- Hello, and welcome to the Physics World weekly
- podcast,
- which is supported by American Elements.
- Simulating quantum systems is something that classical computers
- struggle with.
- Not surprisingly,
- quantum computers are expected to do a much
- better job,
- particularly when a quantum computer's
- architecture
- is designed to solve a specific type of
- physics problem.
- My guest in this episode of the Physics
- World Weekly podcast
- is Ross Jenkinson
- of The UK's
- University of Manchester.
- He talks about the challenges and rewards
- of using quantum computers
- in particle physics,
- a field where classical
- supercomputers
- can struggle
- to simulate simple systems.
- That conversation
- is coming up after this message from American
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- Hi, Ross. Welcome to the podcast.
- Hi, Hamish. Thank you very much for having
- me.
- Yeah. I'm really looking forward to talking about,
- applications for quantum computing
- in particle physics and, beyond.
- So
- maybe we could start off with, sort of
- a basic question.
- Why are quantum computers useful
- for simulating
- particle physics collisions?
- And, you know, in comparison to
- conventional
- supercomputers.
- Yeah. It's it's a great question. So but
- first of all, how are quantum computers different?
- So classical computers are kind of written in
- this language of of binary bits. So zeros
- and ones you might have seen in the
- movies. Lots of zeros, lots of ones. But
- crucially,
- it's a zero or a one for any
- given bit. Whereas a quantum computer, because of
- the principle of superposition,
- a quantum bit or a qubit as we
- call it, can be in zero and one
- at the same time. So what that means
- is that we can evolve two possibilities
- at once. And if you have two qubits,
- then there's four possibilities. You have zero zero,
- one one, zero one, and one zero. So
- you have four possibilities
- that you can evolve simultaneously.
- And, actually, as you keep growing this number
- of qubits, so you have n qubits,
- then that scales as two to the power
- of n. So that's mathematically, that's an exponential.
- So when we say it grows exponentially, we
- really we're not saying that colloquially. We mean
- it really does mathematically grow exponentially.
- The number of possibilities that you can simulate
- and evolve at the same time. So this
- is where a lot of the power of
- quantum computing comes from.
- And the reason why it's particularly good
- at simulating
- quantum field theories or or quantum mechanical systems
- is because
- when you have some quantum mechanical system, you
- may have heard that things can happen simultaneously
- to a particle. If it wants to go
- from a to b, it might take one
- path, but it might take a different one
- and a different one. And actually what quantum
- mechanics says is that it takes all of
- these paths simultaneously.
- And if something has a certain energy level,
- it can be in multiple energy levels at
- the same time. So when we start doing
- quantum mechanics calculations,
- you very quickly realize that this system size
- called the Hilbert space
- grows exponentially as well. So it's really, really
- difficult for a classical computer
- to simulate these things. Whereas, as I was
- just saying, this is the natural language of
- a quantum computer. It can simulate these things
- in exactly the same way, also grows exponentially.
- So they're really good. They're kind of a
- natural way to simulate quantum mechanics. Perhaps that's
- unsurprising that a quantum computer is really good
- at simulating
- quantum physics.
- So that's the goal. That's what we're trying
- to we're trying to do with using quantum
- computers. Yeah.
- And and what's the the the state of
- the art at the moment? So, you know,
- quantum computing is a fairly
- new technology.
- Are the the quantum processors that are available
- today,
- are they up for the job? Are they
- able to,
- to sim to simulate meaningful
- particle physics systems, or do we still have
- a way to go,
- in terms of getting,
- practical results
- out of a quantum computer when it comes
- to particle physics?
- Yeah. So there's kind of a few different
- things there. I mean, so
- the current state of the art will depend
- who you ask. Specifically, it depends which company
- you ask. But if you wanted just to
- get a large array of cubits, I think
- there's a a company that has done 6,000
- cubits all in a all in a large
- collection, and that's all well and good, but
- they're not interacting. You can't use them to
- calculate. In terms of a benchmark calculation,
- so, yeah, chips are already being developed. You
- have Google who have developed a chip that
- they've called Willow, which is a 105
- qubits.
