Quantum Computing Beyond the Hype with Sebastian Hassinger
How to tell genuine quantum progress from hype, why qubit count misleads, where the technology delivers value first, and how a classical developer starts.
Sebastian Hassinger has followed quantum computing from research curiosity toward real machines from inside IBM and AWS, and he wrote The New Quantum Era to explain the field to people without a physics degree. We talked about how to tell genuine progress from marketing, where the technology earns its place first, and what getting ready for it actually asks of an engineering team.
This session was recorded live as part of the Deep Engineering Live Interview Series. The transcript below has been lightly edited for clarity and readability. Audience members joined the conversation and asked questions directly during the session.
Tell us how you came to quantum computing, and where the field stands right now.
My career has been in emerging tech going back to the early nineties, from the early Internet through the arrival of the web. I built the first Apple support site on the web, started a couple of Internet service providers, and worked on innovation inside large companies like IBM and Apple, alongside advising and co-founding startups. So I have moved from one emerging technology to the next for most of my working life.
Quantum found me in 2017. I had been asked to help IBM with the open source strategy for Qiskit, and my first move was to attend the Think Summit at the T.J. Watson Research Lab, where the team was talking about the 53 qubit machine they would launch the following year. It was about ninety five percent incomprehensible to me then, but I could tell it was novel enough and early enough that I would not need to look for another emerging field for the rest of my career. I have always been drawn to quantum physics even without a physics background, so it became an obsession, and within a year I was working on the IBM Quantum team.
On where things stand, the theory goes back to the early eighties, but physical qubits and real proof that you could compute with them did not arrive until around 2000. So this is roughly twenty five years of work, and we are now close to machines that do things classical computers genuinely cannot. Everything so far has been experimental, and your laptop can still simulate a quantum computer better than a current quantum computer can. That is changing fast, and before the end of the decade we should see machines at a scale where they do meaningful work you cannot do classically. That shift drives most of the recent interest.
Strip away the metaphors. What is a quantum computer actually doing that a classical machine cannot?
A lot of the metaphors people use are misleading, so let me try two framings that work for me. A classical computer is built by etching circuits into silicon that switch on and off to represent Boolean logic, and Boolean logic underpins everything it does. A qubit instead uses a two level system from nature, an atom, a superconducting circuit, or a photon, something with two energy levels you can control and read as zero and one. The difference is that those states can exist in superposition, where the value behaves like a wave function sitting probabilistically between zero and one, so a single qubit acts as a vector. Once you have many of them you are doing linear algebra rather than Boolean logic, and linear algebra is very good at high dimensional problems where the variables are heavily interconnected.
The traveling salesman problem is the usual illustration, even if physicists quibble with it. Finding the optimal route through every city is combinatorial, and in a classical machine each new city roughly doubles the combinations you have to test. With qubits you effectively add one qubit to expand the computational space, because the qubits become the exponent for that space rather than a linear count. That is what people mean when they say it would take more bits than there are atoms in the universe to represent a few hundred logical qubits.
The second framing is simpler. You are using quantum systems from nature to simulate other systems from nature. Feynman made this point in his 1981 keynote that many treat as the starting gun for the field, that simulating nature is inherently hard because of the exponential complexity in these many body systems, and that a computer built from the same kind of system should handle the problem far better. The pithy version is that if you want to simulate a system from nature, you need a system from nature.
Superposition and entanglement get used loosely. Which one do people most often misunderstand, and what is the right intuition?
The biggest issue is more an ambiguity than a flat error. People often say a quantum computer tests every solution at once and then selects the right one. That is not literally correct, and the confusion usually traces back to the most famous algorithm in the field, Shor’s algorithm, which Peter Shor discovered in the early nineties. Shor’s algorithm is the one that would let us break RSA and other asymmetric encryption, because it factors a very large number down to its primes, and that factoring is the basis for most modern encryption.
It works through quantum phase estimation. Inside the very large computational space created by many entangled qubits, often called a Hilbert space that scales as two to the power of the number of qubits, the algorithm deliberately creates interference patterns. The wave functions interfere so the correct answer gets amplified and the wrong answers get suppressed. That resembles trying everything at once, which is why the shorthand persists. I once asked Peter how he felt about people describing it that way, and after thinking about it he said, well, it is not really wrong. With anything quantum there is rarely a clean black and white, because this behavior lives so far from our lived experience that any physical or human metaphor introduces some distortion. The communication challenge of grappling with these foreign ideas is part of what makes the field fascinating to me.
Where does that leave quantum computing for AI and machine learning, and why do we know so few quantum algorithms?
There are a few algorithms with a proven theoretical advantage, HHL among them, plus variations that fan out from that small set. I used to find the short list worrying, but I no longer do, and the reason is the history of classical computing. When these machines were being built in the mid twentieth century, nobody knew what they would be good for, and they did not know for a long time. Von Neumann led a team building a machine at the Institute for Advanced Studies in Princeton around the same time the ENIAC was being built at Penn, both driven by the difficulty of the physics calculations that the Manhattan Project and other wartime work demanded.
