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How to engineer a quantum wavefunction

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Peter Evans examines methods for designing and manipulating quantum wavefunctions to achieve desired quantum states and properties in experimental setups.

Image courtesy of interviewee. September 25, 2024

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This video discusses a collaborative research project involving two co-authors, focused on exploring black holes through laboratory experiments. The authors reflect on a conversation about the validity and implications of tabletop experiments that simulate black hole dynamics, particularly using fluid dynamics and water to represent phenomena around black holes. They question how insights from these experiments can apply to quantum systems, given the complexities and challenges associated with studying such phenomena directly in nature.

The discussion includes the importance of inferential strength in scientific experimentation, emphasizing the need for both internal validation (verifying models in controlled conditions) and external validation (confirming findings in varied conditions). The authors draw parallels between quantum simulations and classical experiments, noting that while both seek to model systems for understanding, they face different challenges due to the material types involved.

The paper also touches on the hopeful future of quantum computing and the potential for analog quantum simulations to push the boundaries of scientific understanding, while cautioning that these simulations provide weaker inferential links compared to direct experimental access.

Overall, they conclude that there may be a blurred line between what we know about systems through experimentation and the systems’ inherent properties. The philosophical implications of this uncertainty in understanding quantum systems are discussed, highlighting the intertwining of knowledge and the nature of reality in the quantum realm, especially as technological advances develop further.

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Here are five key points from the video:

1. **Collaboration and Motivation**: The work mentioned in the video is a collaborative effort involving two co-authors, Dominic and Karim, with a foundation in discussions about conducting tabletop experiments to simulate black holes. This project aims to provide insights into quantum systems through analogous experiments.

2. **Wave Function Engineering**: The focus of the research is on "wave function engineering," which pertains to manipulating quantum systems in the lab to better understand and derive information about other quantum systems that cannot be directly observed. This concept emphasizes the engineering aspect of quantum systems and how they can inform about complex behaviors beyond laboratory constraints.

3. **Universal Quantum Computing**: The video discusses the global efforts underway to develop universal quantum computers, with expectations that significant advancements may occur within the next 20–30 years. The potential of quantum computing is highlighted as a means to model and understand complex quantum behavior with greater precision.

4. **Analog Simulation vs. Conventional Experiments**: There's a distinction made between conventional scientific experiments (which rely on direct interaction with material systems) and analog simulations (which represent different systems through analog models). While analog simulations might provide weaker inferences, they are considered valuable because they still reflect the underlying quantum characteristics that connect various systems.

5. **Philosophical Considerations**: The video delves into the philosophical implications of scientific experimentation, particularly regarding the nature of knowledge about systems versus the systems themselves. It raises questions about the assumptions of uniformity in nature and suggests that, in both analog simulations and conventional experiments, the distinction between our knowledge and the actuality of systems may not be as clear-cut as traditionally thought.

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Here are some key quotes from the video you :

1. **On Motivation and Collaboration:**
- "What motivated this was a chat between Dominic and I, which is, uh, quite a number of years ago now."

2. **On Experiments Related to Black Holes:**
- "We had been thinking a lot about a particular problem... about how we might do experiments on black holes."

3. **On Tabletop Experiments:**
- "These were tabletop experiments in the lab... where water was used to simulate what we think might be going on in the space time around a black hole."

4. **On the Debate Over Experimental Validity:**
- "There was this debate about whether these tabletop fluid dynamic experiments really did represent a black hole."

5. **On Scientific Inquiry:**
- "We started looking like, well, what goes on when you go into the lab and you start doing these experiments, what is it that you're doing?"

6. **On Quantum Analog Simulations:**
- "We think the same scenario was arising in... analog quantum simulations... might provide us insight to what's going on in the black hole case."

7. **On Wave Function Engineering:**
- "We think that when you go into the lab and you start trying to manipulate a quantum system, how might you go about manipulating that quantum system in order to tell you about other quantum systems?"

8. **On the Future of Quantum Computing:**
- "We expect that this is probably gonna come about in the next 20 or 30 years, maybe even sooner if some of these breakthroughs really come through fast."

9. **On Uniformity in Nature:**
- "We need to assume some sort of uniformity in nature in order to make our scientific inferences about what goes on in the world."

10. **On Accessing Target Systems:**
- "If we had access, we would just go and do the experiments; we would just do a conventional experiment."