- So that's pretty powerful. I mean, if you
- think about that in terms of classical bits,
- that's not very lot at all. A 105
- bits
- of information isn't a lot. But when you
- do two to the power of a 105
- and think about the kind of possibilities
- there, that is actually really powerful. In fact,
- they did a calculation,
- and this is kind of cheating a little
- bit because they deliberately built the chip to
- do the specific calculation. So I don't want
- to oversell it, but it was built to
- do a calculation, and it did it in
- five minutes. And they calculated that the world's
- largest, most powerful classical computer
- would have done it in ten septillion
- years.
- So it would have taken a a huge
- amount of time for a normal computer to
- do the same calculation.
- So they are already doing really powerful, interesting
- calculations
- using quantum computing chips. But how
- relevant is that for the particle physics stuff?
- Well, we're still a while off. I I
- would say from both a theoretical and an
- engineering point of view, we're kind of in
- the foothills of this really exciting journey.
- We're we're starting to do
- benchmark
- example calculations,
- But in order to actually get them to
- simulate real particle interactions,
- that's quite difficult. And, actually,
- the classical methods we have are really powerful.
- We've been we've been developing them for for
- decades. I say we have been in development
- since before I was born.
- So really, really well tested stuff. In fact,
- quantum electrodynamics,
- which is one of the
- basically applications of our of our quantum mechanical
- methods
- is one of the most well experimentally
- verified methods
- in all of science. It's it's experimentally verified
- at the Large Hadron Collider to about one
- part in 1,000,000,000,000.
- So that's I mean, just to give some
- scale as to how
- unbelievably accurate that is. That's like measuring the
- length of The United Kingdom from from Lands'
- End, Saviano Groats,
- down to the width of a red blood
- cell. In fact, it's more accurate than that.
- So one part in a 1,000,000,000,000.
- So really, really good methods. And so we
- don't have to just get quantum computing to
- work. We have to get it to be
- even better than that, basically.
- And so this is where that kind of
- power of this simultaneously
- evolving things. And, actually, what classical methods do
- is they kind of do slight approximations because
- they they can't do that. So there's certain
- regimes where they can do calculations very well.
- But to actually evolve a quantum system in
- real time and kind of, like, press play,
- press pause, inspect it, we can't do that.
- Whereas a quantum computer perhaps could.
- And and Ross, you mentioned,
- a a quantum computer that was created to
- solve a specific problem,
- and it did so very well. Is that
- is that sort of the idea
- in how quantum computers would be used, at
- least initially, to to solve particle physics problems?
- The the architecture
- of the quantum computer would be designed
- such that it was
- somehow similar
- to
- a particle collision
- that you were interested in? Is is is
- that one way of, you know, sort of
- accelerating
- the use of of,
- of quantum computers? Actually,
- making the architecture
- fit the problem.
- Yeah. I mean, that's a really, really good
- point. And that that is exactly
- the the way forward, I think. So the
- era that we're in at the moment is
- called the NISQ era, n I s q,
- stands for noisy intermediate scale quantum era.
- So the noisy is really the the the
- main part about that. And what that means
- is that we can't error correct. So we
- can't we take a bunch of qubits and
- they're really susceptible to
- changes because of their interaction with the environment.
- They're really hard to isolate.
- So what we have to do is we
- have to kind of get a group of
- qubits to all fact check each other constantly.
- That's how we do quantum error correction, but
- that is currently the engineering challenge that everyone
- is trying to to overcome.
- So we're kind of in that era at
- the moment, and what that means is that
- we don't have fault tolerant quantum computing, but
- we also don't have we're not really close
- to. We're probably twenty, thirty years away from
- a fully programmable quantum computer. So if you
- think about your laptop, you can do all
- sorts on your laptop. It's it's very programmable.
- Right?
