Tag: Emerging-Technologies

Posted on

AI’s role is poised to change monumentally in 2022 and beyond

Shashank Srivastava Contributor
Shashank Srivastava is an experienced product leader who specializes in driving vision and roadmap for businesses.

The latest developments in technology make it clear that we are on the precipice of a monumental shift in how artificial intelligence (AI) is employed in our lives and businesses.

First, let me address the misconception that AI is synonymous with algorithms and automation. This misconception exists because of marketing. Think about it: When was the last time you previewed a new SaaS or tech product that wasn’t “fueled by” AI? This term is becoming something like “all-natural” on food packaging: ever-present and practically meaningless.

Real AI, however, is foundational to supporting the future of how businesses and individuals function in the world, and a huge advance in AI frameworks is accelerating progress.

As a product manager in the deep learning space, I know that current commercial and business uses of AI don’t come close to representing its full or future potential. In fact, I contend that we’ve only scratched the surface.

Ambient computing

The next generation of AI products will extend the applications for ambient computing.

  • Ambient = in your environment.
  • Computing = computational processes.

We’ve all grown accustomed to asking Siri for directions or having Alexa manage our calendar notifications, and these systems can also be used to automate tasks or settings. That is probably the most accessible illustration of a form of ambient computing.

Ambient computing involves a device performing tasks without direct commands — hence the “ambient,” or the concept of it being “in the background.” In ambient computing, the gap between human intelligence and artificial intelligence narrows considerably. Some of the technologies used to achieve this include motion tracking, wearables, speech-recognition software and gesture recognition. All of this serves to create an experience in which humans wish and machines execute.

The Internet of Things (IoT) has unlocked continuous connectivity and data transference, meaning devices and systems can communicate with each other. With a network of connected devices, it’s easy to envision a future in which human experiences are effortlessly supported by machines at every turn.

But ambient computing is not nearly as useful without AI, which provides the patterning, helping software “learn” the norms and trends well enough to anticipate our routines and accomplish tasks that support our daily lives.

On an individual level, this is interesting and makes life easier. But as professionals and entrepreneurs, it’s important to see the broader market realities of how ambient computing and AI will support future innovation.

Posted on

Classiq raises additional funding for its quantum algorithm design tools

Tel Aviv-based Classiq, a startup that wants to make it easier for developers to build quantum algorithms and applications, today announced that it has raised additional funding for its service by adding HSBC, NTT Finance, and Intesa Sanpaolo as new investors to its $33 million Series B round, which brings the round to $36 million and the company’s total funding to $51 million.

While this is not a huge extension round, it’s still worth an extra look because it shows how these new strategic investors in the financial services industry are placing early bets on quantum computing and Classiq’s ability to make building quantum software easier.

“In this round, it was important for us to bring in strategic money — money is important, funding is important and this is our way to scale — but also, in this market, strategic partners are important. The common thing for all of these strategic investors is that they see quantum computing as a key part of their IT strategy,” Classiq CEO Nir Minerbi told me.

The company now has about 40 employees and is looking to scale that to 80 soon. With both the quantum market in Europe and Japan growing quickly, Classiq is focusing its efforts on these geographies right now, in addition to the United States.

“We sell our platform mainly to enterprises and academia — but mainly enterprises. So what we care about is where the main markets of enterprises that will adopt quantum computing are. If I had to name two, it would be Japan and Germany. You see many Japanese enterprises like Toshiba, NTT, Hitachi and Mizuho — and so many others — opening quantum computing teams,” Minerbi explained.

Image Credits: Classiq

Another focus for Classiq is to scale the overall talent pool. Earlier this week, the company launched its Classiq Coding Competition, for example, which rewards those developers who can create the most efficient quantum circuits while using its tools. Minerbi also noted that the Classiq platform is now used at a number of universities, including Carnegie Mellon, to train the next generation of computer scientists.

It’s obviously still (very) early days for quantum computing, but as Minerbi noted, many enterprises are now starting to think about how they can, eventually, integrate quantum into their overall IT strategy. In addition, while verticals like finance, pharma and automotive have long been interested in quantum, a number of cybersecurity firms are also now starting to investigate how they could potentially use quantum computers (beyond the obvious focus on breaking existing encryption schemes).