11. **On the Relationship Among Quantum Systems:**
- "Even though we have different material types, we might have a superconductor... what underpins these inferences in analog quantum simulation is that they're all quantum systems."

12. **On the Philosophical Implications:**
- "Is there really such a distinction between what we know about the system and the way that the system is independently of us knowing it?"

13. **On the Significance of Analog Simulations:**
- "What we really need is quantum technology that is pushing us into realms that we haven't yet observed."

14. **On the Nature of Knowledge in Science:**
- "There isn't a clear distinction between what we know about the system and what is actually the case in the system."

These quotes encapsulate the main themes, which include the exploration of black holes through experiments, the use of analog quantum simulations, collaborations in scientific research, and the philosophical implications of scientific inquiry and knowledge.

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Here are some suggested articles that align well with the themes and content of the video you regarding the exploration of black holes through laboratory experiments, quantum physics, and the philosophy of science:

1. **"Laboratory Analogues of Black Holes"** - This article might cover how researchers create analog systems in the lab that mimic black hole behaviors, similar to the tabletop experiments mentioned in your video.

2. **"Exploring Quantum Simulation: From Many-Body Systems to Black Hole Physics"** - This article could discuss the use of quantum simulators to understand complex phenomena, comparing their applications in both quantum systems and black hole dynamics.

3. **"Fluid Dynamics as a Model for Gravitational Phenomena"** - An exploration of how fluid dynamics experiments can be designed to simulate conditions analogous to those near black holes, reflecting on the challenges of modeling gravity.

4. **"The Philosophical Implications of Quantum Mechanics and Experimentation"** - This piece might delve into the philosophical considerations related to experimental validation and the nature of reality as it relates to quantum theory.

5. **"The Role of Inferential Strength in Scientific Research"** - An examination of the importance of internal and external validation in scientific studies, drawing from various fields, including physics and engineering.

6. **"Quantum Computing and the Future of Scientific Discovery"** - This article could provide insights into how advancements in quantum computing might impact experimental physics, including the simulation of complex systems.

7. **"Between Classical and Quantum Worlds: Understanding Complex Systems"** - A discussion on the bridge between classical experiments and quantum simulations, focusing on the different challenges researchers face in each domain.

8. **"Analog Quantum Simulations: Advancements and Limitations"** - A critical look at the capabilities and shortcomings of analog simulations in quantum mechanics, including implications for understanding fundamental physical laws.

These suggested articles would provide a deeper examination of the themes found in the original video, offering perspectives on experimental methodologies, the philosophical aspects of scientific knowledge, and the exciting developments in understanding black holes and quantum systems.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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This work is with two co-authors, Dominic at,

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university of Maryland and Karim Tebo at the

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University of Bristol.

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What motivated, this was a chat between Dominic

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and I, which is, quite a number of years ago now.

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Um, we, along with,

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with Karim had been thinking a lot about

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a particular problem, that got a bit

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of attention over a period of about 10 years, about,

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how we might do experiments on black holes.

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And these were tabletop experiments in the lab.

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Um, you know, there's, there's, one in particular

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in the United Kingdom, where,

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water was used to, simulate

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what we think might be going on

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in the space time around a black hole.

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And so there was this debate about whether these tabletop,

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fluid,

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dynamic experiments really did represent a black hole.

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And the debate was kind of getting to a point where,

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you know, there was kind of, it was about the nitty gritty

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of what goes on in the process of science.

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And so Dominic and I were talking about this

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and we said, look, we think the same scenario was arising

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in, quantum analog, sorry,

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analog quantum simulations, that,

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might give us a bit of insight into what goes on in the,

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quantum case.

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It might provide us insight

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to what's going on in the black hole case.

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And so we started thinking about, well, what does go on,

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like when scientists go, so Dominic worked in a lab,

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and I'm a philosopher of physics.

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And so we started looking like, well,

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what goes on when you go into the lab

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and you start doing these,

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these experiments, what is it that you're doing?

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And where do you think you get inferential strength

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between the things that are being analyzed in the lab

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and the things that you wanna be saying something about

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which are not in the lab, they're somewhere else,

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but they're still kind of, they're very useful,

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you know, materials or systems or whatever.

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It has to be engineered systems sometimes.

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So how do we say something from the

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lab to the things that are going on?

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So that's where it started. And then we,

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we started trying to build that.