A mathematician named Stan Ulam, working with von Neumann, came up with a sampling method for calculating neutron diffusion, and he called it the Monte Carlo algorithm. It was more than thirty years before someone thought to use Monte Carlo to optimize a portfolio. The technique existed for decades before anyone saw its value outside the original problem. I expect the same pattern with quantum computing, hopefully faster. These machines will let physicists and chemists simulate many body systems and run experiments they cannot run classically, and the techniques they invent for their narrow problems will very likely carry unexpected value into other domains. So the real work for the rest of us is to watch the early users closely and look for techniques we can generalize into algorithms with value in other industries.
Where does quantum genuinely have an advantage, and where will classical computing stay ahead for the foreseeable future?
It helps to start by correcting an instinct. People arriving at the topic assume quantum computers are faster, or built for bigger data. They are actually slower machines working with smaller data. Qubit counts are in the hundreds now, and we hope to reach thousands, maybe tens of thousands. Qubit count is effectively the register size of the machine. QuEra’s Aquila, a 256 qubit neutral atom device, can load 256 bits of information at a time, so on its own that does not sound impressive. The advantage appears when you entangle those qubits, because you have created a many body system whose state vector is two to the 256, which is more states than there are atoms in the universe. You cannot physically recreate that computational space classically.
So the rule of thumb is that quantum has a natural advantage when the problem is high dimensional and heavily interconnected, because vectors and linear algebra represent that kind of space far more efficiently than Boolean logic can. Those problems are abundant in nature. Material science is condensed matter physics, how atoms pack into a lattice and how the resulting material behaves, and that is exactly the kind of simulation quantum computers should do well. There is promising work already on battery design using quantum approaches, and a real aspiration that if we can simulate materials precisely enough we can design new ones, much lighter for the same strength, or with properties like photosynthesis built into the material itself.
Chemistry is quantum mechanical at its core, because it is about how atoms interact, so it is also very hard to simulate precisely. People point to small molecule chemistry as the next frontier beyond materials, and there is a natural leap from there to pharmaceuticals, which are larger and more complex molecules that need bigger machines. If we can build a quantum computer large enough to simulate how a drug candidate behaves and interacts with its target in the body, drug discovery could compress a great deal, because you screen candidates in high fidelity simulation before you ever formulate them. Beyond the physical sciences people talk about optimization and the Shor class of cryptography algorithms, but those are the small number we already know. The rest will fall out of using machines we cannot simulate classically, which is a chicken and egg situation. We know the physical sciences will benefit, and the broader applications in finance or logistics may surprise us, because we cannot imagine them until the machine exists at that scale.
Enterprises often assume quantum will speed up their existing workloads. For which problems is that assumption simply wrong?
The easiest answer is that if your problem is processing large volumes of data quickly, a quantum computer will not do that in the near term, and possibly never, because classical computing keeps advancing too. I avoid saying never about any of this, but that is a game classical may always lead. The clearer way to see the boundary is through fidelity. In classical chemistry simulation we use methods like DMRG that deliberately throw away much of the information about a reaction because there is too much to calculate, so we focus algorithmically on the small part we hope matters most. The promise of quantum is a much higher fidelity simulation that keeps more of the real dynamics. So the enterprise question to ask is whether your use case involves genuinely high dimensional, highly interconnected data. Optimization is one area with potential, because the more interconnected the parameters, the higher dimensional the problem and the harder it is combinatorially, and a larger quantum computer could represent that more efficiently than classical approaches like max cut.
Even so, it is smart for enterprises to invest now in the skills to understand quantum information and explore algorithms, even though no machine can run anything useful yet. Recasting a business problem takes real effort, because we have thought in classical, Boolean terms since the middle of the last century, close to eighty years. That is a deeply ingrained set of assumptions. We look at the world through the lens of what a laptop can do, and most people are not aware how much of their problem solving quietly assumes Boolean algebra at the ground level. Building quantum intuition, learning to see problems through linear algebra, takes a lot of work. The smartest enterprise approaches I have seen hire a small number of strong people and run research with hardware companies and academics. The team at JPMorgan, for instance, studies theoretical problems that map to aspects of their business, publishes open science, and builds the muscle to apply quantum technologies once the machines mature enough to matter.
Give us an honest read on the hardware. How far are we from machines that do useful work beyond what classical systems already handle?
We are on the cusp, and before the end of the decade we should see machines doing meaningful work beyond classical. But there are still deep scientific unknowns across every modality the vendors are pursuing. It is like the early days of classical computing, when the question was not only how to fit more transistors on a chip but which materials and fabrication processes would even work. Those were genuine unknowns the industry tackled over years, and only in aggregate did it look like a smooth curve. We are at that very early stage in a lot of ways.
