Monthly Archives: January 2026

Has quantum advantage been achieved?

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Recently, I gave a couple of perspective talks on quantum advantage, one at the annual retreat of the CIQC and one at a recent KITP programme. I started off by polling the audience on who believed quantum advantage had been achieved. Just this one, simple question.

The audience was mostly experimental and theoretical physicists with a few CS theory folks sprinkled in. I was sure that these audiences would be overwhelmingly convinced of the successful demonstration of quantum advantage. After all, more than half a decade has passed since the first experimental claim (G1) of “quantum supremacy” as the patron of this blog’s institute called the idea “to perform tasks with controlled quantum systems going beyond what can be achieved with ordinary digital computers” (Preskill, p. 2) back in 2012. Yes, this first experiment by the Google team may have been simulated in the meantime, but it was only the first in an impressive series of similar demonstrations that became bigger and better with every year that passed. Surely, so I thought, a significant part of my audiences would have been convinced of quantum advantage even before Google’s claim, when so-called quantum simulation experiments claimed to have performed computations that no classical computer could do (e.g. (qSim)).

I could not have been more wrong.

In both talks, less than half of the people in the audience thought that quantum advantage had been achieved.

In the discussions that ensued, I came to understand what folks criticized about the experiments that have been performed and even the concept of quantum advantage to begin with. But more on that later. Most of all, it seemed to me, the community had dismissed Google’s advantage claim because of the classical simulation shortly after. It hadn’t quite kept track of all the advances—theoretical and experimental—since then.

In a mini-series of three posts, I want to remedy this and convince you that the existing quantum computers can perform tasks that no classical computer can do. Let me caution, though, that the experiments I am going to talk about solve a (nearly) useless task. Nothing of what I say implies that you should (yet) be worried about your bank accounts.

I will start off by recapping what quantum advantage is and how it has been demonstrated in a set of experiments over the past few years.

Part 1: What is quantum advantage and what has been done?

To state the obvious: we are now fairly convinced that noiseless quantum computers would be able solve problems efficiently that no classical computer could solve. In fact, we have been convinced of that already since the mid-90ies when Lloyd and Shor discovered two basic quantum algorithms: simulating quantum systems and factoring large numbers. Both of these are tasks where we are as certain as we could be that no classical computer can solve them. So why talk about quantum advantage 20 and 30 years later?

The idea of a quantum advantage demonstration—be it on a completely useless task even—emerged as a milestone for the field in the 2010s. Achieving quantum advantage would finally demonstrate that quantum computing was not just a random idea of a bunch of academics who took quantum mechanics too seriously. It would show that quantum speedups are real: We can actually build quantum devices, control their states and the noise in them, and use them to solve tasks which not even the largest classical supercomputers could do—and these are very large.

What is quantum advantage?

But what exactly do we mean by “quantum advantage”. It is a vague concept, for sure. But some essential criteria that a demonstration should certainly satisfy are probably the following.

  1. The quantum device needs to solve a pre-specified computational task. This means that there needs to be an input to the quantum computer. Given the input, the quantum computer must then be programmed to solve the task for the given input. This may sound trivial. But it is crucial because it delineates programmable computing devices from just experiments on any odd physical system.
  2. There must be a scaling difference in the time it takes for a quantum computer to solve the task and the time it takes for a classical computer. As we make the problem or input size larger, the difference between the quantum and classical solution times should increase disproportionately, ideally exponentially.
  3. And finally: the actual task solved by the quantum computer should not be solvable by any classical machine (at the time).

Achieving this last criterion using imperfect, noisy quantum devices is the challenge the idea of quantum supremacy set for the field. After all, running any of our favourite quantum algorithms in a classically hard regime on these devices is completely out of the question. They are too small and too noisy. So the field had to come up with the conceivably smallest and most noise-robust quantum algorithm that has a significant scaling advantage against classical computation.

Random circuits are really hard to simulate!

The idea is simple: we just run a random computation, constructed in a way that is as favorable as we can make it to the quantum device while being as hard as possible classically. This may strike as a pretty unfair way to come up with a computational task—it is just built to be hard for classical computers without any other purpose. But: it is a fine computational task. There is an input: the description of the quantum circuit, drawn randomly. The device needs to be programmed to run this exact circuit. And there is a task: just return whatever this quantum computation would return. These are strings of 0s and 1s drawn from a certain distribution. Getting the distribution of the strings right for a given input circuit is the computational task.