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And then, you know, this, this,

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paper about wave function engineering is

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where we ended up, where it comes from.

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It's actually a colleague

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of mine here at the, on campus.

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His name's Andrew White.

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Uh, he works in these, concept simulations.

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And he said that he would love on his door to have,

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you know, wave function engineer.

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'cause that's what he does. What he, he doesn't deal

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with photons and he doesn't deal

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with, you know, other concept.

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He deals with wave functions.

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He's trying to engineer the wave function.

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And I was like, Hey, that's a really cool phrase

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to describe what's going on here.

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So really what we mean is when you go into the lab

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and you start trying to manipulate a quantum system,

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how might you go about

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Manipulating that quantum system in order

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to tell you about other quantum systems?

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And so the engineering part is the manipulating part.

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It's not just in the lab, kind

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of you've engineered this particular system here.

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How can then help that help you to then engineer, you know,

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future quantum technologies or so on and so forth.

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Now, it's probably important to point out at this,

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this juncture that there's,

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quite the effort going on globally to develop,

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a universal quantum computer.

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There are efforts all over the world,

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and now private industry is getting involved,

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particularly in the United States.

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Um, we, we expect

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that this is probably gonna come about in the next 20

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or 30 years, maybe even sooner if some

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of these breakthroughs, really come through fast.

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But that's a universal quantum computer.

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And then when we look at universal classical computers,

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what came before classical computers were analog computers.

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And so we had things like the, the, what was it called,

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the differential analyzer, which was a series of tubes

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that let you do an integration on some function

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through this analog, computation.

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So we had plenty of analog compu, plenty of analog computers

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that were doing the job

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of computers except they were bespoke.

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They could only deal with one particular calculation

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or one particular problem.

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That was then we, you know,

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before we got the, the digital computer,

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which was universal, it could then deal with any problem.

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We fed it, you know, so it was kind of turing's,

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invention basically.

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So we're in the same scenario here.

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We've got a universal quantum computer, probably, you know,

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the next couple of decades can we be using

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analog computation in order

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to tell us about quantum systems?

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And so can we use quantum systems themselves to run

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through some computation about quantum behavior in the lab

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that we can then, you know, analyze, manipulate,

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draw conclusions from,

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and will that then tell us about the behavioral quantum

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systems that we can't manipulate?

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How are these behaving in scenarios

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where we don't have access, whether they're,

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overly complex or, intractable

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or, you know, just

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beyond our modeling capabilities in some sort,

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in some sense, this is something that we think has come out

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of this, of this project

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that was maybe a little bit unexpected.

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We expected there to be, a big difference.

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And I think what we found is that it's a difference in,

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in kind, well, sorry, it's a, it's, yeah, yeah.

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It's a, it's a difference in degree rather

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than a difference in kind.

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So in a conventional experiment,

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we take some system in the lab

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and we, generically, think

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that this system will be standing in for

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every instance of this kind of thing out in the world.

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So whatever our object might be, it might be some photons,

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passing through some polarizers.

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And we think that the photons

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that are sitting there in the lab represent every photon

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that we, that, that we might encounter out in the world.

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And so we have a model for how these photons might behave,

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and we work in the lab to verify our

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local model for how these photons might behave.

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And we call that internal validation.

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So we verify that this is a valid, link between

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the photons in the lab and the model that we've got,

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but then we need to do some work

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to work out whether these photons in the lab stand in

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for every photon there is out in the world.

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We call that external validation.

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And we do that by going

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and conducting experiments in different scenarios,

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in different background conditions, in different,

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spatial locations

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and over time over for different temporal locations.

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And we build confidence that

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what we're doing in the lab really does stand in

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for the things that are going on in the world.

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And there's something deeply philosophical about this

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that goes all the way back to David Hume

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and the problem of induction.

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And that is, we need

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to assume some sort of uniformity in nature in order

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to make our scientific inferences about

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what goes on in, in the world.

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There are various types of uniformity principles.

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So one is we expect, nature to be uniform across space.

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So we think that the, experiments

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that are conducted here in Queensland

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will show us the same properties as the experiments

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that will be conducted in the United Kingdom.

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Uh, we think that there,

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there is some uniformity across time

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that the experiments conducted now will show us the same

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as the experiments conducted 10 years in the future,

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or 50 years in the past.

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But what underpins conventional experimentation is there's

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this, assumption

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that there's uniformity across the material nature

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of the things that we, that we, study.

