Toward quantum value

The theme of this year’s Q2B meeting is “The Roadmap to Quantum Value.” I interpret “quantum value” as meaning applications of quantum computing that have practical utility for end-users in business. So I’ll begin by reiterating a point I have made repeatedly in previous appearances at Q2B. As best we currently understand, the path to economic impact is the road through fault-tolerant quantum computing. And that poses daunting challenges for our field and for the quantum industry.

We are in the NISQ era. NISQ (rhymes with “risk’”) is an acronym meaning “Noisy Intermediate-Scale Quantum.” Here “intermediate-scale” conveys that current quantum computing platforms with of order 100 qubits are difficult to simulate by brute force using the most powerful currently existing supercomputers. “Noisy” reminds us that today’s quantum processors are not error-corrected, and noise is a serious limitation on their computational power. NISQ technology already has noteworthy scientific value. But as of now there is no proposed application of NISQ computing with commercial value for which quantum advantage has been demonstrated when compared to the best classical hardware running the best algorithms for solving the same problems. Furthermore, currently there are no persuasive theoretical arguments indicating that commercially viable applications will be found that do not use quantum error-correcting codes and fault-tolerant quantum computing.

A useful survey of quantum computing applications, over 300 pages long, recently appeared, providing rough estimates of end-to-end run times for various quantum algorithms. This is hardly the last word on the subject — new applications are continually proposed, and better implementations of existing algorithms continually arise. But it is a valuable snapshot of what we understand today, and it is sobering.

There can be quantum advantage in some applications of quantum computing to optimization, finance, and machine learning. But in this application area, the speedups are typically at best quadratic, meaning the quantum run time scales as the square root of the classical run time. So the advantage kicks in only for very large problem instances and deep circuits, which we won’t be able to execute without error correction.

Larger polynomial advantage and perhaps superpolynomial advantage is possible in applications to chemistry and materials science, but these may require at least hundreds of very well-protected logical qubits, and hundreds of millions of very high-fidelity logical gates, if not more. Quantum fault tolerance will be needed to run these applications, and fault tolerance has a hefty cost in both the number of physical qubits and the number of physical gates required. We should also bear in mind that the speed of logical gates is relevant, since the run time as measured by the wall clock will be an important determinant of the value of quantum algorithms.

Overcoming noise in quantum devices

Already in today’s quantum processors steps are taken to address limitations imposed by the noise — we use error mitigation methods like zero noise extrapolation or probabilistic error cancellation. These methods work effectively at extending the size of the circuits we can execute with useful fidelity. But the asymptotic cost scales exponentially with the size of the circuit, so error mitigation alone may not suffice to reach quantum value. Quantum error correction, on the other hand, scales much more favorably, like a power of a logarithm of the circuit size. But quantum error correction is not practical yet. To make use of it, we’ll need better two-qubit gate fidelities, many more physical qubits, robust systems to control those qubits, as well as the ability to perform fast and reliable mid-circuit measurements and qubit resets; all these are technically demanding goals.

To get a feel for the overhead cost of fault-tolerant quantum computing, consider the surface code — it’s presumed to be the best near-term prospect for achieving quantum error correction, because it has a high accuracy threshold and requires only geometrically local processing in two dimensions. Once the physical two-qubit error rate is below the threshold value of about 1%, the probability of a logical error per error correction cycle declines exponentially as we increase the code distance d:

P_logical = (0.1)(P_physical/P_threshold)^(d+1)/2

where the number of physical qubits in the code block (which encodes a single protected qubit) is the distance squared.

Suppose we wish to execute a circuit with 1000 qubits and 100 million time steps. Then we want the probability of a logical error per cycle to be 10^-11. Assuming the physical error rate is 10^-3, better than what is currently achieved in multi-qubit devices, from this formula we infer that we need a code distance of 19, and hence 361 physical qubits to encode each logical qubit, and a comparable number of ancilla qubits for syndrome measurement — hence over 700 physical qubits per logical qubit, or a total of nearly a million physical qubits.  If the physical error rate improves to 10^-4 someday, that cost is reduced, but we’ll still need hundreds of thousands of physical qubits if we rely on the surface code to protect this circuit.

Progress toward quantum error correction

The study of error correction is gathering momentum, and I’d like to highlight some recent experimental and theoretical progress. Specifically, I’ll remark on three promising directions, all with the potential to hasten the arrival of the fault-tolerant era: erasure conversion, biased noise, and more efficient quantum codes.

Erasure conversion

Error correction is more effective if we know when and where the errors occurred. To appreciate the idea, consider the case of a classical repetition code that protects against bit flips. If we don’t know which bits have errors we can decode successfully by majority voting, assuming that fewer than half the bits have errors. But if errors are heralded then we can decode successfully by just looking at any one of the undamaged bits. In quantum codes the details are more complicated but the same principle applies — we can recover more effectively if so-called erasure errors dominate; that is, if we know which qubits are damaged and in which time steps. “Erasure conversion” means fashioning a processor such that the dominant errors are erasure errors.

We can make use of this idea if the dominant errors exit the computational space of the qubit, so that an error can be detected without disturbing the coherence of undamaged qubits. One realization is with Alkaline earth Rydberg atoms in optical tweezers, where 0 is encoded as a low energy state, and 1 is a highly excited Rydberg state. The dominant error is the spontaneous decay of the 1 to a lower energy state. But if the atomic level structure and the encoding allow, 1 usually decays not to a 0, but rather to another state g. We can check whether the g state is occupied, to detect whether or not the error occurred, without disturbing a coherent superposition of 0 and 1.

Erasure conversion can also be arranged in superconducting devices, by using a so-called dual-rail encoding of the qubit in a pair of transmons or a pair of microwave resonators. With two resonators, for example, we can encode a qubit by placing a single photon in one resonator or the other. The dominant error is loss of the photon, causing either the 01 state or the 10 state to decay to 00. One can check whether the state is 00, detecting whether the error occurred, without disturbing a coherent superposition of 01 and 10.