This task, dubbed random circuit sampling, can be solved on a classical as well as a quantum computer, but there is a (presumably) exponential advantage for the quantum computer. More on that in Part 2.

For now, let me tell you about the experimental demonstrations of random circuit sampling. Allow me to be slightly more formal. The task solved in random circuit sampling is to produce bit strings x∈{0,1}nx \in \{0,1\}^n distributed according to the Born-rule outcome distribution

pC(x)=|⟨x|C|0⟩|2p_C(x) = | \bra x C \ket {0}|^2

of a sequence of elementary quantum operations (unitary rotations of one or two qubits at a time) which is drawn randomly according to certain rules. This circuit CC is applied to a reference state |0⟩\ket 0 on the quantum computer and then measured, giving the string xx as an outcome.

The breakthrough: classically hard programmable quantum computations in the real world

In the first quantum supremacy experiment (G1) by the Google team, the quantum computer was built from 53 superconducting qubits arranged in a 2D grid. The operations were randomly chosen simple one-qubit gates (X,Y,X+Y\sqrt X, \sqrt Y, \sqrt{X+Y}) and deterministic two-qubit gates called fSim applied in the 2D pattern, and repeated a certain number of times (the depth of the circuit). The limiting factor in these experiments was the quality of the two-qubit gates and the measurements, with error probabilities around 0.6 % and 4 %, respectively.

A very similar experiment was performed by the USTC team on 56 qubits (U1) and both experiments were repeated with better fidelities (0.4 % and 1 % for two-qubit gates and measurements) and slightly larger system sizes (70 and 83 qubits, respectively) in the past two years (G2,U2).

Using a trapped-ion architecture, the Quantinuum team also demonstrated random circuit sampling on 56 qubits but with arbitrary connectivity (random regular graphs) (Q). There, the two-qubit gates were π/2\pi/2-rotations around Z⊗ZZ \otimes Z, the single-qubit gates were uniformly random and the error rates much better (0.15 % for both two-qubit gate and measurement errors).

All the experiments ran random circuits on varying system sizes and circuit depths, and collected thousands to millions of samples from a few random circuits at a given size. To benchmark the quality of the samples, the widely accepted benchmark is now the linear cross-entropy (XEB) benchmark defined as

χ=2n𝔼C𝔼xpC(x)−1,\chi = 2^n \mathbb E_C \mathbb E_{x} p_C(x) -1 ,

for an nn-qubit circuit. The expectation over CC is over the random choice of circuit and the expectation over xx is over the experimental distribution of the bit strings. In other words, to compute the XEB given a list of samples, you ‘just’ need to compute the ideal probability of obtaining that sample from the circuit CC and average the outcomes.

The XEB is nice because it gives 1 for ideal samples from sufficiently random circuits and 0 for uniformly random samples, and it can be estimated accurately from just a few samples. Under the right conditions, it turns out to be a good proxy for the many-body fidelity of the quantum state prepared just before the measurement.

This tells us that we should expect an XEB score of (1−error per gate)# gates∼c−nd(1-\text{error per gate})^{\text{\# gates}} \sim c^{- n d } for some noise- and architecture-dependent constant cc. All of the experiments achieved a value of the XEB that was significantly (in the statistical sense) far away from 0 as you can see in the plot below. This shows that something nontrivial is going on in the experiments, because the fidelity we expect for a maximally mixed or random state is 2−n2^{-n} which is less than 10−1410^{-14} % for all the experiments.

The complexity of simulating these experiments is roughly governed by an exponential in either the number of qubits or the maximum bipartite entanglement generated. Figure 5 of the Quantinuum paper has a nice comparison.

It is not easy to say how much leverage an XEB significantly lower than 1 gives a classical spoofer. But one can certainly use it to judiciously change the circuit a tiny bit to make it easier to simulate.

Even then, reproducing the low scores between 0.05 % and 0.2 % of the experiments is extremely hard on classical computers. To the best of my knowledge, producing samples that match the experimental XEB score has only been achieved for the first experiment from 2019 (PCZ). This simulation already exploited the relatively low XEB score to simplify the computation, but even for the slightly larger 56 qubit experiments these techniques may not be feasibly run. So to the best of my knowledge, the only one of the experiments which may actually have been simulated is the 2019 experiment by the Google team.