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So we think that these photons are uniform

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with all the other photons or this, you know, hydrogen,

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I'm doing some, spectroscopy on the,

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on a hydrogen atom.

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And I think that that hydrogen is the same as the hydrogen

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that I see in a, in a far distance star when I do the

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spectroscopy on that hydrogen

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and try to tell something about the redshift

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that's going on from, you know, from us to that, to

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that star or galaxy or whatever, whatever it happens to be.

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So that's what happens in conventional experiments.

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We think that the material is the same.

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And so we have grounds for believing that the,

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the experiments are externally valid.

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But when it comes to, analog simulation, well,

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what we are really trying to do is have some system of some

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material type stand in for the system

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of the other material type.

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And the reason that we would want to do that is

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because we don't have access to the target system.

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So we, if we had access, we would just go

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and do the experiments, we would

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just do a conventional experiment.

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We don't have access. So we need

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to do these analog experiments.

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And so in, in, quantum mechanics

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and the sorts of examples

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that we look at in our paper about way function engineering

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show that there are, a great variety of material types

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that underpin these quantum analog simulations.

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I mean, when it comes to, say, simulations of a black hole

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by a water bath with, you know, certain fluid flow,

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we really don't think that the black hole is, you know,

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somehow a, a fluid or it's water

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or something like that, right?

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So these are different material types.

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And part of what we think is interesting in the quantum case

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is that even though we have different material types,

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we might have a superconductor, with boons that are,

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made up of coupled cooper pairs,

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or we might have a magnetic material,

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that has a particular coupled spin states.

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Um, and we might call those, you know, they,

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they get called kind of trip trip ones in,

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in the example that we look at, right?

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There's a, these dimer magnets, these dimer couple,

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spin states.

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They're very different.

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One is a magnetic spins in a magnetic,

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material while the other is, cooper pairs.

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They're kind of, electron

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and then a phone on that couple across distances in a

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superconductor, right?

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And then we've got say, the experiments

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that we do in the lab, in the lab, we have this lattice of,

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sorry, an optical lattice of different,

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you know,

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potentials in which some Adam might be able to sit.

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So we have say a rubidium ato,

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and then we do the experiments here,

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and then we wanna say something about the superconductor,

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or we wanna say something about the magnetic material.

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And they're not the same material type,

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but what we think underpins these inferences in

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analog quantum simulation is

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that they're all quantum systems.

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And so in a sense, the, the empirical access we have to all

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of them has a very similar structure

11:29.775 --> 11:30.835
to it, right?

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So we, we deal with the quantum system,

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we know it obey certain laws in the lab, we want it

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to stand in for these other material types.

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These, you know, there superconductor or a, or a magnet.

11:43.575 --> 11:47.115
Um, or the other example we had is a of helium fluid that

11:47.185 --> 11:49.835
that forms a ozone constant condensate.

11:49.835 --> 11:52.665
So these super fluids, we wanted to

11:52.665 --> 11:54.485
Stand in for these other systems.

11:55.225 --> 11:57.285
Um, and the reason we have confidence

11:57.285 --> 11:59.885
that they might be telling us something about these other

11:59.885 --> 12:01.525
systems is that they're all quantum systems.

12:01.865 --> 12:06.245
We certainly don't wanna say that these sorts

12:06.245 --> 12:10.005
of analog quantum simulations give us the direct

12:10.205 --> 12:13.765
experimental access that conventional experiments get us.

12:13.825 --> 12:16.525
And so these inferences are far weaker that we do,

12:16.675 --> 12:19.645
that we make in, analog simulation.

12:20.225 --> 12:22.645
But here we think that the quantum version

12:22.705 --> 12:24.965
of the analog simulations are stronger

12:25.515 --> 12:27.525
than regular analog simulations

12:27.525 --> 12:31.205
because they get underpinned by this inferential structure

12:31.205 --> 12:33.805
that we can draw between, you know,

12:34.355 --> 12:36.125
this quantum system and that quantum system.

12:36.155 --> 12:37.325
They're all quantum systems.

12:37.385 --> 12:40.445
So what we are really doing is manipulating a wave function

12:40.445 --> 12:42.325
here, and we think it's standing in

12:42.325 --> 12:43.445
for the wave function over there.