Erasure detection has been successfully demonstrated in recent months, for both atomic (here and here) and superconducting (here and here) qubit encodings.

Biased noise

Another setting in which the effectiveness of quantum error correction can be enhanced is when the noise is highly biased. Quantum error correction is more difficult than classical error correction partly because more types of errors can occur — a qubit can flip in the standard basis, or it can flip in the complementary basis, what we call a phase error. In suitably designed quantum hardware the bit flips are highly suppressed, so we can concentrate the error-correcting power of the code on protecting against phase errors. For this scheme to work, it is important that phase errors occurring during the execution of a quantum gate do not propagate to become bit-flip errors. And it was realized just a few years ago that such bias-preserving gates are possible for qubits encoded in continuous variable systems like microwave resonators.

Specifically, we may consider a cat code, in which the encoded 0 and encoded 1 are coherent states, well separated in phase space. Then bit flips are exponentially suppressed as the mean photon number in the resonator increases. The main source of error, then, is photon loss from the resonator, which induces a phase error for the cat qubit, with an error rate that increases only linearly with photon number. We can then strike a balance, choosing a photon number in the resonator large enough to provide physical protection against bit flips, and then use a classical code like the repetition code to build a logical qubit well protected against phase flips as well.

Work on such repetition cat codes is ongoing (see here, here, and here), and we can expect to hear about progress in that direction in the coming months.

More efficient codes

Another exciting development has been the recent discovery of quantum codes that are far more efficient than the surface code. These include constant-rate codes, in which the number of protected qubits scales linearly with the number of physical qubits in the code block, in contrast to the surface code, which protects just a single logical qubit per block. Furthermore, such codes can have constant relative distance, meaning that the distance of the code, a rough measure of how many errors can be corrected, scales linearly with the block size rather than the square root scaling attained by the surface code.

These new high-rate codes can have a relatively high accuracy threshold, can be efficiently decoded, and schemes for executing fault-tolerant logical gates are currently under development.

A drawback of the high-rate codes is that, to extract error syndromes, geometrically local processing in two dimensions is not sufficient — long-range operations are needed. Nonlocality can be achieved through movement of qubits in neutral atom tweezer arrays or ion traps, or one can use the native long-range coupling in an ion trap processor. Long-range coupling is more challenging to achieve in superconducting processors, but should be possible.

An example with potential near-term relevance is a recently discovered code with distance 12 and 144 physical qubits. In contrast to the surface code with similar distance and length which encodes just a single logical qubit, this code protects 12 logical qubits, a significant improvement in encoding efficiency.

The quest for practical quantum error corrections offers numerous examples like these of co-design. Quantum error correction schemes are adapted to the features of the hardware, and ideas about quantum error correction guide the realization of new hardware capabilities. This fruitful interplay will surely continue.

An exciting time for Rydberg atom arrays

In this year’s hardware news, now is a particularly exciting time for platforms based on Rydberg atoms trapped in optical tweezer arrays. We can anticipate that Rydberg platforms will lead the progress in quantum error correction for at least the next few years, if two-qubit gate fidelities continue to improve. Thousands of qubits can be controlled, and geometrically nonlocal operations can be achieved by reconfiguring the atomic positions. Further improvement in error correction performance might be possible by means of erasure conversion. Significant progress in error correction using Rydberg platforms is reported in a paper published today.

But there are caveats. So far, repeatable error syndrome measurement has not been demonstrated. For that purpose, continuous loading of fresh atoms needs to be developed. And both the readout and atomic movement are relatively slow, which limits the clock speed.

Movability of atomic qubits will be highly enabling in the short run. But in the longer run, movement imposes serious limitations on clock speed unless much faster movement can be achieved. As things currently stand, one can’t rapidly accelerate an atom without shaking it loose from an optical tweezer, or rapidly accelerate an ion without heating its motional state substantially. To attain practical quantum computing using Rydberg arrays, or ion traps, we’ll eventually need to make the clock speed much faster.

Cosmic rays!

To be fair, other platforms face serious threats as well. One is the vulnerability of superconducting circuits to ionizing radiation. Cosmic ray muons for example will occasionally deposit a large amount of energy in a superconducting circuit, creating many phonons which in turn break Cooper pairs and induce qubit errors in a large region of the chip, potentially overwhelming the error-correcting power of the quantum code. What can we do? We might go deep underground to reduce the muon flux, but that’s expensive and inconvenient. We could add an additional layer of coding to protect against an event that wipes out an entire surface code block; that would increase the overhead cost of error correction. Or maybe modifications to the hardware can strengthen robustness against ionizing radiation, but it is not clear how to do that.

Outlook

Our field and the quantum industry continue to face a pressing question: How will we scale up to quantum computing systems that can solve hard problems? The honest answer is: We don’t know yet. All proposed hardware platforms need to overcome serious challenges. Whatever technologies may seem to be in the lead over, say, the next 10 years might not be the best long-term solution. For that reason, it remains essential at this stage to develop a broad array of hardware platforms in parallel.

Today’s NISQ technology is already scientifically useful, and that scientific value will continue to rise as processors advance. The path to business value is longer, and progress will be gradual. Above all, we have good reason to believe that to attain quantum value, to realize the grand aspirations that we all share for quantum computing, we must follow the road to fault tolerance. That awareness should inform our thinking, our strategy, and our investments now and in the years ahead.

The gesture surprised me. If I were in a vehicle of the sort most familiar to me—a car—I wouldn’t wave to other drivers. In a tram, I wouldn’t wave to passengers on a parallel track. Granted, trams and cars are closed, whereas boats can be open-air. But even as a pedestrian in a downtown crossing, I wouldn’t wave to everyone I passed. Yet, as boat after boat pulled alongside us, we received salutation after salutation.

