Like, if only those bozos would do it the way that you think, then the world would be fine. There was a little bit of that.” Rose started D-Wave in 1999 with a $4,000 check from his entrepreneurship professor.

For its first five years, the company existed as a think tank focused on research. Draper Fisher Jurvetson got onboard in 2003, viewing the business as a very sexy but very long shot. “I would put it in the same bucket as SpaceX and Tesla Motors,” Jurvetson says, “where even the CEO Elon Musk will tell you that failure was the most likely outcome.” By then Rose was ready to go from thinking about quantum computers to trying to build them–“we switched from a patent, IP, science aggregator to an engineering company,” he says.

Is the set of Boolean values $x$ an interpretation (an assignment of values to variables) that satisfies the given Boolean formula?

For a practical implementation of Grover’s algorithm to solve mathematical problems take a look at the Grover’s Search Jupyter notebook in the sample gallery of your Azure Quantum workspace (an Azure account is required), or see this tutorial to implement Grover’s search algorithm.

For more quantum algorithm samples, see the code samples.

### Quantum machine learning

Machine learning on classical computers is revolutionizing the world of science and business.

First, qubits lose their properties, typically within 100 microseconds, due to noise, according to IBM.

That’s why qubits must operate in extremely cold environments. “Qubits are extremely sensitive to their environment,” FormFactor’s Leong said. “Quieting down the qubit environment in a very cold or cryogenic environment is critical.”

In addition, noise causes errors in the qubits. So quantum computers require error correction. On top of that, the industry needs to scale up quantum computers with thousands of qubits.

It’s nowhere close to that figure.

All told, quantum computing requires some breakthroughs. “We need to make qubits better than we’re making them today. And that’s across the field,” Intel’s Clarke said. “To me, the biggest challenge is how you wire them up.

Every qubit requires its own wire and its control box. That works well when you have 50 or 60 qubits.

*There are many research computers around the globe that are approaching exascale computing (1,000 petaflops). We will have many exascale computers by the end of this decade.”*

Indeed, the industry requires more compute power to solve current and future problems in biotechnology, defense, materials science, medicine, physics, and weather prediction.

*“We need to compute more at the same price. *

The problems are getting harder. The problems we serve are getting bigger and harder on top of that,” Fujimura said.

While traditional computing will continue to progress, the industry is rushing to develop quantum computing.

That isn’t very hard: the factors are 1, 3, 7 and 21.) So Rose switched to a different approach called adiabatic quantum computing, which is if anything even weirder and harder to explain.

An adiabatic quantum computer works by means of a process called quantum annealing. Its heart is a network of qubits linked together by couplings.

You “program” the couplings with an algorithm that specifies certain interactions between the qubits–if this one is a 1, then that one has to be a 0, and so on. You put the qubits into a state of quantum superposition, in which they’re free to explore all those 2-to-the-whatever computational possibilities simultaneously, then you allow them to settle back into a classical state and become regular 1’s and 0’s again.

Unfortunately,” he says, “like all discourse on the Internet, it tends to be driven by a small number of people that are both vocal and not necessarily the most informed.” He’s content to let the products prove themselves, or not. “It’s fine,” he says. “It’s good. Science progresses by rocking the ship. Things like this are a necessary component of forward progress.”

Are D-Wave’s machines quantum computers? fortunately this is one of those scenarios where an answer will in fact become apparent at some point in the next five or so years, as D-Wave punches out a couple more generations of computers and better benchmarking techniques evolve and we either do see a significant quantum speedup or we don’t.

The company has a lot of ground to cover between now and then, not just in hardware but on the software side too.

IBM is next with 65, followed by Google with 53 qubits, Intel (49) and Rigetti (32), according to the Quantum Computing Report.

Qubit count isn’t the only factor. They also must have relatively long coherence times and gate fidelities.

“Qubits and quantum processors are the central part of quantum hardware,” IBM’s Chow said. “To build a quantum computer or a quantum computing system, we will need not only quantum hardware, but also control electronics, classical computing units, and software that runs quantum computing programs.”

On that front, IBM offers Qiskit, an open-source quantum software development kit.

The industry also will require systems with thousands of qubits, but vendors have a long way to go here. The results are still promising, however. In 2019, Google’s 53-qubit processor, called Sycamore, completed a calculation in 200 seconds.

Google claimed it would take a supercomputer about 10,000 years to finish the same task.

Then, in June of 2021, China’s USTC presented a paper on Zuchongzhi, a 66-qubit superconducting quantum processor. In a calculation, USTC utilized 56 qubits. It performed a task 2 to 3 times faster than Google’s 53-qubit processor.

If you’re trying to do planning and scheduling for how you navigate the Curiosity rover on Mars or how you schedule the activities of astronauts on the station, these are clearly problems where a quantum computer–a computer that can optimally solve optimization problems–would be useful,” says Rupak Biswas, deputy director of the Exploration Technology Directorate at NASA Ames. Google has been using its D-Wave to, among other things, write software that helps Google Glass tell the difference between when you’re blinking and when you’re winking.

Lockheed Martin turned out to have some optimization problems too.

It produces a colossal amount of computer code, all of which has to be verified and validated for all possible scenarios, lest your F-35 spontaneously decide to reboot itself in midair.

But this thing, with a picowatt and a microsecond, does the same thing. So it’s just doing something very specific, very fast, very efficiently.”

This is great if you have a really hard discrete combinatorial optimization problem to solve.

Not everybody does. But once you start looking for optimization problems, or at least problems that can be twisted around to look like optimization problems, you find them all over the place: in software design, tumor treatments, logistical planning, the stock market, airline schedules, the search for Earth-like planets in other solar systems, and in particular in machine learning.

Google and NASA, along with the Universities Space Research Association, jointly run something called the Quantum Artificial Intelligence Laboratory, or QuAIL, based at NASA Ames, which is the proud owner of a D-Wave Two.