quantumcomputing

Five Takeaways from “Quantum To Business” 2019

December 20, 2019 by Nabil Leave a Comment

Last week I attended the Quantum to Business (Q2B) conference, an annual event that brings together thought-leaders and stakeholders in the Quantum Computing community.

As a newcomer to Quantum Computing I felt – in the words of a fellow participant – like I was “drinking from three firehoses.” Needless to say, when the smoke cleared from three days of research presentations, product demos, and some incredible chocolate chip cookies, here are my impressions of the current state of the industry.

1. We’ve entered a new era in Quantum Computing. But a lot of tough engineering challenges remain.

With Google’s recent announcement of having achieved “Quantum Supremacy,” there’s a new sense that robust, commercially deployable quantum computers are just around the corner. Not so, seems to be the consensus. A good metaphor for Google’s demonstration is the Wright Brothers’ first flight, which lasted all of 12 seconds. An amazing proof of concept for a new technology, but one with very little practical value.

Qubits – or the induced properties of subatomic particles that sit at the core of quantum computers – are notoriously hard to control. The systems that we’ve built so far are “noisy” – i.e. undisciplined, and spit off lots of inaccurate data. There is a lot of work to be done in reducing errors, or improving the “fault tolerance” of these systems.

Credit: https://towardsdatascience.com/quantum-advantage-b3458646bd9

In fact, the industry hasn’t even converged on how to build a quantum processor. Some teams are moving forward with qubits that need to be supercooled. Others think the trick is using elements that are stable at room temperature. Whereas Google and IBM’s quantum hardware is built using superconducting qubits, Microsoft is taking a different approach, and developing topological ones.

“NISQ,” or “Noisy Intermediate-Scale Quantum,” is the industry shorthand for the era of quantum computing that we’re currently in. While recent advancements have shifted Quantum Computers from theory to reality, until we overcome the (big) engineering challenges that remain, truly useful Quantum Computers are still a ways off. How far off? Some folks say 5-10 years. The joke is that’s what folks have been saying for the last 5-10 years.

2. Today’s Quantum Computers can in fact perform some operations. However, they’re limited in scope.

While we’re still a ways off from reliable quantum computers, there are indeed some things that NISQ Computers are capable of today. The sweet spot is applications that classical computers struggle with, but where getting a wrong answer is – relatively speaking – not a big deal. Such as providing product recommendations (a la Netflix).

One company, D-Wave, has carved out a niche in this space, creating its own version of a Quantum Computer using a more stable process called “Quantum Annealing.” D-Wave has been selling these machines for nearly a decade, with real customers using them on real problems. For example, VW recently announced a partnership with D-Wave, where they will use their machines to optimize traffic flow in Beijing.

Credit: https://www.dwavesys.com/sites/default/files/VW.pdf

Quantum Computers do their work by executing “quantum algorithms.” Some folks at Q2B were adamant that the industry’s focus should be on finding new applications (“use-cases”) for these algorithms. Others felt the emphasis should be on discovering new algorithms, custom-suited for the “noisy” machines that we have. QC Ware, which hosted the conference, is one of several algorithm-developers in this space.

3. Without a revenue-ready product, everyone’s investment pitch is very creative.

As an entrepreneur, the holy grail for a new startup is product-market fit. What’s striking about the QC space is that, for the most part, the product is still under development, and the market demand is still theoretical. It seems obvious that if someone were to build a functional quantum computer, demand would quickly follow. But with engineering timelines unknown, and few profitable applications of the tech as it stands today, everyone is doing their own unique dance for investment and growth.

Investment in Quantum Computing seems to be driven by two factors: perceived potential opportunity. And fear of missing out (FOMO). Specifically:

Government: A robust quantum computer could enable better traffic flows at ports. Or crack, by brute-force, the highest encryption standards we use today. China recently announced a $10B investment in a national laboratory for Quantum Information Science. The US recently announced the Quantum Computing Act, backed by $1.2 Billion federal grant.

Startups: Given the murky product roadmap, a company building a full-stack solution (a la Rigetti) requires investors with patience, a trait investors are not exactly known for. Startups that build just one part of the stack (e.g. software to better control quantum processors) have a more ready market, however a good portion of this market is government and academia – not the typical customer mix you would expect for a venture-backed tech startup.

