sycamore processor), we cannot maximize the potential of even this relatively small count. We are leaving a lot on the table with regards to what we can already accomplish with the computing power we already have. While we should continue to scale up the number of qubits, we also need to focus on maximizing what we already have.

I’ve asked a few quantum computer scientists if quantum computers can solve the Halting Problem. In Wikipedia:

The halting problem is determining, from a description of an arbitrary computer program and an input, whether the program will finish running, or continue to run forever. Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs could not exist.  

That puts it in a class of problems that are “undecidable.” Oddly, opinion was split on the question, despite Turing’s Proof. Like Simplico said to Galileo in Dialogues Concerning Two New Sciences, “If Aristotle had not said otherwise I would have believed it.”

There are so many undecidable problems in math that I wondered if some of these might fall out. For example, straight from current AI problems, Planning in a Partially observable Markov decision process is considered undecidable. A million qubits? Maybe not. After all, Dr. Sivan pointed out that to replicate in a classical processor, the information in just a 300 qubit quantum processor would require more transistors than all of the atoms in the universe.

My take

I've always believed that action speaks louder than words. While Google is taking the long view, Quantum Machines provides the platform to see how far we can go with current technology. Google’s tactics are familiar. Every time you use TensorFlow, it gets better. Every time play with their autonomous car, it gets better. Their collaboration with a dozen or so technically advanced companies makes their quantum technology better.