“Quantum computing has the potential to overhaul how we operate areas of the bank, like option pricing and risk analysis, which would lead to greater efficiencies and customer service improvements,” said Steve Suarez, global head of innovation, global functions at HSBC. “We look forward to working with Classiq to explore this technology further.”

QuantWare will build you a custom 25-qubit quantum processor in 30 days

Classiq raises $10.5M Series A round for its quantum software development platform

Posted on

Baseten nabs $20M to make it easier to build machine learning-based applications

As the tech world inches a closer to the idea of artificial general intelligence, we’re seeing another interesting theme emerging in the ongoing democratization of AI: a wave of startups building tech to make AI technologies more accessible overall by a wider range of users and organizations.

Today, one of these, Baseten — which is building tech to make it easier to incorporate machine learning into a business’s operations, production and processes without a need for specialized engineering knowledge — is announcing $20 million in funding and the official launch of its tools.

These include a client API and a library of pretrained models to deploy models built in TensorFlow, PyTorch or scikit-learn; the ability to build APIs to power your own applications; and the ability the create custom UIs for your applications based on drag and drop components.

The company has been operating in a closed, private beta for about a year and has amassed an interesting group of customers so far, including both Stanford and the University of Sydney, Cockroach Labs, and Patreon, among others, who use it to, for example, help organizations with automated abuse detection (through content moderation) and fraud prevention.

The $20 million is being discussed publicly for the first time now to coincide with the commercial launch, and it’s in two tranches, with equally notable names among those backers.

The seed was co-led by Greylock and South Park Commons Fund, with participation also from the AI Fund, Caffeinated Capital and individuals including Greg Brockman, co-founder and CTO at general intelligence startup OpenAI; Dylan Field, co-founder and CEO of Figma; Mustafa Suleyman, co-founder of DeepMind; and DJ Patil, ex-Chief Scientist of the United States.

Greylock also led the Series A, with participation from South Park Commons, early Stripe exec Lachy Groom; Dev Ittycheria, CEO of MongoDB; Jay Simon, ex-president of Atlassian, now at Bond; Jean-Denis Greze, CTO of Plaid; and Cristina Cordova, another former Stripe exec.

Tuhin Srivastava, Baseten’s co-founder and CEO, said in an interview that the funding will be used in part to bring on more technical and product people, and to ramp up its marketing and business development.

The issue that Baseten has identified and is trying to solve is one that is critical in the evolution of AI: Machine learning tools are becoming ever more ubiquitous and utilized, thanks to cheaper computing power, better access to training models and a growing understanding of how and where they can be used. But one area where developers still need to make a major leap, and businesses still need to make big investments, is when it comes to actually adopting and integrating machine learning: there remains a wide body of technical knowledge that developers and data scientists need to actually integrate machine learning into their work.

“We were born out of the idea that machine learning will have a massive impact on the world, but it’s still difficult to extract value from machine learning models,” Srivastava said. Difficult, because developers and data scientists need to have specific knowledge of how to handle machine learning ops, as well as technical expertise to manage production at the back end and the front end, he said. “This is one reason why machine learning programs in businesses often actually have very little success: it takes too much effort to get them into production.”

This is something that Srivastava and his co-founders Amir Haghighat (CTO) and Philip Howes (Chief Scientist) experienced first-hand when they worked together at Gumroad. Haghighat, who was head of engineering, and Srivastava and Howes, who were data scientists, wanted to use machine learning at the payments company to help with fraud detection and content moderation, and realised that they needed to pick up a lot of extra full-stack engineering skills — or hire specialists — to build and integrate that machine learning along with all of the tooling needed to run it (eg notifications and integrating that data into other tools to action).

They built the systems — still in use, and screening “hundreds of millions of dollars of transactions” — but also picked up an idea in the process: others surely were facing the same issues they did, so why not work on a set of tools to help all of them and take away some of that work?

Today, the main customers of Baseten — a reference to base ten blocks, often used to help younger students learn the basics of mathematics (“It humanizes the numbers system, and we wanted to make machine learning less abstract, too,” said the CEO) — are developers and data scientists who are potentially adopting other machine learning models, or even building their own, but lack the skills to practically incorporate them into their own production flows. There, Baseten is part of a bigger group of companies that appear to be emerging building “MLops” solutions — full sets of tools to make machine learning more accessible and usable by those working in devops and product. These include Databricks, Clear, Gathr and more. The idea here is to give tools to technical people to give them more power and more time to work on other tasks.