Transduction is a concrete example. With superconducting qubits you keep them in a dilution refrigerator near absolute zero, and a fridge has limited space, so to scale you have to connect the qubits at the bottom of one fridge to those in another. That means converting them to a photonic frequency used in telecom, carrying the quantum value over fiber, then converting back to the native frequency in the second fridge. None of the ways we currently know how to do that delivers the fidelity the device needs to operate reliably, and we do not yet know what it will take to fix it. That is a scientific challenge, not just hard engineering, and scientific challenges involve luck, because you do not know what you do not know.
This is why roadmaps are difficult to read. A roadmap is an engineering document projecting deterministic milestones, one year out, three years, five years. Engineering is far more deterministic than the scientific breakthroughs required to enable it, so when I talk to investors I tell them to do deep due diligence on the scientific challenges a hardware company still faces. For an end user the specifics of any single vendor almost do not matter. What matters is the North Star the whole industry sails toward, fault tolerant logical qubits at a scale you cannot simulate on a laptop or a GPU cluster. Once you cannot simulate it classically, you have to run it on a quantum computer, and that is the only line that counts.
Error correction keeps getting described as the bottleneck. What actually changed in the last year, and what has not?
Start with what qubit count actually tells you, which is less than people think, because it is effectively register size, not a measure of progress. We have had machines with thousands of qubits before. At IBM the roadmap produced a chip called Condor at just over a thousand qubits, and it had real value for the R&D effort of designing and fabricating it, but the connectivity was so low and the noise so hard to manage that even researchers preferred going back to the smaller machines around 127 to 133 qubits, which were more capable. So raw qubit number is not an indicator of progress toward usefulness.
What matters is fidelity and noise, and a good proxy is the resilience of the error correction code, which you talk about as a distance or a d value. That is roughly how many errors the system can absorb before the information collapses and you lose the computation, so a higher distance means a more resilient system. The Willow experiment about a year and a half ago was distance seven, if I remember, roughly a hundred physical qubits in a surface code presenting as one logical qubit. That is the useful snapshot, because as an end user you do not need the underlying gate fidelities, you need to know how many logical qubits you get and how resilient the error correction is. What has not changed is that fundamental science is still involved, so anytime you hear someone say quantum is just a matter of engineering now, be suspicious of their claims.
What does getting quantum ready actually mean, and how does a classical developer start?
Quantum ready means different things by context, but broadly it means developing some intuition for what a quantum information approach to a problem looks like. For an existing software developer the best way in is usually coding, and the tools are familiar. Most quantum programming is done in Python. Qiskit is IBM’s Python SDK, and Amazon Braket, which I worked on at AWS, is also Python, along with several others in well understood languages. The way you construct a task and send it to a quantum computer uses tools we already know. The hard part is the logic of the circuit itself, so there are gentle entry points to start building familiarity.
One of my favorites is the Unitary Foundation, where I am a fellow. Every year they run Unitary Hack, a global event over a couple of weeks where maintainers of open source quantum software tag issues in their repositories and developers close them for bounties. The issues are often housekeeping, security, or maintainability work that any classical developer recognizes, and as a side effect you see how a quantum software package works inside. My favorite example is an engineer named Misty Wall. She was a mechanical engineer at ASML, finished a major project, wanted something new, and got interested in quantum without any background in quantum information. She started closing tickets in Mitiq, an open source error mitigation framework, and two or three years later she was lead author on research papers on quantum error mitigation. It does not happen for everyone, but it is a real path.
Unitary Foundation runs a Discord year round, and the popular packages each have a channel, so you can see what is out there, including open source simulation packages. Building a simulator to run a quantum circuit classically is a very good way to understand how circuits and algorithms are actually represented. For a developer this is the best way in, because the alternative is a physics PhD and a job in a lab, which is a long and demanding path. The good news is that other people’s hard work now lets us reach real quantum computers over the Internet with tools we already know.
Follow-up questions over email
After the session, Sebastian answered two questions we did not reach live.
Of the domains people cite, chemistry, materials, optimization, cryptography, machine learning, which is closest to real value and which is furthest away?
Probably materials, with small molecule chemistry, which is sometimes treated as a type of material science, a close second. Optimization, cryptography, and machine learning all need thousands of logical qubits, so those are further off.
You have been on the commercial side at AWS and IBM. Where does the marketing most often outrun the engineering?
Since at least the early Q2B conferences run by QC Ware, there has been a recurring chorus demanding to know quantum computing’s use cases and how it will be useful for enterprises. Marketing can be tempted to take speculative ideas and present them as certainties, stretching a scientist’s speculation into something definitive. The other constant question is when, so timelines are another place marketing takes liberties.
Sebastian Hassinger writes and hosts the New Quantum Era podcast, and his book The New Quantum Era explains the field for readers without a physics background. Find him on LinkedIn.