David Schuster and colleagues' Nature 2004 paper (left) "Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics" helped spawn a new field, circuit quantum electrodynamics. Schuster and colleagues' American Physical Society 2007 paper (right) "Charge-insensitive qubit design derived from the Cooper pair box", introduced a new type of superconducting quantum circuit.
David Schuster and colleagues' Nature, 2004, paper (left) "Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics" helped spawn a new field, circuit quantum electrodynamics. Schuster and colleagues' American Physical Society, 2007, paper (right) "Charge-insensitive qubit design derived from the Cooper pair box", introduced a new type of superconducting quantum circuit.

David Schuster’s quest to make practical quantum computers a reality

With quantum computers poised to take a big step forward, we speak to an Amazon Scholar who has spent two decades driving the technology to realize its enormous potential.

To become a foundational player in the creation of a potentially world-changing technology requires the happy conjunction of talent and timing. Both can be found in physicist David Schuster, who is pioneering the technology underpinning quantum computers.

Amazon Scholar David Schuster is seen inside his lab
Amazon Scholar David Schuster joined the AWS Center for Quantum Computing in October 2020.

Schuster became an Amazon Scholar in October 2020, joining the newly established AWS Center for Quantum Computing. Passionate about computers and physics, it was during Schuster’s undergrad studies at Brown University in the early 2000s that he became aware of the nascent field of quantum computing.

“As soon as I heard about it, I was taken with the idea that I could be involved in building a completely new type of computer,” says Schuster. He saw this chance for what it was, a colossal stroke of right-place-right-time good fortune. “The opportunity to make an impact at such a fundamental level was very exciting.”

To appreciate why quantum computing has such potential, compare it to regular, or “classical”, computers. A classical computer uses digital bits to perform computations, with each bit representing either 0 or 1 at any given time. In simplistic terms, increasing the number of bits available to interact with each other increases the computational power of a computer in an additive, linear fashion. A top-end laptop will boast 32 gigabytes of RAM, which is 256,000,000,000 bits.

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A quantum computer, by contrast, uses quantum bits (qubits) to perform calculations. Each qubit can be in a simultaneously 0 and 1 at any given time. As a result of this “quantum superposition” – which only occurs at the tiniest scales – increasing the qubits results in an exponential explosion in computational power. It is estimated that a fully functional quantum computer with as little as 100 qubits could outsmart today’s most powerful supercomputers for appropriately chosen problems.

Because of the tiny scale and extreme conditions at which qubits exist, it is very difficult to create and control them, but they can nevertheless be made by harnessing a variety of quantum particles, including charged atoms, the directional spin of electrons, and photons.

But it was an experiment published just as Schuster entered grad school at Yale University that demonstrated that a superconducting circuit could be turned into a qubit, though the quantum effect lasted for less than a nanosecond. “In those early days there was a small number of people working in the field,” he recalls, “and a fundamental question was whether you could even make a circuit quantum. They were able to see it as a direct observation!”

Our approach was unique in that we leveraged powerful ideas from atomic physics and mapped them onto circuits, to build circuits that behaved like atoms
David Schuster

Duly inspired, during his PhD research in physics at Yale, Schuster and his colleagues had bold ideas about how to improve the quantum circuit and create new ways of measuring its quantum state. “Our approach was unique in that we leveraged powerful ideas from atomic physics and mapped them onto circuits, to build circuits that behaved like atoms.”

The circuit-based qubit they created contained cavities and could trap and interact, or couple, with a single microwave photons to create a two-level quantum system, with the levels representing 0s and 1s. Published in Nature in 2004, the landmark paper helped to create a new field — circuit quantum electrodynamics.

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Using such circuits as the basis of a qubit has many benefits, says Schuster. One of those is that the cavities help to protect the fragile quantum state against external interference while also allowing the qubits to interact strongly with each other, which is essential for computation.

In 2007, Schuster and his colleagues published another landmark paper, this time in Physical Review A. In it, they introduced a new type of superconducting quantum circuit, coining the term “transmon”. More cunning physics had resulted in a drastic reduction in sensitivity to external noise and an increase in the qubit-photon coupling, while maintaining the ability to control the qubits. The relatively simple transmon has become an industry standard, forming the basis of efforts at Amazon and other computing giants. Some consider the transmon the “transistor” of superconducting quantum computers.

In 2010, Schuster moved to the University of Chicago, where he set up his lab, which explores and develops a range of quantum technologies. This year, for example, the Schuster Lab team published research revealing what they dubbed a “quantum flute”, a piece of hardware able to control multiple microwave photons simultaneously. The team called the work an important step towards efficient quantum RAM and quantum processors.