12:43.445 --> 12:45.285
That's how we get back to this idea of

12:45.985 --> 12:49.675
how way function engineering the potential really comes back

12:49.695 --> 12:53.435
to this idea about, quantum computation.

12:54.295 --> 12:56.995
So the potential here is that we can now

12:58.055 --> 13:02.465
push the boundaries of what is possible

13:03.005 --> 13:04.985
to observe and manipulate in the world

13:05.765 --> 13:10.225
and push those boundaries in a way that's not just,

13:11.085 --> 13:13.585
kind of ungrounded to

13:14.245 --> 13:17.225
the typical access we get to the world.

13:17.655 --> 13:21.465
It's grounded precisely in a particular access that we get

13:21.765 --> 13:23.145
to a system in a lab.

13:23.565 --> 13:28.385
So we are grounded in, you know, the, the,

13:28.975 --> 13:32.825
empirical, nature of our access to the world,

13:33.085 --> 13:36.065
but we are now pushing the boundaries of where we think

13:36.095 --> 13:39.425
that empirical access we have to the world reaches.

13:39.885 --> 13:44.675
So that's the really nice, potential of these is

13:44.795 --> 13:46.795
to really push those boundaries beyond

13:46.825 --> 13:49.515
what is currently technologically possible.

13:50.015 --> 13:53.035
And I guess what's what, what would be nice is if

13:53.575 --> 13:57.625
as our technological capabilities, increase

13:57.965 --> 14:00.225
as we get more quantum technology, to be honest,

14:00.335 --> 14:04.225
what we really need is quantum technology that is pushing us

14:04.895 --> 14:07.265
into realms that we haven't yet observed.

14:07.795 --> 14:10.425
We'll be able to then back check the way

14:10.455 --> 14:13.105
that our analog simulations have been telling us what's

14:13.105 --> 14:14.865
going on in the world and say, Hey, look,

14:14.865 --> 14:18.545
we got these inferences right now with that sort

14:18.545 --> 14:19.825
of verification.

14:20.045 --> 14:22.745
If we can do that back checking about whether we got those

14:22.745 --> 14:24.825
inferences right, we can gain more

14:24.825 --> 14:27.945
and more confidence that the particular inferential

14:28.145 --> 14:30.545
structure we are using in say,

14:30.545 --> 14:33.665
analog quantum simulation is a good inferential structure,

14:34.075 --> 14:35.625
which will let us, you know,

14:35.625 --> 14:36.985
push the boundaries further in the future.

14:38.025 --> 14:39.505
I mean, I guess what's, you know,

14:40.145 --> 14:42.265
ultimately one day we're gonna get a universal quantum

14:42.625 --> 14:43.865
computer, you know, fingers crossed.

14:44.245 --> 14:45.785
And when that happens, a lot

14:45.785 --> 14:48.145
of these techniques are probably gonna lose their

14:48.145 --> 14:50.705
effectiveness because we can just pump it through a computer

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And say, right, we wanna model a superconductor

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or we wanna model a super fluid,

14:55.135 --> 14:59.685
let's just put the model into a, a quantum computer

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and it will tell us exactly what's going on or,

15:03.185 --> 15:05.325
or within the modeling parameters, what's going on.

15:05.505 --> 15:06.885
So they might lose their effectiveness.

15:06.945 --> 15:10.925
But I think the lingering lesson that we get from this is

15:10.925 --> 15:15.255
that actually conventional experiments need

15:15.255 --> 15:18.055
to rely on the same kind of inferential structure.

15:18.805 --> 15:21.375
It's just, it's a more solid grounding

15:21.375 --> 15:23.975
because of the access we have to the target system.

15:24.715 --> 15:28.975
But it raises this question about, the difference,

15:29.115 --> 15:30.495
and this is kind of the, the deeply

15:30.495 --> 15:32.335
philosophical part of it, right?

15:32.875 --> 15:35.615
The difference between what we know about the system

15:35.995 --> 15:38.935
and what the system actually consists in

15:39.715 --> 15:42.095
and what these analog experiments are showing us.

15:42.115 --> 15:46.055
So actually that distinction is really vague.

15:46.555 --> 15:49.435
Um, there isn't a clear distinction between

15:49.435 --> 15:50.595
what we know about the system

15:50.735 --> 15:52.835
and what is actually the case in the system.