“Baseten gets the process of tool-building out of the way so we can focus on our key skills: modeling, measurement and problem solving,” said Nikhil Harithras, senior machine learning engineer at Patreon, in a statement. Patreon is using Baseten to help run an image classification system, used to find content that violates its community guidelines.

Over time, there a logical step that Baseten could make, continuing on its democratization trajectory: considering how to build tools for non-technical audiences, too — an interesting idea in light of the many no-code and low-code products that are being rolled out to give them more power to build their own data science applications, too.

“Non-technical audiences are not something we focus on today, but that is the evolution,” Srivastava said. “The highest level goal is to accelerate the impact of machine learning.”

Posted on

Alibaba-backed Deeproute further slashes L4 driving costs to $3,000

Only four months after Deeproute.ai announced it planned to sell its self-driving solution at an attractive $10,000, the Shenzhen and Fremont-based startup said it has further slashed the cost by approximately 70% to $3,000.

The touted price tag surely stands out, given Deeproute is promising two to five solid-state lidars, eight cameras, as well as Nvidia’s Drive Orin system-on-a-chip (SoC) for each of its Level 4 solutions, the stage of autonomous driving that does not require human intervention in most conditions.

It’s not news that Chinese lidar makers are striving to make the sensor technology more affordable. Xpeng, a Chinese electric vehicle upstart, said in 2021 that it would be adding lidar made by DJI-affiliate Livox to its mass-produced vehicles.

But even with each lidar unit being $500, which is already low by today’s standard (they could easily cost tens of thousands of dollars just a few years ago), the sum quickly adds up to $2,500 without counting the cameras and chips yet.

Deeproute, which is backed by Alibaba and Chinese carmaker Geely, declined to disclose the price for its “bulk purchase” of lidar and other components because “the suppliers want to keep it confidential.” It did share the breakdown, saying five lidar sensors cost about 50%, and the chip accounts for 30%.

Deeproute is working with two partners to achieve affordability. Robosense, a lidar maker also based in Shenzhen, supplies its main lidar and Z Vision, a Beijing-based company, supplies its blind-spot lidar.

The company’s low-cost L4 package, which is part of its Driver 2.0 autonomous driving system, will first be deployed in a robotaxi fleet comprising 30 SAIC Motor SUVs in Shenzhen in the coming months. The solution also uses 5G remote control and network safety redundancy for safety measures.

In a bid to further commercialize Driver 2.0, Deeproute said it plans to bring the solution into mass-produced, consumer-grade vehicles in 2024. It will be working with both Chinese and international automakers, which means the cars could be sold worldwide, with a goal to manufacture 100,00 such vehicles.

“We aspire to be the top facilitator of smart transportation, bringing high-performance, advanced autonomous driving capabilities to the market at an accessible price,” said Maxwell (Guang) Zhou, CEO of Deeproute.ai in a statement.

“The debut of this groundbreaking robotaxi fleet offers the industry a vivid sneak peek into the future of L4 robotaxis and what is possible for the consumer vehicle.”

Autonomous driving startup DeepRoute.ai prices L4 solution at $10,000

Posted on

Quantum Machines acquires QDevil to build out its full-stack quantum orchestration platform

Quantum Machines, the well-funded Israeli startup that specializes in building control systems for quantum computers, today announced that it has acquired QDevil, a well-known Danish company that specializes in building control hardware for quantum systems. The two companies did not disclose the financial details of the transaction and, according to Crunchbase, the company only raised about €1 million, mostly in the form of grants. But it has become a significant player in the market and counts many of the established quantum computing research institutes and commercial entities as its customers.

Quantum Machines founder and CEO Itamar Sivan told me he first met the QDevil team in person at the last in-person APS March meeting back in 2019 and the companies continued to talk over the course of the next few years. “At some point, we realized that it would be highly impactful to join forces, because their products are actually complementary to ours. And therefore, we can now provide a more comprehensive orchestration platform,” said Sivan. It’s one thing to build a quantum processing unit, after all (or buy one off the shelf), but it takes a lot of expertise to then turn that into a complete quantum computer.