- So because that's so far away, you're exactly
- right. We can instead build devices that are
- kind of purpose built. Imagine
- like a calculator. A calculator is a really
- useful device. A calculator is really good at
- maths, but it's not gonna tell you the
- weather. You know? So if you wanted to
- have a purpose built device, you can exactly
- do that. Firstly, with the architecture, so, yeah,
- so you can you can design it in
- such a way
- using a certain medium that it might be
- specifically good
- at. So, for example, when I talk about
- these qubits, they seem like quite abstract ideas,
- but the actual candidates for qubits are things
- like cold atoms,
- trapped ions,
- photons.
- Each one of them has pros and cons.
- And so for specific calculations, you might want
- to use different ones.
- So there's lots of investment in which one's
- gonna be the best for a fully programmable
- quantum computer, but actually they all have their
- own advantages.
- So yeah. And and one of the things
- that I do in my research is
- I say, well, it's all well and good
- trying to build a quantum computer, but if
- you put one in front of me, the
- world
- at the moment doesn't have a set of
- algorithms to run on it, to actually do
- the stuff that we want to do. So
- I'm coming up with
- architecture in the theoretical sense. I'm coming up
- with circuits and algorithms that we could actually
- put onto a quantum computer to get it
- to do the stuff we want to do.
- And so that's the kind of stuff that
- we have to build
- kind of in parallel to the engineering challenges
- for lots of companies are trying to overcome.
- And and what about the idea of, you
- know, you mentioned that conventional supercomputing
- is very advanced
- when it comes to, understanding
- particle physics or doing simulations on particle physics.
- Is I mean, is part of the way
- forward to to only use the quantum processor
- to do a specific calculation
- that would take ages on the supercomputer,
- and then sort of feed that information back
- into the supercomputer, and back and forth, and
- back and forth. So you would use the
- quantum computer
- as a sort of, well, auxiliary
- processor or something that can do one thing
- very well.
- Yeah. Is that a way forward?
- Yeah. 100%. So it's kind of a hybrid
- computing, part quantum, part classical. That's really important.
- Firstly, because there's a bunch of stuff that
- classical computers are very good at, and we've
- already got them to do. And, actually, the
- quantum computer, we don't need it to do
- and it and actually quite difficult. So for
- example, readout,
- reading out a measurement, we know how to
- do that classically. It might be quite hard
- to do quantum mechanically. So as long as
- we get the kind of hard part of
- the calculation
- kind of bootstrapped
- onto a quantum computer, then that's where the
- real advantage lies. It's also worth saying, again,
- there's a lot of hype and and rightly
- so, and I'm very excited about it. It's
- why I've chosen to research it for my
- my job. Right? But it's important not to
- overstate that
- quantum computers will never completely replace classical computers.
- I don't think we will ever live in
- a world where no one has phones, laptops,
- whatever equivalent there is that's running on this
- kind of classical baseline of digital bits,
- and everything is quantum. I don't think that
- would be the case. I think the most
- optimal way will be a hybrid of the
- two, exactly like you say.
- And and what in terms of,
- sort of general areas of particle physics that
- could benefit from quantum computing. You you mentioned
- quantum
- electrodynamics,
- but then there's quantum chromodynamics,
- which is
- extraordinarily
- difficult
- to to do any calculations, really.
- You know, this is the the the physics
- that describes,
- how quarks behave inside protons and neutrons.
- It that is a very difficult problem, computationally.
- Will
- quantum computing help with that?
- Is it is that sort of a quantum
- computing friendly problem where lots of progress could
- be made?
- Yeah. Absolutely. So for a few different reasons.
- So so firstly,
- quantum
- chromodynamics
- quite often in certain energy limits
- doesn't play well with perturbation theory, which is
- our usual toolbox for quantum mechanics, which basically
- just means that if you have a small
- enough coupling constant, you can
- expand your maths in different terms that are
- in less and less important.
- Whereas in quantum chromodynamics, that's often not the
- case, and so what they do is they
- call they do something called lattice QCD,
- which is really interesting in and of its
- own right and very, very well experimentally tested,
- and
- the theory there is really good.