15:53.495 --> 15:55.155
And if we want to be radical

15:55.335 --> 15:58.195
or speculative, we can draw the lessons back from that

15:58.195 --> 16:01.235
and say, actually in conventional experimentation,

16:01.465 --> 16:04.435
even when we can manipulate the target system,

16:04.855 --> 16:06.955
is there really such a distinction between

16:07.665 --> 16:08.955
what we know about the system

16:09.095 --> 16:10.435
and the way that the system is

16:10.795 --> 16:12.035
independently of us knowing it?

16:12.575 --> 16:15.155
And a lot of, there's a lot of people that have been kind

16:15.155 --> 16:19.315
of messaging on this in the quantum information space,

16:19.905 --> 16:22.435
that in quantum information maybe there really isn't a

16:22.435 --> 16:26.305
distinction between the quantum part of the world

16:26.325 --> 16:27.345
and the information part.

16:27.345 --> 16:29.185
The information part is the part that's,

16:29.485 --> 16:30.545
that's more about us

16:30.645 --> 16:32.185
and how we access the quantum world,

16:32.535 --> 16:35.345
what really is the quantum world beyond the information

16:35.345 --> 16:36.465
that we have about it.

16:37.045 --> 16:39.025
Um, so look, that's the speculative,

16:39.025 --> 16:40.225
deep philosophical part.

16:40.225 --> 16:42.825
You know, we don't need to go into too much if, if it

16:42.825 --> 16:44.105
or, or depth on that.

16:44.605 --> 16:48.865
Um, but you know, that's a nice legacy even if we think, say

16:49.415 --> 16:51.665
quantum computing is going to, future,

16:51.665 --> 16:55.505
quantum computing is going to, lessen the importance

16:55.685 --> 16:58.345
or effectiveness of these sorts of analog simulations.

00:00 - This work is with two co-authors, Dominic at, university of Maryland and Karim Tebo at the University of Bristol. What motivated, this was a chat between Dominic and I, which is, quite a number of years ago now. we, along with,

00:14 - with Karim had been thinking a lot about a particular problem, that got a bit of attention over a period of about 10 years, about, how we might do experiments on black holes. And these were tabletop experiments in the lab. you know, there's, there's, one in particular in the United Kingdom, where, water was used to, simulate what we think might be going on in the space time around a black hole.

00:53 - And so there was this debate about whether these tabletop, fluid, dynamic experiments really did represent a black hole. And the debate was kind of getting to a point where, you know, there was kind of, it was about the nitty gritty of what goes on in the process of science. And so Dominic and I were talking about this and we said, look, we think the same scenario was arising in, quantum analog, sorry, analog quantum simulations, that,

01:24 - might give us a bit of insight into what goes on in the, quantum case. It might provide us insight to what's going on in the black hole case. And so we started thinking about, well, what does go on, like when scientists go, so Dominic worked in a lab, and I'm a philosopher of physics. And so we started looking like, well, what goes on when you go into the lab and you start doing these,

01:46 - these experiments, what is it that you're doing? And where do you think you get inferential strength between the things that are being analyzed in the lab and the things that you wanna be saying something about which are not in the lab, they're somewhere else, but they're still kind of, they're very useful, you know, materials or systems or whatever. It has to be engineered systems sometimes. So how do we say something from the lab to the things that are going on?

02:13 - So that's where it started. And then we, we started trying to build that. And then, you know, this, this, paper about wave function engineering is where we ended up, where it comes from. It's actually a colleague of mine here at the, on campus. His name's Andrew White. he works in these, concept simulations. And he said that he would love on his door to have,

02:35 - you know, wave function engineer. 'cause that's what he does. What he, he doesn't deal with photons and he doesn't deal with, you know, other concept. He deals with wave functions. He's trying to engineer the wave function. And I was like, Hey, that's a really cool phrase to describe what's going on here. So really what we mean is when you go into the lab and you start trying to manipulate a quantum system,

02:57 - how might you go about Manipulating that quantum system in order to tell you about other quantum systems? And so the engineering part is the manipulating part. It's not just in the lab, kind of you've engineered this particular system here. How can then help that help you to then engineer, you know, future quantum technologies or so on and so forth. Now, it's probably important to point out at this, this juncture that there's,

03:27 - quite the effort going on globally to develop, a universal quantum computer. There are efforts all over the world, and now private industry is getting involved, particularly in the United States. we, we expect that this is probably gonna come about in the next 20 or 30 years, maybe even sooner if some of these breakthroughs, really come through fast. But that's a universal quantum computer.