The outing marked the midpoint of the Quantum Connections summer school. Physicists Frank Wilczek, Antti Niemi, and colleagues coordinate the school, which draws students and lecturers from across the globe. Although sponsored by Stockholm University, the school takes place at a century-old villa whose name I wish I could pronounce: Högberga Gård. The villa nestles atop a cliff on an island in the archipelago. We ventured off the island after a week of lectures.

Charlie Marcus lectured about materials formed from superconductors and semiconductors; John Martinis, about superconducting qubits; Jianwei Pan, about quantum advantages; and others, about symmetries, particle statistics, and more. Feeling like an ant among giants, I lectured about quantum thermodynamics. Two other lectures linked quantum physics with gravity—and in a way you might not expect. I appreciated the opportunity to reconnect with the lecturer: Igor Pikovski.

Igor doesn’t know it, but he’s one of the reasons why I joined the Harvard-Smithsonian Institute for Theoretical Atomic, Molecular, and Optical Physics (ITAMP) as an ITAMP Postdoctoral Fellow in 2018. He’d held the fellowship beginning a few years before, and he’d earned a reputation for kindness and consideration. Also, his research struck me as some of the most fulfilling that one could undertake.

If you’ve heard about the intersection of quantum physics and gravity, you’ve probably heard of approaches other than Igor’s. For instance, physicists are trying to construct a theory of quantum gravity, which would describe black holes and the universe’s origin. Such a “theory of everything” would reduce to Einstein’s general theory of relativity when applied to planets and would reduce to quantum theory when applied to atoms. In another example, physicists leverage quantum technologies to observe properties of gravity. Such technologies enabled the observatory LIGO to register gravitational waves—ripples in space-time. 

Igor and his colleagues pursue a different goal: to observe phenomena whose explanations depend on quantum theory and on gravity.

In his lectures, Igor illustrated with an experiment first performed in 1975. The experiment relies on what happens if you jump: You gain energy associated with resisting the Earth’s gravitational pull—gravitational potential energy. A quantum object’s energy determines how the object’s quantum state changes in time. The experimentalists applied this fact to a beam of neutrons. 

They put the beam in a superposition of two locations: closer to the Earth’s surface and farther away. The closer component changed in time in one way, and the farther component changed another way. After a while, the scientists recombined the components. The two interfered with each other similarly to the waves created by two raindrops falling near each other on a puddle. The interference evidenced gravity’s effect on the neutrons’ quantum state.

The experimentalists approximated gravity as dominated by the Earth alone. But other masses can influence the gravitational field noticeably. What if you put a mass in a superposition of different locations? What would happen to space-time?

Or imagine two quantum particles too far apart to interact with each other significantly. Could a gravitational field entangle the particles by carrying quantum correlations from one to the other?

Physicists including Igor ponder these questions…and then ponder how experimentalists could test their predictions. The more an object influences gravity, the more massive the object tends to be, and the more easily the object tends to decohere—to spill the quantum information that it holds into its surroundings.

The “gravity-quantum interface,” as Igor entitled his lectures, epitomizes what I hoped to study in college, as a high-school student entranced by physics, math, and philosophy. What’s more curious and puzzling than superpositions, entanglement, and space-time? What’s more fundamental than quantum theory and gravity? Little wonder that connecting them inspires wonder.

But we humans are suckers for connections. I appreciated the opportunity to reconnect with a colleague during the summer school. Boaters on the Stockholm archipelago waved to our cohort as they passed. And who knows—gravitational influences may even have rippled between the boats, entangling us a little.

With thanks to the summer-school organizers, including Pouya Peighami and Elizabeth Yang, for their invitation and hospitality.

Literary Hub has now published my answer.

According to its website, “Literary Hub is an organizing principle in the service of literary culture, a single, trusted, daily source for all the news, ideas and richness of contemporary literary life. There is more great literary content online than ever before, but it is scattered, easily lost—with the help of its editorial partners, Lit Hub is a site readers can rely on for smart, engaged, entertaining writing about all things books.”

My article, “Five best books about the romance of Victorian science,” appeared there last week. You’ll find fiction, nonfiction as imaginative as fiction, and crossings of the border between the two. 

My contribution to literature about the romance of Victorian science—my (mostly) nonfiction book, Quantum Steampunk: The Physics Of Yesterday’s Tomorrow—was  published two weeks ago. Where’s a hot-air-balloon emoji when you need one?

You can read more in Quanta Magazine here and yet more in chapter 9 of my book. For recent advances in fluctuation relations, as opposed to the broad introduction there, check out earlier Quantum Frontiers posts here, here, here, here, and here.

My liberal-arts education did mortify me a little as I pursued my Master’s degree. Most of my peers had focused on physics, mathematics, and computer science while I’d been reading Aristotle. They seemed to breeze through coursework that I clawed my way through. I still sigh wistfully over math courses, such as complex analysis, that I’ve never taken. Meanwhile, a debate about the liberal arts has been raging across the nation. Debt is weighing down recent graduates, and high-school students are loading up on STEMM courses. Colleges are cutting liberal-arts departments, and educational organizations are broadcasting the value of liberal-arts educations.

I’m not an expert in public policy or school systems; I’m a physicist. As a physicist, I’m grateful for my liberal-arts education. It’s enhanced my physics research in at least five ways.

(1) I learned to seek out, and take advantage of, context. Early in my first German-literature course, I’d just completed my first reading assignment. My professor told my class to fetch out our books and open them to the beginning. A few rustles later, we held our books open to page one of the main text. 

No, no, said my professor. Open your books to the beginning. Did anyone even look at the title page?

We hadn’t, we admitted. We’d missed a wealth of information, as the book contained a reproduction of an old title page. Publishers, fonts, and advertisement styles have varied across the centuries and the globe. They, together with printing and reprinting dates, tell stories about the book’s origin, popularity, role in society, and purposes. Furthermore, a frontispiece is worth a thousand words, all related before the main text begins. When my class turned to the main text, much later in the lecture, we saw it in a new light. Context deepens and broadens our understanding.