Enterprises: Large tech players such as Google, IBM and Microsoft have a natural advantage in developing quantum hardware, as with cash reserves they can afford to be patient. Each is vying to be the trusted partner of industry, and build the “sticky” ecosystem that draws in startups and enterprises. Industry is eager to partner with these companies to develop applications for quantum hardware, though seem unlikely to build any hardware themselves. Honeywell stands out as a “traditional” hardware company that’s gone all-in, taking the wraps off a homegrown Quantum Computing program that’s several years in the making.

Consulting Firms: Consultancies seem to have the most ready source of revenue in Quantum. Accenture, Booz Allen, BCG and McKinsey have each built Quantum Computing practices, positioning themselves as the de-facto translator and integrator of quantum computing technologies for the companies they serve. Consultancies already sell “readiness” audits, similar to what we’ve seen in the AI and Digital Transformation spaces.

4. A lot of thought is being paid to building out the larger Quantum Computing ecosystem.

While advances in Quantum Computing hardware are important, if the industry is to be successful, a lot of other pieces need to fall into place. These include: quantum algorithm engineers; hardware suppliers; integrators; sales people who understand the technology and new programming languages that enable end-users to manipulate a quantum processor for the outcomes they seek.

I was impressed by how intentionally these pieces are being put in place. The larger tech companies stand out in their efforts. Google, IBM and Microsoft have all introduced their own programming languages (Cirq; Qiskit, and Q#, respectively), making it easier for developers to write code for quantum hardware. They have also led efforts to build community among key stakeholders, such as startups who are building a given slice of the quantum stack, customers who are interested in developing use-cases, and university researchers looking to conduct new experiments. Microsoft does so through its Quantum Network; IBM through its Q Network, which boasts over 80,000 users.

I even had a chance to play a Minecraft inspired Quantum Computing game, developed to help students and professionals better understand how Quantum Computing works. Part of a larger gaming effort to get folks interested in this space, and encourage a new generation of students to take up degrees quantum computing.

5. Quantum Computing is diversity-challenged.

We know that technology has a diversity problem. The field of Quantum Computing seems to be no exception. I was struck by the fact that about 90% of the speakers were male. So were most of my fellow conference-goers. The “Global South” was missing as well. The scene seemed to be made up of the usual suspects in tech: the US, Europe, and a few “tech forward” countries in Asia.

I was heartened to see some positive moves to address this imbalance at Q2B. Such as male speakers referring to scientists as “she” when presenting. And inviting a female to moderate the conference’s closing panel. As a first-generation American I inquired about – and was extended – a 50% discount on the ticketed price. A generous gesture from Q2B’s organizing team.

I don’t have an answer to this challenge, I was just struck by it over my time there. One step could be to publicize scholarships for underrepresented communities, a path taken by the organizers of MIT’s upcoming EmTech AI conference. Beyond this, of course, we need to address the root causes of this disparity. Work advanced through organizations that support women in STEM.

New technologies represent some of the best economic ladders our society has to offer, for both underrepresented communities, and underrepresented economies. If the future is to be Quantum, my hope is it will also be more reflective of the societies it will impact.

* * *

Q2B impressed me with how smoothly it ran, and how many different important players in the eco-system showed up to trade notes in a relaxed, friendly setting. I would go again.

If you’re interested to learn more about what transpired at the conference, or dig into some of the slides presented (especially with more technical content), I recommend checking out posts under the Twitter hashtag #Q2B19. And if you want to continue the conversation, come join us on Reddit over at r/quantumcomputing.

// As published on LinkedIn.

What Is The Nature Of Reality? How Quantum Computing + A.I. May Supercharge Our Search For Answers

December 2, 2019 by Nabil Leave a Comment

Quantum computing aims to harness the properties of quantum physics to solve real world problems. Its next job may be to help us understand reality itself.

Growing up, the idea of death frightened me. My coping strategy was to distract myself from it, or tell myself that I’d think about it later on in life. This worked fairly well. Minus the occasional night terror. Or that time when I found myself on the family couch, bear-hugging my mom in an existential panic (bless her heart). Today, this fear still crops up, but I’ve gained some new tools to deal with it. As my therapist tells me: feel your fear. And beneath it, often you’ll find another feeling. For me, underneath this fear lies excitement. And underneath this excitement, is for me, a question: What is reality? What is this thing I’ve been born into and, presumably, am so afraid of leaving behind?