- But wouldn't it be great if we could
- real time evolve of these these quarks and
- these quantum chromodynamic particles
- and actually inspect it in real time, real
- time quantum field theory. That's the kind of
- the stuff that I'm interested in. And it's
- also worth saying that there's lots of other
- things in quantum chromodynamics that expand
- massively. So, for example, color
- color flow. So color, this red, green, or
- blue, is how we label quarks. Each one
- of them has their own color. It's the
- charge of QCD,
- kind of like how electric charge is the
- charge of QED. Right? So you have this
- this color,
- And the number of color states as as
- as your your your gluons and your quarks
- and all of these, they explode in these
- jets.
- That grows factorially. So it's actually even worse
- than exponentially. So it's it's impossible
- impossible to do
- on a classical computer
- a 100% accurately. Again, there's a lot of
- really good work that's being done to try
- and model these parton showers. But if we
- can get a a quantum computer to attack
- this problem, I think it would be really,
- really useful. Yeah?
- And and another, sort of computational technique that,
- I suppose is of great interest to,
- particle physicists is
- artificial intelligence. Can
- you talk a bit about how,
- artificial intelligence
- is is being used and could be used
- by,
- particle physicists,
- you know, maybe in the context of quantum
- computing?
- Yeah. Absolutely. I mean, firstly, just at the
- the kind of easy level of, like, how
- do I use AI on a day to
- day basis and the way that it's transformed
- my life. I mean, throughout my PhD, I
- didn't have access to AI. And so when
- I coded something that would be time consuming
- or doing a literature review, obviously, I still
- go away and read lots of papers. But
- finding papers, I would essentially just be using
- Google or looking in journals.
- AI right now is really useful for physicists
- across the world for accelerating the coding process.
- You still have to double check it because
- it can often be be incorrect, but also
- giving you a group of papers to go
- away and read, and you can decide whether
- they're relevant. And often, it does a pretty
- good job of giving you a batch of
- papers to familiarize yourself with the field. So
- that's kind of the the low entry level
- way that AIB is used by me at
- least on a day to day basis. They're
- kind of like blue skies idea, the stuff
- that I'm also working on in terms of
- AI and machine learning.
- Is can we apply these to kind
- of fit quantum algorithms in a way that
- often quantum mechanics is is really counterintuitive.
- So maybe we're thinking about it in the
- wrong way when we try and come up
- with quantum circuits. In fact, it is very
- common that someone comes up with a quantum
- algorithm that is essentially just a classical algorithm,
- but written in the language of qubits and
- actually isn't
- using things like superposition and entanglement to actually
- gain any kind of advantage.
- And so that circuit is never gonna actually
- be better than a classical computer.
- So
- maybe AI
- can work to kind of come up with
- new quantum circuits. And in fact, that is
- being done. There are research groups around the
- world that are kind of trying to come
- up with almost a large language model, but
- for quantum algorithms.
- So that's a really interesting and a niche
- area that that
- excites me a lot. But also machine learning
- says something that I want to try and
- do is
- when we evolve these quantum systems and there's
- there's infinite number of possibilities,
- and formally, you have to
- sum up every single possibility. So how do
- you do an infinite sum? Well, it's very
- difficult.
- And so often,
- computationally, that's impossible. So you have to come
- up with tricks and you have to think,
- okay. Well, which paths are actually important and
- which ones aren't important?
- Or maybe a machine learning model could learn
- that for us. And maybe in the process
- of doing that, it will reveal something about
- quantum mechanics that we don't yet know. So
- these are the kind of ways I want
- to apply AI and machine learning.
- And what what about your colleagues,
- you know, who run particle physics experiments? I
- would imagine that
- AI and machine learning
- is becoming really important
- in in helping them deal with the the
- sort of deluge of data that comes out
- of,
- an experiment on the Large Hadron Collider, for
- example?
- Yeah. I mean, this is, again, great question,
- brilliant point, and and actually quite relevant to
- another thing that I'm working on, actually. So,
- basically,
- Abelard, Hadron Collider, for those who don't know,
- it it's billions of particle interactions per second
- when it when it's running. So these datasets
- are some of the biggest datasets in the
- world, and it's not just one bit of
- data for each particle. You you get its
- momentum. You can get scattering angles. You get
- all all sorts of things. So this dataset
- is really massive. And so
- scouring through it and trying to find interesting
- bumps in the data that you weren't predicting
- that might reveal new physics,
- That's a really difficult thing. Again, it has
- been done very well. I mean, the Higgs
- particle in 2012 was discovered using classical techniques.