When I read a physics paper, I start at the beginning—the true beginning. I note the publication date, the authors, their institutions and countries, and the journal. X’s lab performed the experiment reported on? X was the world’s expert in Y back then but nursed a bias against Z, a bias later proved to be unjustified. So I should aim to learn from the paper about Y but should take statements about Z with a grain of salt. Seeking and processing context improves my use of physics papers, thanks to a German-literature course.

(2) I learned argumentation. Doing physics involves building, analyzing, criticizing, and repairing arguments. I argue that mathematical X models physical system Y accurately, that an experiment I’ve proposed is feasible with today’s technology, and that observation Z supports a conjecture of mine. Physicists also prove mathematical statements deductively. I received proof-writing lessons in a math course, halfway through college. One of the most competent teachers I’ve ever encountered taught the course. But I learned about classes of arguments and about properties of arguments in a philosophy course, Informal Logic. 

There, I learned to distinguish deduction from inference and an argument’s validity and soundness from an argument’s strength and cogency. I learned strategies for proving arguments and learned fallacies to criticize. I came to respect the difference between “any” and “every,” which I see interchanged in many physics papers. This philosophical framework helps me formulate, process, dissect, criticize, and correct physics arguments. 

For instance, I often parse long, dense, technical proofs of mathematical statements. First, I identify whether the proof strategy is reductio ad absurdum, proof by counterexample, or another strategy. Upon identifying the overarching structure, I can fill my understanding with details. Additionally, I check proofs by students, and I respond to criticisms of my papers by journal referees. I could say, upon reading an argument, “Something feels a bit off, and it’s sort of like the thing that felt a bit off in that paper I read last Tuesday.” But I’d rather group the argument I’m given together with arguments I know how to tackle. I’d rather be able to say, “They’re straw-manning my argument” or “That argument begs the question.” Doing so, I put my finger on the problem and take a step toward solving it.

(3) I learned to analyze materials to bits, then extract meaning from the analysis. English and German courses trained me to wring from literature every drop of meaning that I could discover. I used to write one to three pages about a few-line quotation. The analysis would proceed from diction and punctuation to literary devices; allusions; characters’ relationships with each other, themselves, and nature; and the quotation’s role in the monograph. Everything from minutia to grand themes required scrutiny, according to the dissection technique I trained in. Every pincer probe lifted another skein of skin or drew aside another tendon, offering deeper insights into the literary work. I learned to find the skeins to lift, lift them in the right direction, pinpoint the insights revealed, and integrate the insights into a coherent takeaway.

This training has helped me assess and interpret mathematics. Physicists pick a physical system to study, model the system with equations, and solve the equations. The next two steps are intertwined: evaluating whether one solved the equations correctly and translating the solution into the physical system’s behavior. These two steps necessitate a dissection of everything from minutia to grand themes: Why should this exponent be 4/5, rather than any other number? Should I have expected this energy to depend on that length in this way? Is the physical material aging quickly or resisting change? These questions’ answers inform more-important questions: Who cares? Do my observations shed light worth anyone’s time, or did I waste a week solving equations no one should care about?

To answer all these questions, I draw on my literary training: I dissect content, pinpoint insights, and extract meaning. Having performed this analysis in literature courses facilitates an arguably deeper analysis than my physics training did: In literature courses, I had to organize my thoughts and articulate them in essays. This process revealed holes in my argumentation, as well as connections that I’d overlooked. In contrast, a couple of lines in my physics homework earned full marks. The critical analysis of literature has deepened my assessment of solutions’ correctness, physical interpretation of mathematics, and extraction of meaning from solutions. 

(4) I learned what makes a physicist a physicist. In college, I had a friend who was studying applied mathematics and economics. Over dinner, he described a problem he’d encountered in his studies. I replied, almost without thinking, “From a physics perspective, I’d approach the problem like this.” I described my view, which my friend said he wouldn’t have thought of. I hadn’t thought of myself, and of the tools I was obtaining in the physics department, the way I did after our conversation. 

Physics involves a unique toolkit,¹ set of goals, and philosophy. Physicists identify problems, model them, solve them, and analyze the results in certain ways. Students see examples of these techniques in lectures and practice these techniques for homework. But, as a student, I rarely heard articulations of the general principles that underlay the examples scattered across my courses like a handful of marbles across a kitchen floor. Example principles include, if you don’t understand an abstract idea, construct a simple example. Once you’ve finished a calculation, check whether your answer makes sense in the most extreme scenarios possible. After solving an equation, interpret the solution in terms of physical systems—of how particles and waves move and interact. 

I was learning these techniques, in college, without realizing that I was learning them. I became conscious of the techniques by comparing the approach natural to me with the approach taken in another discipline. Becoming conscious of my toolkit enabled me to wield it more effectively; one can best fry eggs when aware that one owns a spatula. The other disciplines at my liberal-arts college served as a foil for physics. Seeing other disciplines, I saw what makes physics physics—and improved my ability to apply my physics toolkit.

(5) I learned to draw connections between diverse ideas. Senior year of high school, my courses extended from physics to English literature. One might expect such a curriculum to feel higgledy-piggledy, but I found threads that ran through all my courses. For instance, I practiced public speaking in Reasoning, Research, and Rhetoric. Because I studied rhetoric, my philosophy teacher turned to me for input when introducing the triumvirate “thesis, antithesis, synthesis.”² The philosophy curriculum included the feminist essay “If Men Could Menstruate,” which complemented the feminist book Wide Sargasso Sea in my English-literature course. In English literature, I learned that Baldassare Castiglione codified how Renaissance noblemen should behave, in The Book of the Courtier. The author’s name was the answer to the first question on my AP Modern European History exam. My history course covered Isaac Newton and Gottfried Wilhelm Leibniz, who invented calculus during the 17th century. I leveraged their discoveries in my calculus course, which I applied in my physics course. My physics teacher hoped that his students would solve the world’s energy problems—perhaps averting the global thermonuclear war that graced every debate in my rhetoric course (“If you don’t accept my team’s policy, then X will happen, leading to Y, leading to Z, which will cause a global thermonuclear war”). 