As luck would have it, some very smart people have been investigating this question for a very long time. Physicists, in particular, have devised smart theories, many of them validated through ingenious scientific experiments, as to what the nature of our reality actually is. It’s what brought us “every action has an equal and opposite reaction,” or Isaac Newton’s laws of motion. Or “a particle can be a wave, and a wave a particle,” or quantum physics as developed by luminaries such as Niels Bohr, Max Planck and Albert Einstein. Discoveries that spawned new theories about the nature of our reality, such as that it: represents 4 out of 11 possible dimensions; is constantly splintering into copies of itself; or that it’s actually generated by our own consciousness (insert head explosion emoji).

Over the last 50 years, however, our understanding of “reality” – or the world around us as explained through fundamental physics – has slowed. While past discoveries enable many of the technologies we depend on today, the long-term, open-source nature of this research means few institutions have been willing to pony up the investment required to push this field along. Caltech physicist Sean Carrol thinks that today there are fewer than 100 physicists actively working on advancing our understanding of fundamental physics. Is this slower pace of discovery an accurate reflection of our curiosity for the world around us? Thankfully, another path is emerging.

Of the many challenges to testing new theories in fundamental physics, two big ones are time, and cost. The time required to design a test for theory. The cost required to build the experiment and run it. While many effective experiments can be done in the low-million dollar range, the ones that yield the most interesting results can cost much more. The Large Hadron Collider, for example, built to help us discover new particles, took decades to plan and cost a whopping $4.75 billion to build.

Simulations, however, offer a potential workaround. They’re quicker to set up, cheaper to build, and could potentially be as useful to researchers as experiments conducted in the “real” world. The challenge until now has been that our simulations have been – necessarily – basic. Accurately simulating interactions between atoms in matter as small as a molecule is computationally overwhelming, even for our most powerful supercomputers. Simply put, our simulations have not been able to mimic real world experiments. Enter Quantum Computing.

Quantum Computing is a fundamentally different approach to building a computer. At its core, the job of a computer is to process long strings of bits encoded as 0s and 1s. A classical computer (the ones we use today) processes these bits via billions of transistors embedded in a silicon chip. A quantum computer, on the other hand, relies on “quantum bits” – or the induced properties of subatomic particles. These “qubits” have the special property of being able to represent a 0, a 1, or any value in between – at the same time. Because of this feature, they eliminate some of the constraints of (binary) classical computing systems and enable enormous computational outputs in parallel.

Quantum computers may help us run the types of hyper-realistic physics simulations that up until now have been impossible, at a fraction of the cost of conducting those experiments “in real life.” In fact, the very act of building stable, useful quantum computers might give us new insights into quantum mechanics itself.

In addition to Quantum Computers, Artificial Intelligence (AI) may also have a role to play. Today, one common way of building AI is through layered neural networks which, through ingesting large amounts of data (say tagged photos of cats), use increasing levels of abstraction to develop an understanding of how this data “works.” Well, what if instead of making sense of cat photos, we asked this AI to make sense of unexplained natural phenomena, such as Dark Matter? Two physicists at MIT, Tailin Wu and Max Tegmark, have started doing just that. They’ve endowed a machine learning algorithm with four common analytical strategies employed by scientists, and asked it to make sense of increasingly realistic simulations of the physical world. Paired with a quantum computer, we can imagine a rich environment in which an AI might help us make sense of the world around us.

* * *

The rise of Artificial Intelligence comes with a long list of potential dangers. I’m especially wary of how AI can be paired with content to influence our behaviors. AI that – as historian Yuval Noah Harari puts it – “knows us better than we know ourselves.” As with any new technology, at their core Quantum Computers and Artificial Intelligence are tools, which we know from experience can be used just as easily to build, as they can to destroy. The ability for Quantum Computing and AI to help us make gains in areas that are important to us, such a developing better treatments for disease, making our cities less congested and modeling climate change will, I hope, set a clear example of the ways we want to apply these technologies, and a clearer contrast to the ways in which we don’t. And, in the process, maybe even shed light on a question that has sparked the curiosity of humanity for generations: what is the nature of reality?

// As published on LinkedIn.

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