Image Credits: Quantum Machines

One of QDevil’s main products is its QDAC, a “high-precision low-noise computer-controlled voltage generator,” as the company describes it. Qubits obviously hate nothing more than noise, so QDevil’s low-noise DAC makes it easier for operators to control their qubits. In addition, QDevil also offers a range of other electronics and specialized components for operating quantum processors. Combined with Quantum Machines’ Pulse Processing Units and software, this will allow the two companies to offer a full-stack solution for orchestrating quantum computers. Sivan also stressed that QDevil has done quite a bit of work on controlling quantum dots, which are an increasingly hot topic in the quantum computing world.

“QDevil is one of the premier providers of electronics for quantum computing,” said Dr. Jonatan Kutchinsky, CEO of QDevil. “We’re delighted to join up with Quantum Machines, a company whose mission and goals align so perfectly with our own. Together we will continue to further develop the quantum community in Denmark and deliver revolutionary technologies that will make it seamless for companies developing quantum computers to realize the potential of their QPUs.”

Sivan also noted that this acquisition brings a lot of new talent to Quantum Machines — and there is only a finite number of PhD physicists with a specialization in quantum mechanics on the market.

“It’s an amazing acquisition for us because it’s both the technology, the products, the customer base and the people,” Sivan said. “It’s really all of that. They have accomplished amazing achievements and I can firmly say now that [Quantum Machines] plus QDevil is selling to almost all the players in quantum computing globally — above 90% — including corporates, startups, national labs.”

Chances are, this isn’t Quantum Machines’ last acquisition. The company has now raised $73 million, so it has a bit of a war chest to acquire smaller companies and build out its platform. We’ll likely see the same play out across the market, given how many small, highly specialized companies there are right now, with a number of larger players trying to build full-stack platforms.

“I believe that yes, acquisitions are definitely going to be a strategy for Quantum Machines and, I believe, for other companies as well,” Sivan said. “Because as the value chain forms, I believe that you will see that eventually, there will be layers in the value chain that will be more or less significant.”

Posted on

Maybell Quantum’s Icebox is a small fridge for large quantum computers

Maybell Quantum, a Denver-based startup that plans to build hardware for the budding quantum computing ecosystem, is coming out of stealth today and launching Icebox. As the name implies, Icebox is a cryogenic platform to cool quantum processors down to the very low temperatures it takes to run a stable quantum system. Traditionally, these are extremely large systems but Maybell says its Icebox is able to support three times as many qubits in one-tenth the space of currently used setups.

“You always see this image of the beautiful golden chandelier. It’s a starkly stunning image, but what you don’t see is what’s associated with that golden chandelier: between two and hundred square feet of tubes and wires and pumps and compressors and liquid nitrogen dewars and noncontact cooling water and all these other things that you need in order to get the bottom of that golden chandelier down to millikelvin temperatures,” Maybell founder and CEO Corban Tillemann-Dick told me.

Image Credits: Maybell Quantum

In part, it’s able to do this because its fridge features 4,500 superconducting “Flexlines,” as the company calls its quantum wires which transmit far less heat and vibration (the archenemies of stable quantum systems) compared to traditional cabling. Tillemann-Dick noted that while the Icebox is all about refrigeration, the wiring is a critical piece of this solution. “Folks work hard to vibrationally isolate their qubits and they’ll put them on floating foundations,” he explained. “They’ll put them in separate rooms and they have all these copper braids and stuff. But then the semi-rigid coax cables that you use to communicate with your qubits – they’re like sticks. You hold them at one end and they stick out straight. That transmits the majority of the vibration that the qubits see in a big system.”

Image Credits: Maybell Quantum

Because of this innovation in the cabling, the Icebox is smaller but can also fit 4,500 of these superconducting wires that it takes to control a quantum processor.

Tillemann-Dick, who previously led Boston Consulting Group’s quantum practice, also noted that the team was able to design the unit from the ground up and in the process, it was able to bring a human-centric design philosophy to a business that traditionally never focused on making its machinery easy to use. That means it fridge has a door to access to the system, for example — and for those times when you have to do a fill wiring swap, for example, the Icebox essentially includes a built-in mini forklift that gives you access to everything. There’s even a small desk that folds out of the rack to help users get their work done.

