(Image by Garik Barseghyan from Pixabay )
The quantum tipping point that fabled moment when quantum technologies break through to commercial adoption at scale has beenquestioned in a previous diginomica report.
According to speakers at Davos this year, a more likely scenario is the gradual emergence of vertical use cases in which a quantum advantage finding hidden correlations between data points, for example will present itself. Plus, algorithms that allow the querying of massive data sets more quickly than classical models.
Even so, investors of every size are beginning to back quantum technologies, especially those that relate to enterprise data. And they are doing so despite the lack of strategic, long-term thinking among potential customers.
According to McKinsey, quantum investment exceeded $1.4 billion in 2021, while the UK government launched a 10-year, 2.5 billion ($3.2 billion) funding drive in March.
One of the challenges, says David Magerman, a founding partner at New York City-based investment house Differential Ventures, is that in a world of AI hype and a rush to associate with popular technologies like GPT, some enterprise users prefer a quick hit of tactical advantage to a long bet with an uncertain payback date.
A sugar rush trumps healthy eating for the future, perhaps? Magerman says:
I come from the financial industry. And while there are a lot of problems that quantum computing will solve significantly better [than classical systems], the financial industry is very much about the next five minutes.
All the spending that goes on research in finance is about solving problems today. There's such a competitive environment that they can't afford to be too forward-looking. We can't get the financial industry to devote many resources to quantum until production solutions are imminent. Then theyll spend a lot of money playing catch-up.
But every dollar they spend on forward-looking research gives their competitors a chance to beat them today.
A frustrating problem for tech innovators who need financial backing. One that applies across many sectors, though in areas such as cybersecurity and communications, where quantum-safe technologies are vital, and industries such as materials science and pharmaceuticals, early investment is a more immediate priority.
Yet being first out of the (quantum) gate is what drives Differential Ventures, a seed fund that backs promising early-stage companies in quantum, plus AI, machine learning, and data science. That said, seed-investment in the US can mean writing cheques for a few million dollars, as well as smaller, less risky sums.
Hotspots include bridging the gap between quantum technologies and classical environments, says Magerman. For example, his company recently led a $6.1 million seed extension round in Agnostiq, the Toronto-based distributed computing start-up that is building Covalent, an enterprise quantum and high-performance computing platform.
His company is also backing more personal artificial intelligence (AIs that query personally identifiable data, or PID), and their necessary flipside: private AIs, which keep PID safe from acquisitive AIs online. That certainly keeps the options open!
He explains the Differential philosophy:
A lot of people promote fantastical solutions that are oversold and aren't realistic. We'll avoid those companies. And those that are pitching things that aren't that hard, even if they're interesting applications. Once those ideas hit the mainstream, they are hard to protect from the competition, so we avoid those investments too.
Really, its based on my experience as a data scientist.
So, how common is demand for seed funding in quantum computing, an area often based in research labs and university spinouts, where high-risk, long-term, big-ticket VC backing would seem to be the order of the day? Massive funds that can afford to have a speculative under-performer in their portfolio when the other wins might be massive?
He says:
We typically avoid hardware companies, because they tend to require much more capital. And not only to start out: a quantum hardware company might raise a few million dollars in their first investment round, but they're going to require probably hundreds of millions to get to product.
But there are a lot of quantum software companies that typically want the seed rounds we look to invest in. They can get to product and get to revenue at scale, which means we can continue to invest in them before they get too big for us.
For example, Agnostiq recognized early on that the big problem impeding the growth of quantum research was orchestration tools that could integrate classical computing workloads with quantum computing resources. So, they built a product that allows companies to do high-performance computing orchestration.
So, does Magerman believe that a tipping point is approaching, or is it more a case of softly, softly? Because on the face of it, a $42 million seed fund (at present) could not afford to wait too long for commercial payback in such an uncertain space?
He explains:
I wouldn't say it's fast approaching, but I would say it's inevitable. And I think it will approach more quickly based on more intellectual and financial resources being devoted to solving the problem.
The subtext is the limits of Moores Law being reached, at which point quantum may offer a promising way forward: a new upward slope of increasing speed and power, assuming the challenge of noise in quantum circuits can be, if not solved, then at least made workable at scale.
He says:
The end of Moore's Law, plus the supply-chain issues that we had with silicon during COVID are among the factors contributing to the increased attention on quantum, accelerating the solutions to problems that prevent it from scaling.
For so long there has been this belief that, if you can continue to scale and get more power out of classical computing chips by engineering them with more densely packed circuitry, making them faster, and simply building more of them there is less pressure to develop alternative architectures and hardware.
But now we're seeing that we're becoming more limited in computing power [in the near future] yet we have more and more data, and more and more computing needs, especially with AI there'll be a lot more pressure to accelerate the development of quantum hardware.
There are still unsolved problems it is still too error prone and difficult to scale but these are practical problems, not theoretical ones. I wouldn't bet against the ingenuity of humans, especially when there's economic forces pushing for a solution!
So, the voices saying a tipping point wont happen were wrong?
He says:
I think the tipping point is more about coming up with the killer algorithm that is currently intractable. One that would take, effectively, infinite computing time in a classical environment, but which quantum computing could solve in a reasonable amount of time.
So, the two questions are: one, can you actually implement these algorithms in a quantum computing environment? Can you solve that problem? And, two, does the precise solution to the optimization perform substantially better, in practical problems, then the heuristic approximation?
Because it could be that the heuristic approximation is good enough and that getting the exact mathematical solution doesn't give you meaningfully better real-world performance.
So, the value of quantum will be finding the real-world problem where the heuristic solutions we are using today are inadequate and the quantum solution is substantially better.
So, what should enterprise decision-makers do in this push me, pull you environment of conflicting messages, not to mention the tactical pressure to innovate now, rather than place longer-term bets?
He says:
Theres a big education process that needs to happen, and that's going to need both a bottom-up and a top-down approach.
First, you have to get quantum researchers inside these corporate research groups, and give them the opportunity to experiment with solutions and promote those up the food chain to senior management.
And the top-down is getting management to make the hiring decisions, to bring in people who understand quantum technology well enough to do good research and prepare the company for quantum at scale.
Wise words from a brave investor who is prepared to be there in the early days of new ventures, even as the big guns of IBM, Google, Microsoft, et al, gear up in the background.
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Quantum commercialization softly, softly towards the inevitable future, says backer - Diginomica
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