Threads linked everything across my liberal-arts education; every discipline featured an idea that paralleled an idea in another discipline. Finding those parallels grew into a game for me, a game that challenged my creativity. Cultivating that creativity paid off when I began doing physics research. Much of my research has resulted from finding, in one field, a concept that resembles a concept in another field. I smash the ideas together to gain insight into each discipline from the other discipline’s perspective. For example, during my PhD studies, I found a thread connecting the physics of DNA strands to the physics of black holes. That thread initiated a research program of mine that’s yielded nine papers, garnered 19 collaborators, and spawned two experiments. Studying diverse subjects trained me to draw creative connections, which underlie much physics research.

I haven’t detailed all the benefits that a liberal-arts education can accrue to a physics career. For instance, the liberal arts enhance one’s communication skills, key to collaborating on research and to conveying one’s research. Without conveying one’s research adroitly, one likely won’t impact a field much. Also, a liberal-arts education can help one connect with researchers from across the globe on a personal level.³ Personal connections enhance science, which scientists—humans—undertake.

As I began building my research group, I sought advice from an MIT professor who’d attended MIT as an undergraduate. He advised me to seek students who have unusual backgrounds, including liberal-arts educations. Don’t get me wrong; I respect and cherish the colleagues and friends of mine who attended MIT, Caltech, and other tech schools as undergraduates. Still, I wouldn’t trade my German literature and economics. The liberal arts have enriched my physics research no less than they’ve enriched the rest of my life.

¹A toolkit that overlaps partially with other disciplines’ toolkits, as explained in (3).

²I didn’t help much. When asked to guess the last concept in the triumvirate, I tried “debate.”

³I once met a Ukrainian physicist who referred to Ilya Muromets in a conversation. Ilya Muromets is a bogatyr, a knight featured in Slavic epics set in the Middle Ages. I happened to have taken a Slavic-folklore course the previous year. So I responded with a reference to Muromets’s pals, Dobrynya Nikitich and Alyosha Popovich. The physicist and I hit it off, and he taught me much about condensed matter over the following months.

As Quantum Frontiers regulars know, steampunk is a genre of literature, art and film. Steampunkers fuse 19th-century settings (such as Victorian England, the Wild West, and Meiji Japan) with futuristic technologies (such as dirigibles, time machines, and automata). So does my field of research, a combination of thermodynamics, quantum physics, and information processing. 

Thermodynamics, the study of energy, developed during the Industrial Revolution. The field grew from practical concerns (How efficiently can engines pump water out of mines?) but wound up addressing fundamental questions (Why does time flow in only one direction?). Thermodynamics needs re-envisioning for 21st-century science, which spotlights quantum systems—electrons, protons, and other basic particles. Early thermodynamicists couldn’t even agree that atoms existed, let alone dream that quantum systems could process information in ways impossible for nonquantum systems. Over the past few decades, we’ve learned that quantum technologies can outperform their everyday counterparts in solving certain computational problems, in securing information, and in transmitting information. The study of quantum systems’ information-processing power forms a mathematical and conceptual toolkit, quantum information science. My colleagues and I leverage this toolkit to reconceptualize thermodynamics. As we combine a 19th-century framework (thermodynamics) with advanced technology (quantum information), I call our field quantum steampunk.

Glimpses of quantum steampunk have surfaced on this blog throughout the past eight years. The book is another animal, a 15-chapter closeup of the field. The book sets the stage with introductions to information processing, quantum physics, and thermodynamics. Then, we watch these three perspectives meld into one coherent whole. We tour the landscape of quantum thermodynamics—the different viewpoints and discoveries championed by different communities. These viewpoints, we find, offer a new lens onto the rest of science, including chemistry, black holes, and materials physics. Finally, we peer through a brass telescope to where quantum steampunk is headed next. Throughout the book, the science interleaves with anecdotes, history, and the story of one woman’s (my) journey into physics—and with snippets from a quantum-steampunk novel that I’ve dreamed up.

On this blog, different parts of my posts are intended for different audiences. Each post contains something for everyone, but not everyone will understand all of each post. In contrast, the book targets the general educated layperson. One of my editors majored in English, and another majored in biology, so the physics should come across clearly to everyone (and if it doesn’t, blame my editors). But the book will appeal to physicists, too. Reviewer Jay Lawrence, a professor emeritus of Dartmouth College’s physics department, wrote, “Presenting this vision [of quantum thermodynamics] in a manner accessible to laypeople discovering new interests, Quantum Steampunk will also appeal to specialists and aspiring specialists.” This book is for you.

Strange to say, I began writing Quantum Steampunk under a year ago. I was surprised to receive an email from Tiffany Gasbarrini, a senior acquisitions editor at Johns Hopkins University Press, in April 2020. Tiffany had read the article I’d written about quantum steampunk for Scientific American. She wanted to expand the press’s offerings for the general public. Would I be interested in writing a book proposal? she asked.

Not having expected such an invitation, I poked around. The press’s roster included books that caught my eye, by thinkers I wanted to meet. From Wikipedia, I learned that Johns Hopkins University Press is “the oldest continuously running university press in the United States.” Senior colleagues of mine gave the thumbs-up. So I let my imagination run.

I developed a table of contents while ruminating on long walks, which I’d begun taking at the start of the pandemic. In late July, I submitted my book proposal. As the summer ended, I began writing the manuscript.

Writing the first draft—73,000 words—took about five months. The process didn’t disrupt life much. I’m used to writing regularly; I’ve written one blog post per month here since 2013, and I wrote two novels during and after college. I simply picked up my pace. At first, I wrote only on weekends. Starting in December 2020, I wrote 1,000 words per day. The process wasn’t easy, but it felt like a morning workout—healthy and productive. That productivity fed into my science, which fed back into the book. One of my current research projects grew from the book’s epilogue. A future project, I expect, will evolve from Chapter 5.

