Amazon Scholar John Preskill on the AWS quantum computing effort

The noted physicist answers 3 questions about the challenges of quantum computing and why he’s excited to be part of a technology development project.

In June, Amazon Web Services (AWS) announced that John Preskill, the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology, an advisor to the National Quantum Initiative, and one of the most respected researchers in the field of quantum information science, would be joining Amazon’s quantum computing research effort as an Amazon Scholar.

Quantum computing is an emerging technology with the potential to deliver large speedups — even exponential speedups — over classical computing on some computational problems.

John Preskill
John Preskill, the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology and an Amazon Scholar
Credit: Caltech / Lance Hayashida

Where a bit in an ordinary computer can take on the values 0 or 1, a quantum bit, or qubit, can take on the values 0, 1, or, in a state known as superposition, a combination of the two. Quantum computing depends on preserving both superposition and entanglement, a fragile condition in which the qubits’ quantum states are dependent on each other.

The goal of the AWS Center for Quantum Computing, on the Caltech campus, is to develop and build quantum computing technologies and deliver them onto the AWS cloud. At the center, Preskill will be joining his Caltech colleagues Oskar Painter and Fernando Brandao, the heads of AWS’s Quantum Hardware and Quantum Algorithms programs, respectively, and Gil Refael, the Taylor W. Lawrence Professor of Theoretical Physics at Caltech and, like Preskill, an Amazon Scholar.

Other Amazon Scholars contributing to the AWS quantum computing effort are Amir Safavi-Naeini, an assistant professor of applied physics at Stanford University, and Liang Jiang, a professor of molecular engineering at the University of Chicago.

Amazon Science asked Preskill three questions about the challenges of quantum computing and why he’s excited about AWS’s approach to meeting them.

Q: Why is quantum computing so hard?

What makes it so hard is we want our hardware to simultaneously satisfy a set of criteria that are nearly incompatible.

On the one hand, we need to keep the qubits almost perfectly isolated from the outside world. But not really, because we want to control the computation. Eventually, we’ve got to measure the qubits, and we've got to be able to tell them what to do. We're going have to have some control circuitry that determines what actual algorithm we’re running.

So why is it so important to keep them isolated from the outside world? It's because a very fundamental difference between quantum information and ordinary information expressed in bits is that you can't observe a quantum state without disturbing it. This is a manifestation of the uncertainty principle of quantum mechanics. Whenever you acquire information about a quantum state, there's some unavoidable, uncontrollable disturbance of the state.

So in the computation, we don't want to look at the state until the very end, when we're going to read it out. But even if we're not looking at it ourselves, the environment is looking at it. If the environment is interacting with the quantum system that encodes the information that we're processing, then there's some leakage of information to the outside, and that means some disturbance of the quantum state that we're trying to process.

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So really, we need to keep the quantum computer almost perfectly isolated from the outside world, or else it's going to fail. It's going to have errors. And that sounds ridiculously hard, because hardware is never going to be perfect. And that's where the idea of quantum error correction comes to the rescue.

The essence of the idea is that if you want to protect the quantum information, you have to store it in a very nonlocal way by means of what we call entanglement. Which is, of course, the origin of the quantum computer’s magic to begin with. A highly entangled state has the property that when you have the state shared among many parts of a system, you can look at the parts one at a time, and that doesn't reveal any of the information that is carried by the system, because it's really stored in these unusual nonlocal quantum correlations among the parts. And the environment interacts with the parts kind of locally, one at a time.

If we store the information in the form of this highly entangled state, the environment doesn't find out what the state is. And that's why we're able to protect it. And we've also figured out how to process information that's encoded in this very entangled, nonlocal way. That's how the idea of quantum error correction works. What makes it expensive is in order to get very good protection, we have to have the information shared among many qubits.

Q: Today’s error correction schemes can call for sharing the information of just one logical qubit — the one qubit actually involved in the quantum computation — across thousands of additional qubits. That sounds incredibly daunting, if your goal is to perform computations that involve dozens of logical qubits.

Well, that's why, as much as we can, we would like to incorporate the error resistance into the hardware itself rather than the software. The way we usually think about quantum error correction is we’ve got these noisy qubits — it's not to disparage them or anything: they're the best qubits we've got in a particular platform. But they're not really good enough for scaling up to solving really hard problems. So the solution which at least theoretically we know should work is that we use a code. That is, the information that we want to protect is encoded in the collective state of many qubits instead of just the individual qubits.

We're interested in what is fundamentally different between classical systems and quantum systems. And I don't know a statement that more dramatically expresses the difference than saying that there are problems that are easy quantumly and hard classically.

But the alternative approach is to try to use error correction ideas in the design of the hardware itself. Can we use an encoding that has some kind of intrinsic noise resistance at the physical level?

The original idea for doing this came from one of my Caltech colleagues, Alexei Kitaev, and his idea was that you could just design a material that sort of has its own strong quantum entanglement. Now people call these topological materials; what's important about them is they're highly entangled. And so the information is spread out in this very nonlocal way, which makes it hard to read the information locally.