If there are better methods, or computers, or more willingness to spend money on simulating random circuits today, though, I would be very excited to hear about it!

Proxy of a proxy of a benchmark

Now, you may be wondering: “How do you even compute the XEB or fidelity in a quantum advantage experiment in the first place? Doesn’t it require computing outcome probabilities of the supposedly hard quantum circuits?” And that is indeed a very good question. After all, the quantum advantage of random circuit sampling is based on the hardness of computing these probabilities. This is why, to get an estimate of the XEB in the advantage regime, the experiments needed to use proxies and extrapolation from classically tractable regimes.

This will be important for Part 2 of this series, where I will discuss the evidence we have for quantum advantage, so let me give you some more detail. To extrapolate, one can just run smaller circuits of increasing sizes and extrapolate to the size in the advantage regime. Alternatively, one can run circuits with the same number of gates but with added structure that makes them classically simulatable and extrapolate to the advantage circuits. Extrapolation is based on samples from different experiments from the quantum advantage experiments. All of the experiments did this.

A separate estimate of the XEB score is based on proxies. An XEB proxy uses the samples from the advantage experiments, but computes a different quantity than the XEB that can actually be computed and for which one can collect independent numerical and theoretical evidence that it matches the XEB in the relevant regime. For example, the Google experiments averaged outcome probabilities of modified circuits that were related to the true circuits but easier to simulate.

The Quantinuum experiment did something entirely different, which is to estimate the fidelity of the advantage experiment by inverting the circuit on the quantum computer and measuring the probability of coming back to the initial state.

All of the methods used to estimate the XEB of the quantum advantage experiments required some independent verification based on numerics on smaller sizes and induction to larger sizes, as well as theoretical arguments.

In the end, the advantage claims are thus based on a proxy of a proxy of the quantum fidelity. This is not to say that the advantage claims do not hold. In fact, I will argue in my next post that this is just the way science works. I will also tell you more about the evidence that the experiments I described here actually demonstrate quantum advantage and discuss some skeptical arguments.

Let me close this first post with a few notes.

In describing the quantum supremacy experiments, I focused on random circuit sampling which is run on programmable digital quantum computers. What I neglected to talk about is boson sampling and Gaussian boson sampling, which are run on photonic devices and have also been experimentally demonstrated. The reason for this is that I think random circuits are conceptually cleaner since they are run on processors that are in principle capable of running an arbitrary quantum computation while the photonic devices used in boson sampling are much more limited and bear more resemblance to analog simulators.

I want to continue my poll here, so feel free to write in the comments whether or not you believe that quantum advantage has been demonstrated (by these experiments) and if not, why.

References

[G1] Arute, F. et al. Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019).

[Preskill] Preskill, J. Quantum computing and the entanglement frontier. arXiv:1203.5813 (2012).

[qSim] Choi, J. et al. Exploring the many-body localization transition in two dimensions. Science 352, 1547–1552 (2016). .

[U1] Wu, Y. et al. Strong Quantum Computational Advantage Using a Superconducting Quantum Processor. Phys. Rev. Lett. 127, 180501 (2021).

[G2] Morvan, A. et al. Phase transitions in random circuit sampling. Nature 634, 328–333 (2024).

[U2] Gao, D. et al. Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor. Phys. Rev. Lett. 134, 090601 (2025).

[Q] DeCross, M. et al. Computational Power of Random Quantum Circuits in Arbitrary Geometries. Phys. Rev. X 15, 021052 (2025).

[PCZ] Pan, F., Chen, K. & Zhang, P. Solving the sampling problem of the Sycamore quantum circuits. Phys. Rev. Lett. 129, 090502 (2022).

Nicole’s guide to interviewing for faculty positions

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Snow is haunting weather forecasts, home owners are taking down Christmas lights, stores are discounting exercise equipment, and faculty-hiring committees are winnowing down applications. In-person interviews often take place between January and March but can extend from December to April. If you applied for faculty positions this past fall and you haven’t begun preparing for interviews, begin. This blog post relates my advice about in-person interviews. It most directly addresses assistant professorships in theoretical physics at R1 North American universities, but the advice generalizes to other contexts. 

Top takeaway: Your interviewers aim to confirm that they’ll enjoy having you as a colleague. They’ll want to take pleasure in discussing a colloquium with you over coffee, consult you about your area of expertise, take pride in your research achievements, and understand you even if your specialty differs from theirs. You delight in learning and sharing about physics, right? Focus on that delight, and let it shine.

