Happy Nice Mall

Today’s most advanced quantum computers have dozens of decohering (or “noisy”) physical qubits. Building a quantum computer capable of cracking RSA codes from such components would require millions, if not billions, of qubits. Only tens of thousands of these were to be used for computation – so-called logical qubits; the rest will be required for bugfixing that compensates for the mismatch.

The qubit systems we have today are a tremendous scientific achievement, but they don’t get us any closer to having a quantum computer that can solve a problem everyone cares about. It’s like trying to make today’s best smartphones using vacuum tubes from the early 1900s. You can establish the principle that if you can put 100 tubes together and get 10 billion of them to work together somehow harmoniously, seamlessly, you can do any miracle. What’s missing, however, is the breakthrough of integrated circuits and CPUs that led to smartphones – it took 60 years of arduous engineering to move from the invention of transistors to the smartphone without new physics involved in the process.

Actually there are ideas and I played some role In developing theories for these ideas to bypass quantum error correction by using much more stable qubits, in an approach called topological quantum computing. Microsoft work on this approach. But developing topological quantum computing hardware has also proven to be a major challenge. It’s unclear whether comprehensive quantum error correction or topological quantum computing (or something else like a hybrid between the two) will be the ultimate winner.

Physicists are smart as we all know (disclosure: I’m a physicist), and some physicists are very good at coming up with acronyms that make sense. The great difficulty of getting out of decoherence led to the impressive NISQ acronym for the “noisy medium-sized quantum” computer – for the idea that small-noise collections of physical qubits could do something useful and better than a classical computer. I’m not sure what this object is: How loud is it? How many qubits? Why is this a computer? What valuable problems could such a NISQ machine solve?

A final lab experiment Using 20 noisy superconducting qubits, Google observed some predicted aspects of quantum dynamics (called “time crystals”). The experiment was an impressive showcase of electronic control techniques, but showed no computing advantages over conventional computers, which can easily simulate time crystals with a similar number of virtual qubits. He also didn’t explain anything about the fundamental physics of time crystals. Other NISQ victories are recent simulated experiments. random quantum circuitsagain a highly specialized mission with no commercial value.

Using NISQ is certainly an excellent new fundamental research idea – it could aid physics research in fundamental areas like quantum dynamics. But despite a constant hype of NISQ from various quantum computing initiatives, its commercialization potential is far from clear. I’ve seen vague claims about how NISQ can be used for rapid optimization or even AI training. I’m not an expert in optimization or artificial intelligence, but I asked the experts and they were equally baffled. I asked researchers involved in various initiatives how NISQ would optimize any difficult task involving real-world applications, and I interpreted their complex answers as saying it’s possible, mainly because we don’t fully understand how classical machine learning and AI actually work. NISQ could do this even faster. Maybe, but hope for the best, not this technology.

There are proposals to use small-scale quantum computers for drug design as a way to quickly calculate molecular structure; this is a surprising application given that quantum chemistry is only a small part of the whole process. Equally surprising are the claims that short-term quantum computers will help finance. No technical documentation convincingly demonstrates that, let alone NISQ machines, small quantum computers can lead to significant optimization in algorithmic trading or risk assessment or arbitrage or hedging or targeting and forecasting or asset trading or risk profile. But that hasn’t stopped a few investment banks from jumping into the quantum computing majority.

A true quantum computer would have applications unimaginable today, such as when the first transistor was made in 1947, no one could have predicted how this would eventually lead to smartphones and laptop computers. I am utterly hopeful and have great faith in quantum computing as a potentially disruptive technology, but I am very surprised to claim that it will start generating millions of dollars in profits for real companies selling services or products in the near future. How?

Quantum computing is indeed one of the most important developments not only in physics but in all science. But “entanglement” and “superposition” are not magic wands that we can shake up and expect to transform technology in the near future. Quantum mechanics is indeed strange and illogical, but that alone does not guarantee revenue and profit.

Ten years and earlier, I was often asked when I was thinking about when a real quantum computer would be built. (It is interesting that I no longer face this question as the quantum computing hype has convinced people that these systems already exist or are just around the corner). My definitive answer has always been I don’t know. It’s impossible to predict the future of technology – it happens when it does. We can try to make an analogy with the past. It took more than 60 years for the aviation industry to transition from the Wright brothers to jumbo jets that carry hundreds of passengers thousands of miles. The immediate question is where on this timeline the development of quantum computing, as it stands today, should be placed. With the Wright brothers in 1903? The first jet planes around 1940? Or maybe we’re still way back in the early 16th century with Leonardo da Vinci’s flying machine? I do not know. Neither will anyone else.

Sankar Das Sarma is the director of the company. Condensed Matter Theory Center University of Maryland at College Park.