03:53 - And then when we look at universal classical computers, what came before classical computers were analog computers. And so we had things like the, the, what was it called, the differential analyzer, which was a series of tubes that let you do an integration on some function through this analog, computation. So we had plenty of analog compu, plenty of analog computers that were doing the job of computers except they were bespoke. They could only deal with one particular calculation

04:25 - or one particular problem. That was then we, you know, before we got the, the digital computer, which was universal, it could then deal with any problem. We fed it, you know, so it was kind of turing's, invention basically. So we're in the same scenario here. We've got a universal quantum computer, probably, you know, the next couple of decades can we be using analog computation in order

04:53 - to tell us about quantum systems? And so can we use quantum systems themselves to run through some computation about quantum behavior in the lab that we can then, you know, analyze, manipulate, draw conclusions from, and will that then tell us about the behavioral quantum systems that we can't manipulate? How are these behaving in scenarios where we don't have access, whether they're, overly complex or, intractable

05:25 - or, you know, just beyond our modeling capabilities in some sort, in some sense, this is something that we think has come out of this, of this project that was maybe a little bit unexpected. We expected there to be, a big difference. And I think what we found is that it's a difference in, in kind, well, sorry, it's a, it's, yeah, yeah. It's a, it's a difference in degree rather than a difference in kind.

05:55 - So in a conventional experiment, we take some system in the lab and we, generically, think that this system will be standing in for every instance of this kind of thing out in the world. So whatever our object might be, it might be some photons, passing through some polarizers. And we think that the photons that are sitting there in the lab represent every photon that we, that, that we might encounter out in the world.

06:29 - And so we have a model for how these photons might behave, and we work in the lab to verify our local model for how these photons might behave. And we call that internal validation. So we verify that this is a valid, link between the photons in the lab and the model that we've got, but then we need to do some work to work out whether these photons in the lab stand in for every photon there is out in the world. We call that external validation.

07:00 - And we do that by going and conducting experiments in different scenarios, in different background conditions, in different, spatial locations and over time over for different temporal locations. And we build confidence that what we're doing in the lab really does stand in for the things that are going on in the world. And there's something deeply philosophical about this that goes all the way back to David Hume

07:24 - and the problem of induction. And that is, we need to assume some sort of uniformity in nature in order to make our scientific inferences about what goes on in, in the world. There are various types of uniformity principles. So one is we expect, nature to be uniform across space. So we think that the, experiments that are conducted here in Queensland will show us the same properties as the experiments

07:54 - that will be conducted in the United Kingdom. we think that there, there is some uniformity across time that the experiments conducted now will show us the same as the experiments conducted 10 years in the future, or 50 years in the past. But what underpins conventional experimentation is there's this, assumption that there's uniformity across the material nature of the things that we, that we, study.

08:20 - So we think that these photons are uniform with all the other photons or this, you know, hydrogen, I'm doing some, spectroscopy on the, on a hydrogen atom. And I think that that hydrogen is the same as the hydrogen that I see in a, in a far distance star when I do the spectroscopy on that hydrogen and try to tell something about the redshift that's going on from, you know, from us to that, to that star or galaxy or whatever, whatever it happens to be.

08:47 - So that's what happens in conventional experiments. We think that the material is the same. And so we have grounds for believing that the, the experiments are externally valid. But when it comes to, analog simulation, well, what we are really trying to do is have some system of some material type stand in for the system of the other material type. And the reason that we would want to do that is because we don't have access to the target system.

09:15 - So we, if we had access, we would just go and do the experiments, we would just do a conventional experiment. We don't have access. So we need to do these analog experiments. And so in, in, quantum mechanics and the sorts of examples that we look at in our paper about way function engineering show that there are, a great variety of material types that underpin these quantum analog simulations.

09:40 - I mean, when it comes to, say, simulations of a black hole by a water bath with, you know, certain fluid flow, we really don't think that the black hole is, you know, somehow a, a fluid or it's water or something like that, right? So these are different material types. And part of what we think is interesting in the quantum case is that even though we have different material types, we might have a superconductor, with boons that are, made up of coupled cooper pairs,

10:10 - or we might have a magnetic material, that has a particular coupled spin states. and we might call those, you know, they, they get called kind of trip trip ones in, in the example that we look at, right? There's a, these dimer magnets, these dimer couple, spin states. They're very different. One is a magnetic spins in a magnetic, material while the other is, cooper pairs.