Anatomy of an interview: The typical interview lasts for one or two days. Expect each day to begin between 8:00 and 10:00 AM and to end between 7:00 and 8:30 PM. Yes, you’re justified in feeling exhausted just thinking about such a day. Everyone realizes that faculty interviews are draining, including the people who’ve packed your schedule. But fear not, even if you’re an introvert horrified at the thought of talking for 12 hours straight! Below, I share tips for maintaining your energy level. Your interview will probably involve many of the following components:

  • One-on-one meetings with faculty members: Vide infra for details and advice.
  • A meeting with students: Such meetings often happen over lunch or coffee.
  • Scientific talk: Vide infra.
  • Chalk talk: Vide infra.
  • Dinner: Faculty members will typically take you out to dinner. However, as an undergrad, I once joined a student dinner with a faculty candidate. Expect dinner to last a couple of hours, ending between 8:00 and 8:30 PM.
  • Breakfast: Interviews rarely extend to breakfast, in my experience. But I once underwent an interview whose itinerary was so packed, a faculty member squeezed himself onto the schedule by coming to my hotel’s restaurant for banana bread and yogurt.

After receiving the interview invitation, politely request that your schedule include breaks. First, of course, you’ll thank the search-committee chair (who probably issued the invitation), convey your enthusiasm, and opine about possible interview dates. After accomplishing those tasks, as a candidate, I asked that a 5-to-10-minute break separate consecutive meetings and that 30–45 minutes of quiet time precede my talk (or talks). Why? For two reasons.

First, the search committee was preparing to pack my interview day (or days) to the gills. I’d have to talk for about twelve hours straight. And—much as I adore the physics community, adore learning about physics from colleagues, and adore sharing physics—I’m an introvert. Such a schedule exhausts me. It would probably exhaust all but the world champions of extroversion, and few physicists could even qualify for that competition. After nearly every meeting, I’d find a bathroom, close my eyes, and breathe. (I might also peek at my notes about my next interviewee; vide infra.) The alone time replenished my energy.

Second, committees often schedule interviews back to back. Consecutive interviews might take place in different buildings, though, and walking between buildings doesn’t take zero minutes. Also, physicists love explaining their research. Interviewer #1 might therefore run ten minutes over their allotted time before realizing they had to shepherd me to another building in zero minutes. My lateness would disrespect Interviewer #2. Furthermore, many interviews last only 30 minutes each. Given 30 - 10 - (\gtrsim 0) \approx 15 minutes, Interviewer #2 and I could scarcely make each other’s acquaintance. So I smuggled travel time into my schedule.

Feel awkward about requesting breaks? Don’t worry; everyone knows that interview days are draining. Explain honestly, simply, and respectfully that you’re excited about meeting everyone and that breaks will keep you energized throughout the long day.

Research your interviewers: A week before your interview, the hiring committee should have begun drafting a schedule for you. The schedule might continue to evolve until—and during—your interview. But request the schedule a week in advance, and research everyone on it.

When preparing for an interview, I’d create a Word/Pages document with one page per person. On Interviewer X’s page, I’d list relevant information culled from their research-group website, university faculty pages, arXiv page, and Google Scholar page. Does X undertake theoretical or experimental research? Which department do they belong to? Which experimental platform/mathematical toolkit do they specialize in? Which of their interests overlap with which of mine? Which papers of theirs intrigue me most? Could any of their insights inform my research or vice versa? Do we share any coauthors who might signal shared research goals? I aimed to be able to guide a conversation that both X and I would enjoy and benefit from.

Ask your advisors if they know anybody on your schedule or in the department you’re visiting. Advisors know and can contextualize many of their peers. For example, perhaps X grew famous for discovery Y, founded subfield Z, or harbors a covert affection for the foundations of quantum physics. An advisor of yours might even have roomed with X in college.

Prepare an elevator pitch for your research program: Cross my heart and hope to die, the following happened to me when I visited another institution (although not to interview). My host and I stepped into elevator occupied by another faculty member. Our conversation could have served as the poster child for the term “elevator pitch”:

Host: Hi, Other Faculty Member; good to see you. By the way, this is Nicole from Maryland. She’s giving the talk today.

Other Faculty Member: Ah, good to meet you, Nicole. What do you work on?