- So
- it's
- doing really well,
- but machine learning absolutely is being used
- to essentially try and spot patterns
- in the data
- in a much quicker, much more efficient way,
- and perhaps in a in a novel way
- using techniques that we might not have thought
- of
- in order
- to discover
- new particles or new structures
- in the standard model. So, for example, something
- I'm working on is, can we map this
- to a quantum computer? So can we take
- the final
- state particles,
- take their momenta,
- map it onto a bunch of qubits,
- and then entangle those qubits in such a
- way that we try to mirror some of
- the correlations that you might expect between these
- final state particles.
- So we entangle
- pairwise entangle the final state particles, and then
- we run a machine learning code on this
- network of qubits.
- And
- can this quantum computing and machine learning work
- together to potentially give us an advantage over
- traditional machine learning methods.
- So some really exciting stuff there, but absolutely
- machine learning and AI is being used
- and being used increasingly more in particle physics
- and a lot of my colleagues at the
- LHC.
- So it sounds like,
- so so the LHC at the moment, I'm
- I'm not sure if it's is it shut
- down for the high luminosity
- upgrade or is it Yeah. It's about to.
- About to. Right. So, you know, that's going
- to take a few years. And, I mean,
- I would imagine that when the LHC is
- back and up and running,
- I mean, is it going to be a
- different world for particle physicists? Because all these
- machine learning and AI techniques
- will have been
- developed
- and,
- matured.
- And also, you know, we we've got another
- few years of quantum computing
- progress to be made. I mean, you I
- I mean, it sounds to me like you,
- and your colleagues should be very excited
- about,
- a new sort of paradigm in how you
- can,
- interpret data from the LHC, and lots more
- data,
- that that is a result of the of
- the upgrade. I mean, it it must be
- very exciting.
- Yeah. Well, exactly. And that point, if I
- made it there at the end, is is
- the crucial one. I mean, it's coming at
- the right time. There's this high luminosity upgrade
- is gonna give even more data. So that
- would almost be quite scary if we weren't
- equipped with the right tools to combat that,
- and that's exactly what AI and machine learning
- give us access to. And also, it's worth
- saying that, I mean, a lot of my
- work, I don't work directly with datasets of
- a large Hadron Collider often, and the kind
- of stuff that interests me
- in parallel is this simulation using quantum computers
- to simulate
- particle interactions.
- So we can build that on a a
- tabletop experiment.
- Obviously, we need probably thousands and thousands at
- least of qubits.
- So that might be a little while away,
- but I can think of the algorithms now
- and say one day when you give me
- a tabletop of qubits, this is what I'm
- gonna run and I'm gonna simulate
- a particle interaction. And so we can kind
- of attack it from two angles, top down
- at the LHC. Look at all this data.
- Let's go deep diving into it. And bottom
- up from tabletop quantum computing experiments.
- Let's try and build a simulation of these
- and see if we can expect it from
- below. So we can kind of pincer movement,
- the the standard model of the universe, and
- try and understand more about it.
- And, Ross, I wanted to to shift gears
- a bit and and talk about
- how quantum computers could be used to
- to, I suppose, solve one of the the
- the biggest challenges
- facing physicists at the moment, and that is
- developing
- a viable
- quantum
- theory of gravity. You know, at the moment,
- quantum mechanics is
- an extraordinarily
- successful theory.
- And, Einstein's
- general theory of relativity does a great job
- at describing gravity.
- But, it has proven very difficult to bring
- those two things together in a in a
- theory that works. So how could quantum computers
- help,
- with that challenge?
- Yeah. I mean, exactly like you say that
- they're kind of fundamentally incompatible despite being two
- of the most well verified theories in all
- science. It is quite counterintuitive, but general relativity
- speaks in this language of continuous space and
- time and deterministic
- results.