As soon as I finished draft one—last January—Tiffany and I hunted for an illustrator. We were fortunate to find Todd Cahill, a steampunk artist. He transformed the poor sketches that I’d made into works of art.

Early this spring, I edited the manuscript. That edit was to a stroll as the next edit was to the Boston Marathon. Editor Michael Zierler coached me through the marathon. He identified concepts that needed clarification, discrepancies between explanations, and analogies that had run away with me—as well as the visions and turns of phrase that delighted him, to balance the criticism. As Michael and I toiled, 17 of my colleagues were kind enough to provide feedback. They read sections about their areas of expertise, pointed out subtleties, and confirmed facts.

Soon after Michael and I crossed the finished line, copyeditor Susan Matheson took up the baton. She hunted for typos, standardized references, and more. Come June, I was editing again—approving and commenting on her draft. Simultaneously, Tiffany designed the cover, shown above, with more artists. The marketing team reached out, and I began planning this blog post. Scratch that—I’ve been dreaming about this blog post for almost a year. But I forced myself not to spill the beans here till I told the research group I’ve been building. I shared about the book with them two Thursdays ago, and I hope that book critics respond as they did.

Every time I’ve finished a draft, my husband and I have celebrated by ordering takeout sandwiches from our favorite restaurant. Three sandwich meals are down, and we have one to go.

Having dreamed about this blog post for a year, I’m thrilled to bits to share my book with you. It’s available for preordering, and I encourage you to support your local bookstore by purchasing through bookshop.org. The book is available also through Barnes & Noble, Amazon, Waterstones, and the other usual suspects. For press inquiries, or to request a review copy, contact Kathryn Marguy at kmarguy@jhu.edu.

Over the coming year, I’ll continue sharing about my journey into publishing—the blurbs we’ll garner for the book jacket, the first copies hot off the press, the reviews and interviews. I hope that you’ll don your duster coat and goggles (every steampunker wears goggles), hop into your steam-powered gyrocopter, and join me.

Hard as I studied, my classmates enjoyed greater facility with the computer-science curriculum. They saw immediately how long an algorithm would run, while I hesitated and then computed the run time step by step. I felt behind. So I protested when my professor said, “You’re good at this.” 

I now see that we were focusing on different facets of learning. I rued my lack of intuition. My classmates had gained intuition by exploring computer science in high school, then slow-cooking their experiences on a mental back burner. Their long-term exposure to the material provided familiarity—the ability to recognize a new problem as belonging to a class they’d seen examples of. I was cooking course material in a mental microwave set on “high,” as a semester’s worth of material was crammed into ten weeks at my college.

My professor wasn’t measuring my intuition. He only saw that I knew how to compute an algorithm’s run time. I’d learned the material required of me—more than I realized, being distracted by what I hadn’t learned that difficult autumn.

We can learn a staggering amount when pushed far from our comfort zones—and not only we humans can. So can simple collections of particles.

Examples include a classical spin glass. A spin glass is a collection of particles that shares some properties with a magnet. Both a magnet and a spin glass consist of tiny mini-magnets called spins. Although I’ve blogged about quantum spins before, I’ll focus on classical spins here. We can imagine a classical spin as a little arrow that points upward or downward.  A bunch of spins can form a material. If the spins tend to point in the same direction, the material may be a magnet of the sort that’s sticking the faded photo of Fluffy to your fridge.

The spins may interact with each other, similarly to how electrons interact with each other. Not entirely similarly, though—electrons push each other away. In contrast, a spin may coax its neighbors into aligning or anti-aligning with it. Suppose that the interactions are random: Any given spin may force one neighbor into alignment, gently ask another neighbor to align, entreat a third neighbor to anti-align, and having nothing to say to neighbors four and five.

The spin glass can interact with the external world in two ways. First, we can stick the spins in a magnetic field, as by placing magnets above and below the glass. If aligned with the field, a spin has negative energy; and, if antialigned, positive energy. We can sculpt the field so that it varies across the spin glass. For instance, spin 1 can experience a strong upward-pointing field, while spin 2 experiences a weak downward-pointing field.

Second, say that the spins occupy a fixed-temperature environment, as I occupy a 74-degree-Fahrenheit living room. The spins can exchange heat with the environment. If releasing heat to the environment, a spin flips from having positive energy to having negative—from antialigning with the field to aligning.

Let’s perform an experiment on the spins. First, we design a magnetic field using random numbers. Whether the field points upward or downward at any given spin is random, as is the strength of the field experienced by each spin. We sculpt three of these random fields and call the trio a drive.

Let’s randomly select a field from the drive and apply it to the spin glass for a while; again, randomly select a field from the drive and apply it; and continue many times. The energy absorbed by the spins from the fields spikes, then declines.

Now, let’s create another drive of three random fields. We’ll randomly pick a field from this drive and apply it; again, randomly pick a field from this drive and apply it; and so on. Again, the energy absorbed by the spins spikes, then tails off.

Here comes the punchline. Let’s return to applying the initial fields. The energy absorbed by the glass will spike—but not as high as before. The glass responds differently to a familiar drive than to a new drive. The spin glass recognizes the original drive—has learned the first fields’ “fingerprint.” This learning happens when the fields push the glass far from equilibrium,¹ as I learned when pushed during my mildly hellish autumn.

So spin glasses learn drives that push them far from equilibrium. So do many other simple, classical, many-particle systems: polymers, viscous liquids, crumpled sheets of Mylar, and more. Researchers have predicted such learning and observed it experimentally. 

Scientists have detected many-particle learning by measuring thermodynamic observables. Examples include the energy absorbed by the spin glass—what thermodynamicists call work. But thermodynamics developed during the 1800s, to describe equilibrium systems, not to study learning. 