It’s not the benches around Cray’s early supercomputers but it’s definitely a focus on the user experience that current quantum computer cooling systems lack.

“I realized this was a company that should exist when doing strategy work for quantum players at [Boston Consulting Group] and I said, ‘listen, I’m not going to make the difference at one of the cubit players. Plus, they felt like a lottery ticket to me. But when it comes down to improving the supply chain and applying human-centered design to problems, that I have a ton of experience with,'” Tillemann-Dick said when I asked him about how he got to focus on this specific niche in the quantum ecosystem. Together with Dr. Kyle Thompson, Maybell’s
CTO and co-founder who brought a lot of hands-on experience with cryogenic systems to the company, the team started working on their cooling solution and raised some seed funding.

Maybell says it has already received contracts from “DARPA, NSIC/DIU, and leading research universities,” all of which surely appreciate that the Icebox is basically a standard two-rack system instead of a room-scale machine.

“Labs like mine, at the cutting edge of quantum research, have a critical need for high quality, smaller footprint cryogenic systems. That’s what Maybell is building. It lets us do more research more quickly and accelerate our contributions to Quantum Sciences,” said Professor Javad Shabani of NYU’s Shabani Lab for Quantum Materials & Devices.

The quantum computing space is moving quickly these days. We’re now in what seems like a transition period where a handful of large well-funded players like D-Wave, IBM, Rigetti and IonQ are trying to control as much of the stack as possible to something that’s more akin to the modern classical computing landscape with lots of highly specialized players that all provides the parts that the system integrators can then assemble according to their — and their users’ — needs. That’s going to play out on every level of the ecosystem, from control hardware and software to the quantum processing units themselves and the fundamental technologies like, in this case, sub-Kelvin refrigeration.

Posted on

Autobrains nabs $19M, bringing its Series C to $120M, to take on Mobileye in autonomous driving tech

AI has been the backbone of many a technological breakthrough over the years, but one challenge it has yet to solve is that of self-driving: try as they may, engineers have yet to build a platform that can manage all the practicalities and unexpected eventualities of conducting a vehicle as well as or better than a human can do, and which has also convinced regulators and the general population of its reliability. We’re still seeing a lot of development, however, and today, Autobrains, one of the hopefuls in this space that believes it has figured out how to fix the 1% margin of error typical in self-driving with a “self-learning” approach that is hardware-agnostic (more on that below) is announcing yet more funding to continue developing its platform.

The Israeli startup has raised $19 million, rounding out its Series C at $120 million. The first tranche of this investment was made public in November 2021, and altogether the investor list includes Temasek, previous strategic backers Continental and BMW i Ventures, and new backers Knorr-Bremse AG and VinFast. As before, the company is not disclosing its valuation, but for some context, it’s a crowded space that provides some comparable numbers.

Israel’s Mobileye, which Autobrains’ CEO and founder Igal Rachelgauz describes as his company’s biggest competitor, earlier this month filed confidentially for an IPO (owner Intel would retain a stake in the spun out company should this go ahead). It’s been reported that Mobileye could be valued at around $50 billion if it lists. Wayve, another Israeli self-driving startup, raised $200 million in January, valued in the region of $1 billion.

Autobrains has to date raised just under $140 million, and it’s taking an approach that it believes will give it more traction in the market because of its flexibility.

A lot of self-driving technology (Mobileye’s being one example) is based around LIDAR sensors, with a few companies (like Wayve) building systems on lower cost bases using radar, smartphones, and AI to stitch the experience together. Autobrains takes a different approach that might be described as hardware-agnostic, using radar, and also LIDAR but only if the OEM has built it in.

The company’s approach comes from more than a decade of R&D. Originally, the startup descends from a company called Cortica AI (which Rachelgauz had founded), which has spent years building AI-based imaging technology applied across a wide variety of use cases (our first coverage of it, in fact, was about developing image recognition for advertising): Autobrains was spun out initially branded as “Cartica AI” to realize more of the value of the IP as it pertained to the very specific use case of driving. The company says it has more than 250 patents filed on its technology already.