- And quantum mechanics speaks in this language of
- discretized space, quantized
- space, and energy levels, and and not deterministic
- at all. It's it's inherently probabilistic, and so
- they're completely different languages. The mathematical toolbox that
- you use for them both are completely different.
- So for that reason, they're incompatible, but they're
- also if you try and quantize gravity or
- gravitize quantum, you end up with a bunch
- of divergences,
- so infinite terms.
- So
- for example, if you try to quantize gravity
- and think of a particle, call it the
- graviton,
- it's infamously unrenormalizable.
- I know there are some people who might
- take issue with that, but that's kind of
- general consensus. Right? So it's it's really difficult.
- So this is where I'm gonna come back
- to trying to do the bottom up approach
- because
- there are some theories
- that are dual theories of quantum and gravity.
- Now they're not theories of our universe. They're
- theories of kind of toy models,
- but it's this idea
- called the holographic
- principle.
- And the idea is if you come up
- with some
- quantum theory and you calculate a bunch of
- quantities in this quantum theory,
- it's actually equivalent
- to a gravitational theory that's not quantum whatsoever
- in one dimension higher,
- so an extra space time dimension,
- and you calculate the same quantities, you get
- the same results.
- So, again, one is not contingent on the
- other, but two different theories give the same
- results, almost to imply
- that if you have a gravitational theory,
- the more fundamental version is this quantum mechanical
- theory that exists in fewer dimensions.
- So, for example, something you could do on
- a tabletop is something called the SYK model,
- the SAC devier Kitaev model.
- And that's actually a zero plus one dimensional
- theory, which means a zero space dimensions
- and one time dimension. So it starts to
- get very wacky, very hard to picture. It
- says, imagine you have a bunch of particles,
- essentially. They're called Majorana fermions.
- You have a bunch of these particles.
- They don't exist in space because space doesn't
- exist, but we can still label them in
- the kind of abstract,
- and they do
- change with time and their correlation has changed
- with time. And all you do is you
- randomly couple different groups of these particles.
- And then there's your quantum theory and you
- calculate a few things and it's quite a
- simple theory,
- something that we could try and build using
- qubits in a lab.
- What actually happens is really interesting is that
- the results you get are equivalent
- to something called j t gravity. So this
- is this is a one plus one dimensional
- theory. So one dimension of space, so it
- exists on a line basically
- and varies in time.
- So you have this
- baseline quantum model, this SYK model,
- almost giving rise to gravity and giving rise
- to a dimension of space because the two
- theories give the same results.
- So this is really interesting. Obviously, we don't
- live on a line. We don't live in
- a one plus one dimensional universe, so it's
- not useful for us in our understanding of
- our universe. But if we can build a
- tabletop experiment
- that doesn't have gravity in it at all,
- just has entanglement and cubits,
- and from that get results that mimic gravity,
- then perhaps we can make the argument that
- gravity and actually space itself
- and perhaps even time
- are emergent properties of quantum entanglement.
- So this is the idea of emergent space
- time, and this is the kind of way
- that we could attack quantum gravity. Rather than
- gravitize quantum or quantize gravity, let's
- use fundamental quantum building blocks to build space
- and time itself.
- And that that takes me nicely to to
- my final question for you, Ross. And I
- I suppose it's
- a more general question about the intersection between
- quantum mechanics
- and, general relativity.
- Quantum information theory, which is the sort of
- the conceptual framework
- behind
- quantum computers, is being used
- to study
- that interface.
- And
- it it it seems to be throwing up
- some really interesting things.
- I mean, I I wrote an article probably
- about a year ago about something called,
- indefinite
- causal order, which is a really strange thing
- when you when you start,
- combining,
- quantum mechanics
- and general relativity.
- And,
- quantum information theory is, you know, it's been
- shedding light on the nature of
- space time,
- black holes, and, you know, all those sort
- of wonderful things. Can you talk a bit
- about
- what sort of insights quantum information theory can
- give us into the, you know, the universe
- in general, I suppose, beyond
- the quantum world. Yeah. And it's really, really
- interesting. So information is kind of this abstract
- concept, but it's
- often proposed as being fundamental in the universe
- and and information is always conserved.