Making a topological material is something people are trying to do. I think the idea is still brilliant, and maybe in the end it will be a game-changing idea. But so far it's just been too hard to make the materials that have the right properties.

A better bet for now might be to do something in-between. We want to have some protection at the hardware level, but not go as far as these topological materials. But if we can just make the error rate of the physical qubits lower, then we won't need so much overhead from the software protection on top.

Q: For a theorist like you, what’s the appeal of working on a project whose goal is to develop new technologies?

My training was in particle physics and cosmology, but in the mid-nineties, I got really excited because I heard about the possibility that if you could build a quantum computer, you could factor large numbers. As physicists, of course, we're interested in what is fundamentally different between classical systems and quantum systems. And I don't know a statement that more dramatically expresses the difference than saying that there are problems that are easy quantumly and hard classically.

The situation is we don't know much about what happens when a quantum system is very profoundly entangled, and the reason we don't know is because we can't simulate it on our computers. Our classical computers just can't do it. And that means that as theorists, we don't really have the tools to explain how those systems behave.

I have done a lot of work on these quantum error correcting codes. It was one of my main focuses for almost 15 years. There were a lot of issues of principle that I thought were important to address. Things like, What do you really need to know about noise for these things to work? This is still an important question, because we had to make some assumptions about the noise and the hardware to make progress.

I said the environment looks at the system locally, sort of one part at a time. That's actually an assumption. It's up to the environment to figure out how it wants to look at it. As physicists, we tend to think physics is kind of local, and things interact with other nearby things. But until we’re actually doing it in the lab, we won't really be sure how good that assumption is.

So this is the new frontier of the physical sciences, exploring these more and more complex systems of many particles interacting quantum mechanically, becoming highly entangled. Sometimes I call it the entanglement frontier. And I'm excited about what we can learn about physics by exploring that. I really think in AWS we are looking ahead to the big challenges. I'm pretty jazzed about this.

#403: Amazon Scholars

On November 2, 2020, John Preskill joined Simone Severini, the director of AWS Quantum Computing, for an interview with Simon Elisha, host of the Official AWS Podcast.

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Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to join its team. CXBT's mission is to create best-in-class AI agents that seamlessly integrate multimodal inputs, enabling natural, empathetic, and adaptive interactions. We leverage advanced architectures, cross-modal learning, interpretability, and responsible AI techniques to provide coherent, context-aware responses augmented by real-time knowledge retrieval. As part of CXBT, we have a vision to revolutionize how we understand, test, and optimize customer experiences at scale. Where traditional testing approaches fall short, we create AI-powered solutions that enable rapid experimentation, de-risk product launches, and generate actionable insights, -all before a single real customer is impacted. Be a part of our agentic initiative and shape how Amazon leverages artificial intelligence to run tests at scale and improve customer experiences. As an Applied Scientist, you will research state-of-the-art techniques in agent-based modeling, and lead scientific innovation by building foundational agentic simulation capabilities. If you are passionate about the intersection of AI and human behavior modeling, and want to fundamentally influence how Amazon tests and improves customer experiences, this role offers a great opportunity to make your mark. Key job responsibilities - Design and implement frameworks for creating representative, diverse agents that faithfully capture real-world characteristics - Use state-of-the-art techniques in user modeling and behavioral simulation to build robust agentic frameworks - Develop data simulation approaches that mimic real-world speech interactions. - Research and implement novel algorithms and modeling techniques. - Acquire and curate diverse datasets while ensuring user privacy. - Create robust evaluation metrics and test sets to assess language model performance. - Innovate in data representation and model training techniques. - Apply responsible AI practices throughout the development process. - Write clear, scientific documentation describing methodologies, solutions, and design choices. A day in the life Our team is dedicated to improving Amazon's products and services through evaluation of the end-to-end customer experience using both internal and external processes and technology. Our mission is to deeply understand our customers' experiences, challenge the status quo, and provide insights that drive innovation to improve that experience. Through our analysis and insights, we inform business decisions that directly impact customer experience as customers of new GenAI and LLM technologies. About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers).
US, WA, Seattle
We are looking for a passionate Applied Scientist to contribute to the next generation of agentic AI applications for Amazon advertisers. In this role, you will support the development of agentic architectures, help build tools and datasets, and contribute to systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work alongside senior scientists at the forefront of applied AI, gaining hands-on experience with methods for fine-tuning, reinforcement learning, and preference optimization, while contributing to evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—contributing to customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will support the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role involves tackling well-scoped technical problems, while collaborating with engineers and product managers to bring solutions into production. Key Job Responsibilities - Contribute to building agents that guide advertisers in conversational and non-conversational experiences. - Implement model and agent optimization techniques, including supervised fine-tuning, instruction tuning, and preference optimization (e.g., DPO/IPO) under guidance from senior scientists. - Support dataset curation and tool development for MCP. - Contribute to evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Implement and iterate on agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Support prototyping of multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering, science, and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and apply findings to practical problems. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.