One of the main barriers to self-driving AI has been the inability for machine learning systems to account for edge cases — with decision making based essentially on labelled data sets that have been fed into the algorithms. “It’s a very expensive process involving thousands of people, but still faces the challenge of accuracy because you can’t cover all the edge cases,” Rachelgauz said. So in one tragic example, while the operator in the Uber self-driving car pilot accident in Arizona was charged over the crash, the reason the car didn’t stop on its own was that it didn’t recognize the jay-walker.

As Rachelgauz describes it, Autobrains does not depend on labelled data, and has been built to “works closer to human way” of learning, by keeping the data randomized, letting the platform find the commonalities, and then going over the learnings to keep what is relevant to continue learning from (eg clothes that are the same color as the background) but disregard details that are not (eg, the shapes of clouds). What is kept then starts to form clusters of understanding that teach the self-driving platform to react more accurately to related scenarios. Pedestrians, for example, might have up to 100 different classes of behavior that are being developed on the Autobrains system.

Currently the platform is set up for two levels of self-driving. The first is to feed assisted systems aimed at improving human driver safety, which is scheduled to be rolled out commercially in 2023 adding on average $100 to the price of a car. The second is aimed at self driving at levels 4 and 5 and is “being worked on now” and will use whatever hardware has been built into vehicles to work. It’s projected to cost in the “few thousands of dollars” at the moment, and production should start on it in 2024, but with caveats that this could move depending on the market, its customers’ appetites to invest in this, the progress of technology, and of course what consumers realistically will want and use. (The two-level approach. focusing initially on scenarios involving AI-based driver assistance rather than autonomy, is one that other startups in the space are also taking: for example another self-learning startup called Annotell, which also recently raised funding.)

“I think it’s a process, not an immediate target,” Rachelgauz said of the fully-autonomous roadmap. “But if we can commit to 2024, [we can so so understanding] it will take time to see how we can we scale it safely. The way it will happen is the differentiating factor for us.”

“Autobrains’ technology holds the promise we have all been looking for to create the paradigm shift in the industry to self-learning AI, bridging the gap to fully autonomous driving,” said Thuy Linh Pham, Deputy CEO VinFast, in a statement. “Autobrains captured our attention by applying unsupervised AI software, as opposed to traditional software that is based on manually labeled data, to make self-driving vehicles adaptive to unprecedented behaviors in real-time. We expect that Autobrains will actualize this ambitious goal into a reality in the near future.”

Posted on

Alice&Bob, a quantum computing startup, raises $30M to launch its first fault tolerant ‘cat qubit’ computers in 2023

Quantum computing has been something of a holy grail in the world of technology: in theory, it promises an unprecedented amount of processing power that could be used to solve the most complicated problems; in practice, most attempts at building the physical manifestation of the concept produce too many errors as a byproduct to make quantum computers productive at scale. Now a startup out of France believes it has made a breakthrough to solve those issues, and it is announcing some funding to help it apply those advances to building machines it intends to launch next year.

Alice&Bob — Paris startup that is building what it says are fault-tolerant quantum processors — has raised €27 million (just under $30 million at today’s rates), money that it will be using to develop chips and computers, and a business model based around exponentially-powered quantum-computing-as-a-service, all for a 2023 launch. The Series A is being co-led by Elaia, Bpifrance (by way of its Digital Venture fund), and Supernova Invest, with participation also from Breega. (Elaia and Breega also backed Alice&Bob in a €3 million seed round in 2020, the year the startup was founded.)

The startup’s name is a reference to the two fictional characters that are often used as archetypes for hypothetical thought experiments in areas like cryptography and quantum physics. Similarly, the breakthrough building block of Alice&Bob’s system also has a theoretical reference in it. Rather than pursuing ways to reduce faults by throwing more qubits at the problem (qubits being the typical building block of a quantum computing system), Théau Peronnin, the CEO, tells me that Alice&Bob have devised a different architecture that it calls a “cat qubit”, a reference to Schrödinger’s Cat and the idea of something being in “two states” at once, as a way to reduce the faults in processing, thereby to reduce the number of processors needed to overcome those faults.

While most approaches to building quantum computer processors have been based around superconducting circuits — Peronnin notes that this is what IBM and Google have been working on — what his startup has devised “is another kind of chip architecture” that automatically corrects the errors.

Founded by Peronnin and Raphaël Lescanne as a spinout from French academia — of the 40 people who work there now, more than half have PhDs — Alice&Bob has continued to collaborate with French research labs to prove out its approach.

Recent work with one group led by quantum physics professor Zaki Leghtas showed that its cat qubits resists “bit flips” (the name for one of the two types of faults quantum systems usually encounter) for several minutes. That may not sound like much up time, but it’s nearly a 100,000x improvement on earlier quantum efforts.

Peronnin tells me that the startup’s next step — and the reason why it will be another year before we see any product announcements — is to work on using its cat qubit to reduce phase flips (the other type of fault). 

The work it has done so far has been extensive enough that Peronnin believes the startup has leaped ahead of many of the quantum efforts of other, much larger tech companies. Alice&Bob have filed patents on its hardware and software components, and while it does not have plans to license it to other companies before using it to build its own computers, he noted that this remains an option down the road, too. 

“At the moment, we’re focusing on building the whole machin because we first have to demonstrate that the technology can deliver on its promises,” he said. “But since we’re seeing some competitors, and part of our technology is patented, then the question [of licensing] will be open.” He noted as one example Amazon’s quantum roadmap, which mentioned the component that Alice&Bob designed 151 times on 118 pages. “I think we can say they are following us.”

The global quantum race has been precisely that up to now: although we’ve seen many announcements from larger tech companies of the work that they are doing, plus a wide swathe of startups coming out of academia also looking to commercialize their particular advancements and approaches (Alice&Bob being among the latter), no one has actually reached the finish line just yet with machines that can actually deliver, at scale, in the kind of exponential computing that quantum computing promises.

Yet it all looks tantalizingly close, and given how the rest of the tech world has advanced — with advances in AI, a push for applying technology to solve some of the world’s most pernicious problems, and simply to make existing work more efficient — there remains a huge appetite to continue funding startups like this one to see if who and what makes it all come together in the end.

“As deep tech investors, we are convinced that some of society’s biggest challenges can be addressed by using breakthrough technologies created in research labs,” said Sofia Dahoune, Investment Director at Elaia, in a statement. “As such, we regard quantum computing as one of the most promising world-changing technologies that could foster extraordinary progress across a broad range of applications.”

Posted on

Volocopter raises $170M, now valued at $1.87B, to fuel the first commercial launches of its flying taxi fleet

Volocopter, the startup out of southern Germany (Bruchsal) that has been developing electric VTOL (vertical take-off and landing) aircraft and a business model for operating them in taxi-style fleets in urban areas, has picked up another big round of funding as it inches closer to its first commercial launches. It has raised $170 million, funding that it said it will be using to kick off its first air taxi services, which it noted in an announcement would be “in cities like Singapore, Rome, and Paris.”

The money is part of a Series E, and Volocopter describes it at a first closing made at a pre-money valuation of $1.7 billion, which works out to a post-money valuation of $1.87B. I’ve asked what its target is for the full Series E and any other details it can share on the timing and will update as and when I learn more.

This first tranche is being led by WP Investment, a new backer of the company from South Korea, with strategic investor Honeywell (also a new investor), previous backers Atlantia, Whysol, and btov Partners, and other unnamed existing investors also participated. It has raised $579 million in total to date, and other investors include Geely, Mercedes-Benz Group, Intel Capital, and BlackRock.

Volocopter made a big mark in the autonomous vehicle space when in 2017 — backed by giants like Intel — it ran its first autonomous flying car test in Dubai. (Intel also imported and showed off the Volopter’s self-flying capabilities at an over-the-top event of its own.)

Notably, the announcement of the funding today doesn’t have even a single mention of autonomy or self-driving capabilities, underscoring some more realistic framing as these services get closer to being rolled out.

“This funding round is a testament to Volocopter’s leading position in what is a highly attractive emerging market. We continue to make significant technical and commercial progress as we work toward bringing urban air mobility to life at scale in cities worldwide,” said Florian Reuter, CEO of Volocopter, in a statement.

It also does not specify any timing for the commercial launch, but in March 2021, Reuter told us the services were two years out (so, 2023). We’re also asking about the latest estimated launch date.

Currently, Volocopter highlighting three craft that will appear in its taxi fleets: VoloCity, VoloConnect, and VoloDrone, and it said that it is the “first and only” electric vertical takeoff and landing (eVTOL) company to receive Design Organisation Approval (DOA) from the European Union Aviation Safety Agency (EASA). That means if it plays its cards right it could have a clear shot at the market either as a standalone branded commercial provider, or as a partner to other urban transportation companies, before competitors — others hoping to make a mark in this space include Lilium, Kitty Hawk and Joby Aviation — move in.