- And so when you have information about, for
- example, a thermodynamic
- room of particles, you you apply something called
- statistical mechanics to that, and you can get
- some conclusions about the information in the room,
- often this thing called entropy. Right? So may
- have heard of entropy before. There's quantum mechanical
- equivalent. Imagine you have lots of quantum particles
- and you can do this the field of
- quantum information.
- And within that, there are definitions of entropy
- about how much we know about this quantum
- system, how much we don't know, what if
- we only look at part of it, what
- does that teach us about potentially entanglement between
- different regions, things like that. And this toolbox
- has been developed for a little while and
- it's really interesting, and it kind of can
- give us a bunch of stuff to do
- with quantum computers.
- But then in parallel, the field of black
- holes was developing their own little toolbox to
- try and understand the event horizon and what
- happens to things that falls inside a black
- hole. And then really surprisingly,
- some of the results
- again seem to match. It seemed to be
- that, actually, we can start using these ideas
- of quantum information theory on black holes themselves.
- And, actually,
- our current understanding, and this is really bleeding
- edge physics at the moment, so this might
- change, but But our current understanding is if
- something falls into a black hole, that information
- isn't lost forever.
- It's actually encoded in some sense on the
- surface of the black hole
- on the event horizon. And Leonard Susskind would
- be an advocate for saying it goes inside
- and outside the black hole. This is the
- idea of black hole complementarity.
- So there's some really interesting stuff here, but
- the idea is it's preserved. It's encoded, almost
- encrypted, and it's done so redundantly.
- So you don't need the entire surface of
- the black hole to reconstruct all of the
- information, almost as if it's error correcting in
- some way. And then that information is slowly
- released back into the universe over billions of
- billions of years
- through Hawking radiation. So some really interesting stuff,
- and all of this has been found out
- by applying this toolbox of quantum information
- to black hole physics and to these extreme
- environments where there's interesting quantum mechanical effects and
- strong gravitational effects. So this could be a
- good playground to understand quantum gravity, for example.
- So this is a kind of idea that
- we can apply quantum information to perhaps surprising
- areas of physics and learn quite a lot.
- Yeah. That that that is really interesting. I
- mean, is this this,
- black hole? Is it that firewall?
- I I I remember there's a lot of
- work about that.
- Maybe five years ago, there was a flurry
- of,
- of papers. And, yeah, I remember
- really struggling
- to get my head around,
- exactly what was going on. But, yeah, I
- mean, that is a really interesting it's is
- that is that the black hole information paradox?
- Exactly. So the information paradox originally was when
- when Stephen Hawking
- came up with, oh, hold on. That there's
- some kind of radiation that's leading out. But
- he said that radiation cannot
- contain the information. The information's inside the black
- hole. Right?
- So but the problem is if if a
- black hole is radiating, it's it's slowly shrinking.
- So eventually, that black hole will disappear. So
- if the information is not in the radiation
- and it's the black hole is gone, where's
- the information?
- So this was the information paradox. And he
- actually had a bet, with with, with Preskill
- about this.
- And he said, Preskill said that, no. The
- information must be in in in your radiation,
- in the Hawking radiation. And Stephen Hawking disagreed,
- but eventually conceded that that he was correct.
- And that that is where we're at at
- the moment. We think the information is indeed
- in the Hawking radiation. So it is really
- interesting. Really interesting. And, again, this could change,
- but this is what's so exciting about the
- field.
- Yeah. Yeah. Yeah. It it it's all coming
- back to me now. And then you've got
- this no is it the no hair theorem?