One study of learning—the study of machine learning—has boomed over the past two decades. As described by the MIT Technology Review, “[m]achine-learning algorithms use statistics to find patterns in massive amounts of data.” Users don’t tell the algorithms how to find those patterns.

It seems natural and fitting to use machine learning to learn about the learning by many-particle systems. That’s what I did with collaborators from the group of Jeremy England, a GlaxoSmithKline physicist who studies complex behaviors of many particle systems. Weishun Zhong, Jacob Gold, Sarah Marzen, Jeremy, and I published our paper last month. 

Using machine learning, we detected and measured many-particle learning more reliably and precisely than thermodynamic measures seem able to. Our technique works on multiple facets of learning, analogous to the intuition and the computational ability I encountered in my computer-science course. We illustrated our technique on a spin glass, but one can apply our approach to other systems, too. I’m exploring such applications with collaborators at the University of Maryland.

The project pushed me far from my equilibrium: I’d never worked with machine learning or many-body learning. But it’s amazing, what we can learn when pushed far from equilibrium. I first encountered this insight sophomore fall of college—and now, we can quantify it better than ever.

¹Equilibrium is a quiet, restful state in which the glass’s large-scale properties change little. No net flow of anything—such as heat or particles—enter or leave the system.

Most physicists agree that quantum phenomena probably don’t affect cognition significantly. Cognition occurs in biological systems, which have high temperatures, many particles, and watery components. Such conditions quash entanglement (a relationship that quantum particles can share and that can produce correlations stronger than any produceable by classical particles). 

Yet Matthew Fisher, a condensed-matter physicist, proposed a mechanism by which entanglement might enhance coordinated neuron firing. Phosphorus nuclei have spins (quantum properties similar to angular momentum) that might store quantum information for long times when in Posner molecules. These molecules may protect the information from decoherence (leaking quantum information to the environment), via mechanisms that Fisher described.

I can’t check how correct Fisher’s proposal is; I’m not a biochemist. But I’m a quantum information theorist. So I can identify how Posners could process quantum information if Fisher were correct. I undertook this task with my colleague Elizabeth Crosson, during my PhD. 

Experimentalists have begun testing elements of Fisher’s proposal. What if, years down the road, they find that Posners exist in biofluids and protect quantum information for long times? We’ll need to test whether Posners can share entanglement. But detecting entanglement tends to require control finer than you can exert with a stirring rod. How could you check whether a beakerful of particles contains entanglement?

I asked that question of Adam Bene Watts, a PhD student at MIT, and John Wright, then an MIT postdoc and now an assistant professor in Texas. John gave our project its codename. At a meeting one day, he reported that he’d watched the film Avengers: Endgame. Had I seen it? he asked.

No, I replied. The only superhero movie I’d seen recently had been Ant-Man and the Wasp—and that because, according to the film’s scientific advisor, the movie riffed on research of mine. 

Go on, said John.

Spiros Michalakis, the Caltech mathematician in charge of this blog, served as the advisor. The film came out during my PhD; during a meeting of our research group, Spiros advised me to watch the movie. There was something in it “for you,” he said. “And you,” he added, turning to Elizabeth. I obeyed, to hear Laurence Fishburne’s character tell Ant-Man that another character had entangled with the Posner molecules in Ant-Man’s brain.² 

John insisted on calling our research Project Ant-Man.

John and Adam study Bell tests. Bell test sounds like a means of checking whether the collar worn by your cat still jingles. But the test owes its name to John Stewart Bell, a Northern Irish physicist who wrote a groundbreaking paper in 1964. 

Say you’d like to check whether two particles share entanglement. You can run an experiment, described by Bell, on them. The experiment ends with a measurement of the particles. You repeat this experiment in many trials, using identical copies of the particles in subsequent trials. You accumulate many measurement outcomes, whose statistics you calculate. You plug those statistics into a formula concocted by Bell. If the result exceeds some number that Bell calculated, the particles shared entanglement.

We needed a variation on Bell’s test. In our experiment, every trial would involve hordes of particles. The experimentalists—large, clumsy, classical beings that they are—couldn’t measure the particles individually. The experimentalists could record only aggregate properties, such as the intensity of the phosphorescence emitted by a test tube.

Adam, MIT physicist Aram Harrow, and I concocted such a Bell test, with help from John. Physical Review A published our paper this month—as a Letter and an Editor’s Suggestion, I’m delighted to report.

For experts: The trick was to make the Bell correlation function nonlinear in the state. We assumed that the particles shared mostly pairwise correlations, though our Bell inequality can accommodate small aberrations. Alas, no one can guarantee that particles share only mostly pairwise correlations. Violating our Bell inequality therefore doesn’t rule out hidden-variables theories. Under reasonable assumptions, though, a not-completely-paranoid experimentalist can check for entanglement using our test. 

One can run our macroscopic Bell test on photons, using present-day technology. But we’re more eager to use the test to characterize lesser-known entities. For instance, we sketched an application to Posner molecules. Detecting entanglement in chemical systems will require more thought, as well as many headaches for experimentalists. But our paper broaches the cask—which I hope to see flow in the next Ant-Man film. Due to debut in 2022, the movie has the subtitle Quantumania. Sounds almost as crazy as studying the possibility that quantum phenomena affect cognition.

¹Of those options, I’ve undertaken only the last.

²In case of any confusion: We don’t know that anyone’s brain contains Posner molecules. The movie features speculative fiction.

So does a drunkard’s walk. Imagine a college student who (outside of the pandemic) has stayed out an hour too late and accepted one too many red plastic cups. He’s arrived halfway down a sidewalk, where he’s clutching a lamppost, en route home. Each step has a 50% chance of carrying him leftward and a 50% chance of carrying him rightward. This scenario repeats itself every Friday. On average, five minutes after arriving at the lamppost, he’s back at the lamppost. But, if we wait for a time , we have a decent chance of finding him a distance away. These characteristic typify a simple random walk.