On that note, this Series E is an all-equity round, but Volocopter has also been raising a lot of debt for the building of that bigger fleet. Earlier this year it inked a $1 billion deal “in principle” with Aviation Capital Group (ACG) to finance the sale and leasing operation of Volocopter aircraft (that will mean that it could borrow up to $1 billion in debt when the time comes). This will kick in only once Volocopter has full aircraft certification.

It has so far completed some 1,000 public and private test flights.

The company also noted in the announcement that its plan will be to use the funding to get to launch, but also to eventually going public.

“Volocopter has spectacular investors from around the globe, which puts us in an excellent position to focus on our first-to-certification and first-to-market strategies before we embark on the path to public listing,” said Christian Bauer, CCO of Volocopter, in a statement.

That fist-mover advantage, combined with the work that it’s been doing for the last ten years developing its air vehicles, seem to be the two factors that are lending it a lot of credibility right now with investors, even though it has yet to prove a market for the product.

“We are confident that Volocopter will be among the first to bring UAM to cities globally, since seeing its aircraft fly in Seoul last year. As a leader in ESG investment, we are excited to empower city sustainability through Volocopter,” said Dr. Lei Wang, chairman of WP Investment, in a statement. The firm’s co-chairman Tiffany Park added that Korea will be a part of the commercial launch as well.

Posted on

QuantrolOx uses machine learning to control qubits

QuantrolOx, a new startup that was spun out of Oxford University last year, wants to use machine learning to control qubits inside of quantum computers. The company, which was co-founded by Oxford professor Andrew Briggs, tech entrepreneur Vishal Chatrath, the company’s chief scientist Natalia Ares and head of quantum technologies Dominic Lennon, today announced that it has raised a £1.4 million (or about $1.9 million) seed funding round led by Nielsen Ventures and Hoxton Ventures. Voima Ventures, Remus Capital, Dr. Hermann Hauser and Laurent Caraffa also invested in the round.

The company’s technology is technology-agnostic, and could be applied to all of the standard quantum computing technologies. The idea here is that instead of going through a slow manual tuning process, QuantrolOx’s system will be able to tune, stabilize and optimize qubits significantly faster. Current methods, QuantrolOx CEO Chatrath argues, aren’t scalable, especially as these machines continue to improve.

“I was talking to one U.S. investor. He said that we are like the picks and shovels of the quantum industry, in that we don’t have to wait to get revenues for a quantum computer to be useful,” Chatrath said. “As you get from five qubits to — hopefully — millions of qubits, you need our software every single day to be able to do the device characterization and tune the qubits.”

For the time being, though, the company’s focus is on solid-state qubits. In part that’s because those are systems the company has access to, including through a close partnership with a lab in Finland that the company wasn’t quite ready to disclose yet. As with all machine learning problems, QuantrolOx needs to gather enough data to build effective machine learning models.

As Chatrath also noted, we’re still in the very early stages of quantum computing, but if tools like QuantrolOx can help researchers speed up the process of testing their devices, that’s a boon for the entire industry. He noted that a lot of companies in the industry are already approaching the company to use its control software.

The company currently has seven full-time employees and plans to hire about 10 more people in the near future. But as Chatrath noted, he doesn’t expect that number to grow much more in the next two years. “We don’t need a huge team, because we are focusing on our specific niche,” he said. “We don’t want to full-stack. We don’t want to go higher in the stack — and we can’t go lower in the stack because that’s the hardware. So we are very much focused.”

Currently, QuantrolOx is focused on building more partnerships with the builders of quantum computers. These are rather deep partnerships because the team essentially needs access to the physical machines but also the source code that controls them so it can integrate with these systems.

One problem in the industry right now, of course, is that there are very few standards, something Chatrash is keenly aware of. “For the quantum industry to succeed, we need lots of startups like ourselves who are hyper-specialized in one particular area, because without companies who are hyper-specializing, we will not get economies of scale,” he said. “I think this whole full-stack story has to stop sooner or later. People need to start building an ecosystem of companies.”