- Yeah. Is that is that at the heart
- of the paradox that the black hole
- can't
- hold this information or that it shouldn't be
- there or Yeah. There can't be kind of,
- this kind of fuzzy thing that happens on
- the edge. But this is all all kind
- of intertwined, and and the firewall that you
- mentioned earlier is kind of, again, up for
- debate. So some people would would believe that
- you can't cross the event horizon because you
- would you would tear up. It would kind
- of become infinite temperature. That isn't really the
- prevailing theory at the moment. I think it's
- slightly more common to say that if you
- invoke the equivalence principle, which means that if
- you're in free fall, you're not supposed to
- be able to tell if that you're in
- a particularly special environment, including that of the
- event horizon. So for us if it was
- a small black hole, the tidal effects would
- rip you apart, so you could definitely tell.
- But if it was a really supermassive black
- hole, you could pass the event horizon and
- not even notice. We could be doing it
- right now. The Earth could be falling into
- some supermassive black hole, and we wouldn't know.
- So this is
- kind of what we think
- is happening. Obviously, if you look at someone
- from afar
- because of general relativity and different different
- perspectives, different frames, you would see something different.
- You would never see them pass the event
- horizons black of the black hole because time
- dilates infinitely. So that light
- is never gonna reach you of them of
- them passing the black hole. So this is
- kind of, again, leading back into the Susskind
- view of complementarity.
- You you you almost pass the event horizon
- and don't pass the event horizon, and something
- is also true for information.
- So really, really cool stuff.
- Yeah. It is I mean, it is astonishing,
- or at least I find it astonishing that
- you can you can have these sort of
- quantum conversations
- about a black hole, which is, you know,
- something that's huge and, you know,
- the
- incredibly violent
- gravitational
- field that it has. You would have thought
- it would it would be the least quantum
- thing in the world.
- Yet, you know, you've got all these subtle
- effects,
- that you that I suppose quantum mechanics forces
- you Yeah. To to think about.
- Well, that's what I mean.
- Everywhere. Even in the the vacuum of empty
- spaces,
- motivation to explain Hawking radiation. Even the vacuum,
- right at the boundary of a black hole
- at the event horizon, There's interesting stuff happening
- there. There's an interaction between these really cool
- environments.
- Well, that's great, Ross. Thanks so much for
- for coming on the podcast and and talking
- to us about quantum computing and particle physics,
- quantum gravity, and black holes as well.
- So so what's some
- you mentioned some of your research,
- at the moment. Is there anything in particular
- that you're working on now that you'd like
- to share with our audience?
- Yeah. So, I mean, so I I finished
- my PhD last year, actually, and it was
- mostly related in kind of causality for quantum
- field theory. So the mathematical framework of of
- our best theory of quantum mechanics. And now
- I'm pivoting into
- actually kind of more applications for quantum computers.
- So hopefully later this year, I'll have some
- papers out that are gonna be about the
- kind of algorithms that we can use to
- simulate
- quantum field theories
- on qubits. So hopefully, stay tuned for that.
- And I think, I mean, I think we've
- got a good careers message here. I mean,
- we we love to talk about careers
- on the Physics World podcast.
- And,
- you know, it sounds to me that,
- you you can do particle physics.
- And then, I mean, maybe you're not going
- to do this or maybe you will, that
- you can move into a career
- in quantum computing
- fairly seamlessly. I mean, is that right? Would
- you I mean, if somebody came to you
- and said, well, you know, I'm really interested
- in particle physics,
- but I wanna get a job at a
- quantum computing company. I mean, it sounds to
- me like,
- doing a PhD in particle physics be a
- good idea.
- Yeah. Yeah. I mean, the the boundaries are
- definitely getting more and more blurred as time
- goes on, as we've discussed the ideas of
- AI, machine learning, even qubits starting to be
- applied in particle physics. And so it becomes
- very interdisciplinary.
- And, actually, my my masters that I did
- before my PhD
- was half in quantum computing and half in
- particle physics. And so I always kind of
- balanced it because I'm interested in both. And
- so I've I've managed to be be very
- lucky in that I get to study both
- fields simultaneously. But, yeah, you can absolutely
- absolutely pivot.
- Well, that's great, Ross. Well, thanks again for
- coming on the podcast. Thank you very much
- for having me.
- That was Ross Jenkinson of the University of
- Manchester.
- Thanks, Ross, for a fascinating discussion.
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