10:38 - They're kind of, electron and then a phone on that couple across distances in a superconductor, right? And then we've got say, the experiments that we do in the lab, in the lab, we have this lattice of, sorry, an optical lattice of different, you know, potentials in which some Adam might be able to sit. So we have say a rubidium ato, and then we do the experiments here,

11:06 - and then we wanna say something about the superconductor, or we wanna say something about the magnetic material. And they're not the same material type, but what we think underpins these inferences in analog quantum simulation is that they're all quantum systems. And so in a sense, the, the empirical access we have to all of them has a very similar structure to it, right? So we, we deal with the quantum system,

11:33 - we know it obey certain laws in the lab, we want it to stand in for these other material types. These, you know, there superconductor or a, or a magnet. or the other example we had is a of helium fluid that that forms a ozone constant condensate. So these super fluids, we wanted to Stand in for these other systems. and the reason we have confidence that they might be telling us something about these other systems is that they're all quantum systems.

12:01 - We certainly don't wanna say that these sorts of analog quantum simulations give us the direct experimental access that conventional experiments get us. And so these inferences are far weaker that we do, that we make in, analog simulation. But here we think that the quantum version of the analog simulations are stronger than regular analog simulations because they get underpinned by this inferential structure that we can draw between, you know,

12:34 - this quantum system and that quantum system. They're all quantum systems. So what we are really doing is manipulating a wave function here, and we think it's standing in for the wave function over there. That's how we get back to this idea of how way function engineering the potential really comes back to this idea about, quantum computation. So the potential here is that we can now push the boundaries of what is possible

13:03 - to observe and manipulate in the world and push those boundaries in a way that's not just, kind of ungrounded to the typical access we get to the world. It's grounded precisely in a particular access that we get to a system in a lab. So we are grounded in, you know, the, the, empirical, nature of our access to the world, but we are now pushing the boundaries of where we think that empirical access we have to the world reaches.

13:39 - So that's the really nice, potential of these is to really push those boundaries beyond what is currently technologically possible. And I guess what's what, what would be nice is if as our technological capabilities, increase as we get more quantum technology, to be honest, what we really need is quantum technology that is pushing us into realms that we haven't yet observed. We'll be able to then back check the way that our analog simulations have been telling us what's

14:13 - going on in the world and say, Hey, look, we got these inferences right now with that sort of verification. If we can do that back checking about whether we got those inferences right, we can gain more and more confidence that the particular inferential structure we are using in say, analog quantum simulation is a good inferential structure, which will let us, you know, push the boundaries further in the future.

14:38 - I mean, I guess what's, you know, ultimately one day we're gonna get a universal quantum computer, you know, fingers crossed. And when that happens, a lot of these techniques are probably gonna lose their effectiveness because we can just pump it through a computer And say, right, we wanna model a superconductor or we wanna model a super fluid, let's just put the model into a, a quantum computer and it will tell us exactly what's going on or,

15:03 - or within the modeling parameters, what's going on. So they might lose their effectiveness. But I think the lingering lesson that we get from this is that actually conventional experiments need to rely on the same kind of inferential structure. It's just, it's a more solid grounding because of the access we have to the target system. But it raises this question about, the difference, and this is kind of the, the deeply philosophical part of it, right?

15:32 - The difference between what we know about the system and what the system actually consists in and what these analog experiments are showing us. So actually that distinction is really vague. there isn't a clear distinction between what we know about the system and what is actually the case in the system. And if we want to be radical or speculative, we can draw the lessons back from that and say, actually in conventional experimentation,

16:01 - even when we can manipulate the target system, is there really such a distinction between what we know about the system and the way that the system is independently of us knowing it? And a lot of, there's a lot of people that have been kind of messaging on this in the quantum information space, that in quantum information maybe there really isn't a distinction between the quantum part of the world and the information part.

16:27 - The information part is the part that's, that's more about us and how we access the quantum world, what really is the quantum world beyond the information that we have about it. so look, that's the speculative, deep philosophical part. You know, we don't need to go into too much if, if it or, or depth on that. but you know, that's a nice legacy even if we think, say

16:49 - quantum computing is going to, future, quantum computing is going to, lessen the importance or effectiveness of these sorts of analog simulations.

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