Random walks crop up across statistical physics. For instance, consider a grain of pollen dropped onto a thin film of water. The water molecules buffet the grain, which random-walks across the film. Robert Brown observed this walk in 1827, so we call it Brownian motion. Or consider a magnet at room temperature. The magnet’s constituents don’t walk across the surface, but they orient themselves according random-walk mathematics. And, in quantum many-particle systems, information can spread via a random walk. 

So, my statistical-mechanics professor said, someone should open a restaurant near MIT. Serve lo mein and Peking duck, and call the restaurant the Random Wok.

This is the professor who, years later, confronted another alumna and me at a snack buffet.

“You know what this is?” he asked, waving a pastry in front of us. We stared for a moment, concluded that the obvious answer wouldn’t suffice, and shook our heads.

“A brownie in motion!”

Not only pollen grains undergo Brownian motion, and not only drunkards undergo random walks. Many people random-walk to their careers, trying out and discarding alternatives en route. We may think that we know our destination, but we collide with a water molecule and change course.

Such is the thrust of Random Walks, a podcast to which I contributed an interview last month. Abhigyan Ray, an undergraduate in Mumbai, created the podcast. Courses, he thought, acquaint us only with the successes in science. Stereotypes cast scientists as lone geniuses working in closed offices and silent labs. He resolved to spotlight the collaborations, the wrong turns, the lessons learned the hard way—the random walks—of science. Interviewees range from a Microsoft researcher to a Harvard computer scientist to a neurobiology professor to a genomicist.

You can find my episode on Instagram, Apple Podcasts, Google Podcasts, and Spotify. We discuss the bridging of disciplines; the usefulness of a liberal-arts education in physics; Quantum Frontiers; and the delights of poking fun at my PhD advisor, fellow blogger and Institute for Quantum Information and Matter director John Preskill. 

Those students came to mind as I grew to know David Limmer. David is an assistant professor of chemistry at the University of California, Berkeley. He studies statistical mechanics far from equilibrium, using information theory. Though a theorist ardent about mathematics, he partners with experimentalists. He can pass as a physicist and keeps an eye on topics as far afield as black holes. According to his faculty page, I discovered while writing this article, he’s even three years older than I. 

I met David in the final year of my PhD. I was looking ahead to postdocking, as his postdoc fellowship was fading into memory. The more we talked, the more I thought, I’d like to be like him.

I had the good fortune to collaborate with David on a paper published by Physical Review A this spring (as an Editors’ Suggestion!). The project has featured in Quantum Frontiers as the inspiration for a rewriting of “I’m a little teapot.” 

We studied a molecule prevalent across nature and technologies. Such molecules feature in your eyes, solar-fuel-storage devices, and more. The molecule has two clumps of atoms. One clump may rotate relative to the other if the molecule absorbs light. The rotation switches the molecule from a “closed” configuration to an “open” configuration.

These molecular switches are small, quantum, and far from equilibrium; so modeling them is difficult. Making assumptions offers traction, but many of the assumptions disagreed with David. He wanted general, thermodynamic-style bounds on the probability that one of these molecular switches would switch. Then, he ran into me.

I traffic in mathematical models, developed in quantum information theory, called resource theories. We use resource theories to calculate which states can transform into which in thermodynamics, as a dime can transform into ten pennies at a bank. David and I modeled his molecule in a resource theory, then bounded the molecule’s probability of switching from “closed” to “open.” I accidentally composed a theme song for the molecule; you can sing along with this post.

That post didn’t mention what David and I discovered about quantum clocks. But what better backdrop for a mental trip to elementary school or to three years into the future?

https://xkcd.com/255

I’ve blogged about autonomous quantum clocks (and ancient Assyria) before. Autonomous quantum clocks differ from quantum clocks of another type—the most precise clocks in the world. Scientists operate the latter clocks with lasers; autonomous quantum clocks need no operators. Autonomy benefits you if you want for a machine, such as a computer or a drone, to operate independently. An autonomous clock in the machine ensures that, say, the computer applies the right logical gate at the right time.

What’s an autonomous quantum clock? First, what’s a clock? A clock has a degree of freedom (e.g., a pair of hands) that represents the time and that moves steadily. When the clock’s hands point to 12 PM, you’re preparing lunch; when the clock’s hands point to 6 PM, you’re reading Quantum Frontiers. An autonomous quantum clock has a degree of freedom that represents the time fairly accurately and moves fairly steadily. (The quantum uncertainty principle prevents a perfect quantum clock from existing.)

Suppose that the autonomous quantum clock constitutes one part of a machine, such as a quantum computer, that the clock guides. When the clock is in one quantum state, the rest of the machine undergoes one operation, such as one quantum logical gate. (Experts: The rest of the machine evolves under one Hamiltonian.) When the clock is in another state, the rest of the machine undergoes another operation (evolves under another Hamiltonian).

Physicists have been modeling quantum clocks using the resource theory with which David and I modeled our molecule. The math with which we represented our molecule, I realized, coincided with the math that represents an autonomous quantum clock.

Think of the molecular switch as a machine that operates (mostly) independently and that contains an autonomous quantum clock. The rotating clump of atoms constitutes the clock hand. As a hand rotates down a clock face, so do the nuclei rotate downward. The hand effectively points to 12 PM when the switch occupies its “closed” position. The hand effectively points to 6 PM when the switch occupies its “open” position.

The nuclei account for most of the molecule’s weight; electrons account for little. They flit about the landscape shaped by the atomic clumps’ positions. The landscape governs the electrons’ behavior. So the electrons form the rest of the quantum machine controlled by the nuclear clock.

Experimentalists can create and manipulate these molecular switches easily. For instance, experimentalists can set the atomic clump moving—can “wind up” the clock—with ultrafast lasers. In contrast, the only other autonomous quantum clocks that I’d read about live in theory land. Can these molecules bridge theory to experiment? Reach out if you have ideas!

And check out David’s theory lab on Berkeley’s website and on Twitter. We all need older siblings to look up to.