Be able to answer that question—to synopsize your research program—before leaving the elevator. Feel free start with your subfield: artificial active matter, the many-body physics of quantum information, dark-matter detection, etc. But the subfield doesn’t suffice. Oodles of bright-eyed, bushy-tailed young people study the many-body physics of quantum information. How does your research stand out? Do you apply a unique toolkit? Are you pursuing a unique goal? Can you couple together more qubits than any other experimentalist using the same platform? Make Other Faculty Member think, Ah. I’d like to attend that talk.

Dress neatly and academically: Interview clothing should demonstrate respect, while showing that you understand the department’s culture and belong there. Almost no North American physicists wear ties, even to present colloquia, so I advise against ties. Nor do I recommend suits. 

To those presenting as male, I’d recommend slacks; a button-down shirt; dark shoes (neither sneakers nor patent leather); and a corduroy or knit pullover, a corduroy or knit vest, or a sports jacket. If you prefer a skirt or dress, I’d recommend that it reach at least your knees. Wear comfortable shoes; you’ll stand and walk a great deal. Besides, many interviews take place during the winter, a season replete with snow and mud. I wore knee-height black leather boots that had short, thick heels.

Look the part. Act the part. Help your interviewers envision you in the position you want.

Pack snacks: A student group might whisk you off to lunch at 11:45, but dinner might not begin until 6:30. Don’t let your blood-sugar level drop too low. On my interview days, I packed apple slices and nuts: a balance of unprocessed sugar, protein, and fat.

One-on-one meetings: The hiring committee will cram these into your schedule like sardines into a tin. Typically, you’ll meet with each faculty member for approximately 30 minutes. The faculty member might work in your area of expertise, might belong to the committee (and so might subscribe to a random area of expertise), or might simply be curious about you. Prepare for these one-on-one meetings in advance, as described above. Review your notes on the morning of your interview. Be able to initiate and sustain a conversation of interest to you and your interlocutor, as well as to follow their lead. Your interlocutor might want to share their research, ask technical questions about your work, or hear a bird’s-eye overview of your research program. 

Other topics, such as teaching and faculty housing, might crop up. Feel free to address these subjects if your interlocutor introduces them. If you’re directing the conversation, though, I’d focus mostly on physics. You can ask about housing and other logistics if you receive an offer, and these topics often arise at faculty dinners.

The job talk: The interview will center on a scientific talk. You might present a seminar (perhaps billed as a “special seminar”) or a colloquium. The department will likely invite all its members to attend. Focus mostly on the research you’ve accomplished. Motivate your research program, to excite even attendees from outside your field. (This blog post describes what I look for in a research program when evaluating applications.) But also demonstrate your technical muscle; show how your problems qualify as difficult and how you’ve innovated solutions. Hammer home your research’s depth, but also dedicate a few minutes to its breadth, to demonstrate your research maturity. At the end, offer a glimpse of your research plans. The hiring committee might ask you to dwell more on those in a chalk talk (vide infra). 

Practice your talk alone many times, practice in front of an audience, revise the talk, practice it alone again many times, and practice it in front of another audience. And then—you guessed it—practice the talk again. Enlist listeners from multiple subfields of physics, including yours. Also, enlist grad students, postdocs, and faculty members. Different listeners can help ensure that you’re explaining concepts understandably, that you’ve brushed up on the technicalities, and that you’re motivating your research convincingly.

A faculty member once offered the following advice about questions asked during job talks: if you don’t know an answer, you can offer to look it up after the talk. But you can play this “get out of jail free” card only once. I’ll expand on the advice: if you promise to look up an answer, then follow through, and email the answer to the inquirer. Also, even if you don’t know an answer, you can answer a related question that’ll satisfy the inquirer partially. For example, suppose someone asks whether a particular experiment supports a prediction you’ve made. Maybe you haven’t checked—but maybe you have checked numerical simulations of similar experiments.

The chalk talk: The hiring committee might or might not request a chalk talk. I have the impression that experimentalists receive the request more than theorists do. Still, I presented a couple of chalk talks as a theorist. Only the hiring committee, or at least only faculty members, will attend such a talk. They’ll probably have attended your scientific talk, so don’t repeat much of it. 

The name “chalk talk” can deceive us in two ways. First, one committee requested that I prepare slides for my chalk talk. Another committee did limit me to chalk, though. Second, the chalk “talk” may end up a conversation, rather than a presentation.