A tour of David Schuster's quantum computing lab

This year, Schuster is moving to the applied physics department at Stanford University, with the rest of his lab joining him there in 2023. For the rest of 2022, however, most of Schuster’s time will be spent working at the Center for Quantum Computing.

One of the key challenges of quantum systems is that quantum states are incredibly fragile. Consider a regular computer, in which a single bit might consist of a billion electrons sloshing back and forth, with their location representing a 0 or a 1.

“If some electrons get lost, that’s OK. And redundancy is built in. But in the case of a qubit, there’s just one photon and no redundancy,” says Schuster. “And beside the possibility of losing the photon altogether, the slightest noise from the environment can disturb the quantum superposition, creating errors.” This risk of noise is one of the reasons quantum computing typically requires superconducting materials and temperatures very near absolute zero to operate at all.

And the fact that quantum states can only be maintained for a very brief time compounds the error problem.

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“I like to joke that my goal is to make qubits last for the blink of an eye,” says Schuster. Right now, state-of-the-art devices with multiple qubits might boast decoherence times of around 100 microseconds (0.0001 seconds), he says.

Decoherence means the loss of the fragile quantum state. This loss of information results in small computation errors that can quickly multiply, potentially making any output useless. And the more qubits in play, the more quickly errors can accumulate. With leading quantum processors currently containing up to a few hundred qubits (of a variety of natures), we are in what Caltech theoretical physicist and Amazon Scholar John Preskill termed the “noisy intermediate-scale quantum” (NISQ) era.

“By the time we get about 100 qubits interacting with each other, the errors become so great you can't really do much, so there's no point making a 1000-qubit system yet,” says Schuster.

Decoherence is a tractable problem, though, and it is being relentlessly addressed by researchers including Schuster and members of his lab. Fortunately, however, decoherence does not need to be totally overcome before quantum computers can successfully scale up.

Already, the accuracy of the qubits in Schuster’s quantum systems is well over 99%. In fact, as a scientific field the accuracy is getting so high that a threshold is approaching, says Schuster, beyond which sophisticated error-correcting algorithms will be able to counteract the remaining problems caused by the fragility of the qubits.

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“Once we get our error rate low enough, scaling up will actually result in even fewer errors,” he says. “Amazon's effort is focused on getting to this goal of error correction, because then we can truly make a large-scale quantum computer.”

Schuster is two decades into his quantum journey. Is it getting any easier?

“When I started it all felt impossible, but we just tried it anyway,” he says. “Now, the problems no longer necessarily seem impossible, but they are still extremely difficult.”

So why join Amazon now?

Amazon’s efforts are experimental and bold — they are trying different approaches. I think Amazon understands the true magnitude of the challenge and the ultimate value of quantum computing.
David Schuster

“The quality of the team was very appealing,” he says, “and Amazon’s efforts are experimental and bold — they are trying different approaches. I think Amazon understands the true magnitude of the challenge and the ultimate value of quantum computing to their customers through Amazon Web Services, so they are patient.”

It is well known that the arrival of quantum computing will have enormous implications for online security and encryption, because the highest levels of protection currently being employed to protect online data will not stand up to the sheer power of quantum computers. Quantum computing will bring with it uncrackable encryption.

Security implications aside, what other useful applications might we expect? There are entire classes of scientific problems that are intractable to classical computers that should succumb to quantum efforts, says Schuster. He is personally excited about the potential to better understand materials in which quantum mechanics plays an important role.

“Many special materials involve complex quantum interactions that we don't understand and, right now, about 30% of supercomputer capacity goes to solving quantum mechanical problems,” he says.

It is inefficient to solve quantum mechanical problems on a classical computer, he adds. “Very small quantum systems that involve 20 particles or states, you could maybe solve on a laptop. But if it involves 50, even the world's biggest supercomputer can't really do very much with it.”

Such research carried out on quantum computers could have big impacts on the discovery of new materials for renewable energy, computing, chemistry, medicines, and more.

There are also some surprising possibilities for Schuster’s quantum circuits.

“I never would have expected this, but I ended up getting involved in searching for dark matter,” he says. There is a type of proposed dark matter — low mass bosons — that would occasionally interact with ordinary matter, resulting in the production of a single microwave photon. And as luck would have it, Schuster’s qubit circuits are able to trap and measure these photons.

“We can use our qubits to detect these newly created photons,” he explains, “making the search for this type of dark matter about 1000 times faster!”

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We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.
IN, KA, Bangalore
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences