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What's the biggest gap between what quantum can do today and what you actually need?

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For people actually working with quantum hardware or simulators: what's the biggest gap between what you can do today and what you actually need? Is it qubit count, error rates, software tooling, something else?


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Holds PhD in Quantum

It’d be really nice if we could get a co-designed quantum hardware and quantum simulation algorithm pair such that the device was optimized to solve quaternary biological structures fast enough to map drug libraries, novel (e.g. mutated) proteins, and their binding constants.

In principle, if we could generate a full, high-fidelity mapping of drugs to known targets for human biology in weeks to months, that’d be QC’s AlphaFold moment in the life sciences with deep impacts across biomedical research and medicine.

If we could do it in days (fairly standard for intensive bioinformatics workflows) for ~20 protein targets, we’d have the technological basis for scalable n-of-1 drug re-purposing studies in cancer (direct clinical application) and some other conditions.

Right now, odds are if any technology will get there first, it’ll be generative AI.

Working in Industry

I've done some work in the past on optimizing both algorithms and hardware design with quantum chemistry applications in mind, but honestly without a chemistry/biochemistry background it's pretty difficult to work out what the interesting/difficult problems are in the field.

Realistically, most quantum researchers just stick to one single canonical example because one, it's hard enough to get something that works well even for one example, and two, if you pick something new you'd have to explain the impact to get quantum people interested in reading your paper. I feel like at the moment, quantum computing is simply in a "if we build it they will come" phase.

Holds PhD in Quantum

Yea, the cross-disciplinary piece is tough. My MSc training centered on computational biology, dissertation on quantum methods and connecting it to the biomedical domain. I agree with your point and would say that the issues exists on the other side too — biomedicine as a wider field struggles to value quantum methods and their potential; that issues only grown with the explosion of generative AI.

Extending the thought: As a field, computational biology is still figuring out the proper quantum hardware and software abstractions to map problems such that they speak to real (biological) problems with high fidelity. Autodiff frameworks + QML methods seemed a promising (partial) abstraction on the software side, but QML is of an unclear value to biomedical problems generally. Structural biology piece I led with is a gold standard conceptual use case, but the actual roadmap is very rough.

If you ever want to discuss, would be interested in learning more about your experience.

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Working in Industry

Quantum algorithms that solve valuable problems.

Even if perfect large-scale QC hardware popped into existence today, it's impact would be limited. The impact on prime-number-based cryptography would cause IT departments to scramble to update their TLS libraries. Annoying, but not life changing.

What we need is more research into what real-world problems QC capability could be applied to, irrespective of whether the hardware is there or not. Remember that Claude Shannon and Alan Turing had comprehensive theories of what we could use a digital computer for long before they ever existed. Throwing money into the hardware first and hoping that a use case will come along at some point is a really risky investment.

Professor

I agree with you that there is a lack of practical algorithms, but I don’t get the comparison to Alan Turing. We know a lot more about quantum complexity theory than Alan Turing knew about classical complexity theory.

Quantum computers are useful only if we have quantum algorithms with superpolynomial advantage over classical equivalent.

Electronic classical computers became useful running (faster) the same algorithms than human (classical) computers. When electronic computers arrived we already had thousands of useful algorithms.

So how many quantum algorithms with superpolynomial advantage do we have today? The list is thin... Very thin.

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Working in Industry

I concede the point. You're right that Turing, brilliant as he was, was pretty early for me to include him in the claim that we had a theoretical foundation for classical computer complexity theory before they became practical. I'll stand by my claims about Shannon, though. That dude had it all figured out before the vacuum-tube contraptions got very far. We've been riding on his coat-tails since.

I still think that there's a lot of theory to be developed in QC to turn it into practical applications that actually help people outside of very specific niches.

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Throwing money into the hardware first and hoping that a use case will come along at some point is a really risky investment.

Agreed. On that note, certain current classical computing hardware investments spring to mind......

Working in Industry

Oh, no doubt about that! The speculation about AI is structurally very similar to QC. Both have real potential, but the potential also has limitations that boosters can't be bothered to understand in their rush to be excited about something.

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In Grad School for Quantum

Aren't all the potential Quantum Chemistry applications valuable enough already?

Working in Industry

Theoretically. If you're in grad school for quantum computing, please go make your name by helping us prove it. I don't mean that in a negative or cynical way. We're all waiting for the answer to that question. If you can create results rather than blog posts, you're on a good path in terms of your career and in terms of helping humanity.

What are the concrete applications you imagine? How does QC deliver an advantage over classical computing for those applications? Answer that in 80+ pages with empirical results, and you've got your PhD, and might change the world.

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Regarding Quantum Computing and Quantum Chemistry, Garnet Chan gave an interesting interview. You might want to check out here:

https://www.newquantumera.com/podcast/quantum-chemistrys-classical-limits-with-garnet-chan/

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Lots of algorithms can be made asymptotically more efficient by implementing Grover’s as a subroutine. Graph connectivity is an example. Of course, a speed up of (iirc) n{1/4} would be eclipsed in practice by the need for quantum hardware.

I think QC has a great use cases in the field of defense and military industry. What you say!?

Working in Industry

I would say you should articulate those use cases and which quantum algorithms you think can be applied to them and why quantum advantage creates a meaningful differentiation from current capacity.

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Edited
Holds PhD in Quantum

I work on hardware. The scalability issue is directly in my wheelhouse as there’s currently no possible way to fit all the RF lines we need into a quantum computer with logical qubits. Im also in superconducting, so I know that spin qubits have an edge in exploring this compared to me, but there is a hazy roadmap that exists for us via shrinking RF lines into CMOS components that go into the cryostat

Personally, I think the new line of Versal RFSoCs are going to offer really good insights in getting AI computational overhead closer to the control fabric. That, and these companies are going to start pushing out more specialized hardware for quantum applications. Next step is going to either be eFPGAs or cryogenic CMOS, and AI powered multiplexing for cabling in the near future

So, actually the biggest gap may be we need more electrical engineers in the field and people that do what I do, but I’m biased (and very alone in my research…)

Edited

This is an extremely general question. I’ll give my opinion as someone new to the this field.

So far very little from any quantum computing vendors have been shown to be genuinely useful to industries. Most are testing toy problem in pilots. This isn’t a surprise given that we are still at the early stage of HW development. The whole ecosystem still needs to evolve for a number of years to deliver utility. Hypothetically, even if a HW vendor suddenly delivers real quantum advantages today, applying it to real industrial use cases would require both deep domain expertise as well as quantum computing knowledge. So my answer is that there’s no single gap which can be filled to magically unlock broad utility.

For context, my background is B2B software and services GTM. Not in quantum R&D.

Quantum computers can't do anything (useful), so it's all gap.

Funding streams for free and open source software. Current market incentivizes research papers through academia and hardware through industry. There's a huge gap in FOSS.