The hiring-committee chair should stipulate in advance what they want from your chalk talk. If they don’t, ask for clarification. Common elements include the following:

  • Describe the research program you’ll pursue over the next five years.
  • Where will you apply for funding? Offer greater detail than “the NSF”: under which NSF programs does your research fall? Which types of NSF grants will you apply for at which times?
  • How will you grow your group? How many undergrads, master’s students, PhD students, and postdocs will you hire during each of the next five years? When will your group reach a steady state? How will the steady state look?
  • Describe the research project you’ll give your first PhD/master’s/undergraduate student.
  • What do you need in a startup package? (A startup package consists of university-sourced funding. It enables you to hire personnel, buy equipment, and pay other expenses before landing your first grants.)
  • Which experimental/computational equipment will you purchase first? How much will it cost?
  • Which courses do you want to teach? Identify undergraduate courses, core graduate-level courses, and one or two specialized seminars.

Sample interview questions: Sketch your answers to the following questions in bullet points. Writing the answers out will ensure that you think through them and will help you remember them. Using bullet points will help you pinpoint takeaways.

  • The questions under “The chalk talk”
  • What sort of research do you do?
  • What are you most excited about?
  • Where do you think your field is headed? How will it look in five, ten, or twenty years?
  • Which paper are you proudest of?
  • How will you distinguish your research program from your prior supervisors’ programs?
  • Do you envision opportunities for theory–experiment collaborations?
  • What teaching experience do you have? (Research mentorship counts as teaching. Some public outreach can count, too.)
  • Which mathematical tools do you use most?
  • How do you see yourself fitting into the department? (Does the department host an institute for your subfield? Does the institute have oodles of theorists whom you’ll counterbalance as an experimentalist? Will you bridge multiple research groups through your interdisciplinary work? Will you anchor a new research group that the department plans to build over the next decade?)

Own your achievements, but don’t boast: At a workshop late in my PhD, I heard a professor describe her career. She didn’t color her accomplishments artificially; she didn’t sound arrogant; she didn’t even sound as though she aimed to impress her audience. She sounded as though the workshop organizer had tasked her with describing her work and she was following his instructions straightforwardly, honestly, and simply. Her achievements spoke for themselves. They might as well have been reciting Shakespeare, they so impressed me. Perhaps we early-career researchers need another few decades before we can hope to emulate that professor’s poise and grace. But when compelled to describe what I’ve done, I lift my gaze mentally to her.

My schooling imprinted on me an appreciation for modesty. Therefore, the need to own my work publicly used to trouble me. But your interviewers need to know of your achievements: they need to respect you, to see that you deserve a position in their department. Don’t downplay your contributions to collaborations, and don’t shy away from claiming your proofs. But don’t brag or downplay your collaborators’ contributions. Describe your work straightforwardly; let it speak for itself.

Evaluators shouldn’t ask about your family: Their decision mustn’t depend on whether you’re a single adult who can move at the drop of a hat, whether you’re engaged to someone who’ll have to approve the move, or whether you have three children rooted in their school district. This webpage elaborates on the US’s anti-discrimination policy. What if an evaluator asks a forbidden question? One faculty member has recommend the response, “Does the position depend on that information?”

Follow up: Thank each of your interviewers individually, via email, within 24 hours of the conversation. Time is to faculty members as water is to Californians during wildfire season. As an interviewee, I felt grateful to all the faculty who dedicated time to me. (I mailed hand-written thank-you cards in addition to writing emails, but I’d expect almost nobody else to do that.)

How did I compose thank-you messages? I’d learned some nugget from every meeting, and I’d enjoyed some element of almost every meeting. I described what I learned and enjoyed, and I expressed the gratitude I felt.

Try to enjoy yourself: A committee chose your application from amongst hundreds. Cherish the compliment. Cherish the opportunity to talk physics with smart people. During my interviews, I learned about quantum information, thermodynamics, cosmology, biophysics,  and dark-matter detection. I connected with faculty members whom I still enjoy greeting at conferences; unknowingly recruited a PhD student into quantum thermodynamics during a job talk; and, for the first time, encountered a dessert shaped like sushi (at a faculty dinner. I stuck with a spicy tuna roll, but the dessert roll looked stunning). Retain an attitude of gratitude, and you won’t regret your visit.