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.

Preskill portrait.jpg
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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About the Author
Larry Hardesty is the editor of the Amazon Science blog. Previously, he was a senior editor at MIT Technology Review and the computer science writer at the MIT News Office.

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Come and join the Database Migration Accelerator team that helps our customers migrate to the cloud. We are on a mission to transform legacy enterprise workloads into modern AWS native application architectures. We achieve this by utilizing cutting edge tools, sophisticated engineering systems and database expertise. We provide fixed price and high speed migrations to the cloud. Database Migration Accelerator is combining various AWS cloud platform services into one product which would serve our customers.We are a team of professionals that are forward-looking and using latest technology offerings (AWS cloud services, Machine Learning, Mathematical Optimization, Relational and NoSQL databases) to build new capability to operationalize and automate migration methodologies. Databases Services at AWS cover a range of data platforms including: Amazon Aurora, DynamoDB, Redshift, Athena, as well as AWS Database Migration Service, Data Pipeline, Glue and more. As each service grows, so does adoption by customers world-wide.We have an opportunity for a Senior Applied Scientist who is passionate about combining machine learning with developing new offerings for the cloud and is enthusiastic about applying bold new ideas to real-world problems.Joining the AWS Database Services team as a Senior Applied Scientist gives you the opportunity to:· Work for a company that’s at the forefront of the cloud computing space.· Be a part of something unique what no other previously developed and was successful.· Design machine learning solutions to intelligently move enterprises to the cloud.· Truly own the solution from concept design through development to production.· Join the team whose activities are regularly called out publicly by AWS CEO Andy JassyWork/Life BalanceOur team places value on work-life balance. Our team is global, based in the US and Poland. Our Poland teams typically start later in the day to have a couple of hours of overlap with US teams.Mentorship & Career GrowthOur team is dedicated to supporting new team members in an environment that celebrates knowledge sharing and mentorship. Our senior engineers mentor more junior engineers through one-on-one mentoring and collaborative code reviews. Projects and tasks are assigned in a way that leverages your strengths and helps you further develop your skillset.Inclusive Team CultureWe get to build a really cool service and the main contributing factor to our success is the inclusive and welcoming culture that we embody every day.We welcome teammates who are enthusiastic, empathetic, curious, motivated, reliable, and able to collaborate with a diverse team of peers.As a Senior Applied Scientist, your responsibilities will include:· Building new cloud based Machine Learning solutions and algorithms to accelerate migrations to the cloud· Participating in hands-on machine learning experimentation and delivering the results in the form of new products· Creating technical strategies and delivering with limited guidance· Solving difficult and complex software problems. Your solutions should be extensible· Cross-collaborating with a number of different teams with overlapping work, including solutions architects, developers, product managers, senior leaders, and many more· Mentoring more junior members of the team or collaboration partners
PL, Gdansk
Come and join the Database Migration Accelerator team that helps our customers migrate to the cloud. We are on a mission to transform legacy enterprise workloads into modern AWS native application architectures. We achieve this by utilizing cutting edge tools, sophisticated engineering systems and database expertise. We provide fixed price and high speed migrations to the cloud. Database Migration Accelerator is combining various AWS cloud platform services into one product which would serve our customers.We are a team of professionals that are forward-looking and using latest technology offerings (AWS cloud services, Machine Learning, Mathematical Optimization, Relational and NoSQL databases) to build new capability to operationalize and automate migration methodologies. Databases Services at AWS cover a range of data platforms including Amazon Aurora, DynamoDB, Redshift, Athena, as well as AWS Database Migration Service, Data Pipeline, Glue and more. As each service grows, so does adoption by customers world-wide.We have an opportunity for a Senior Applied Scientist who is passionate about mathematical optimization with developing new offering for the cloud and is enthusiastic about applying bold new ideas to real-world problems.Joining the AWS Database Services team as a Senior Applied Scientist gives you the opportunity to:· Work for a company that’s at the forefront of the cloud computing space· Be a part of something unique what no other previously developed and was successful.· Design mathematical optimization algorithms to intelligently move enterprises to the cloud.· Truly own solution from concept design through development to production· Join the team whose activities are regularly called out publicly by AWS CEO Andy JassyWork/Life BalanceOur team places value on work-life balance. Our team is global, based in the US and Poland. Our Poland teams typically start later in the day to have a couple of hours of overlap with US teams.Mentorship & Career GrowthOur team is dedicated to supporting new team members in an environment that celebrates knowledge sharing and mentorship. Our senior engineers mentor more junior engineers through one-on-one mentoring and collaborative code reviews. Projects and tasks are assigned in a way that leverages your strengths and helps you further develop your skillset.Inclusive Team CultureWe get to build a really cool service and the main contributing factor to our success is the inclusive and welcoming culture that we embody every day.We welcome teammates who are enthusiastic, empathetic, curious, motivated, reliable, and able to collaborate with a diverse team of peers.As a Senior Applied Scientist, your responsibilities will include:· Build new cloud based mathematical solutions and algorithms to accelerate migrations to the cloud· Participate in algorithms experimentation and deliver the results in the form of new products· Develop scalable optimization algorithms for moving customer workloads to cloud environments. Create technical strategies and deliver with limited guidance· Creating technical strategies and delivering with limited guidance· Solving difficult and complex software problems. Your solutions should be extensible· Cross-collaborating with a number of different teams with overlapping work, including solutions architects, developers, product managers, senior leaders, and many more· Mentoring more junior members of the team or collaboration partners
US, NY, New York
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLCPosition: Data Scientist IILocation: New York, NYPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
Amazon Web Services is looking for world class scientists to join the Security Analytics and AI Research group within AWS Security Services. This team is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). On this team, you will invent and implement innovative solutions for never-before-solved problems. If you have a passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Key Responsibilities:· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment· Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services· Report results in a scientifically rigorous way· Interact with security engineers and related domain experts to dive deep into the types of challenges that we need innovative solutions forHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
AT, Graz
Location: Graz, AustriaDuration: 3-6 monthsAbout us:We are working on the future. If you are seeking an innovative, fast-paced environment where you can apply state-of-the-art technologies to solve extreme-scale real world challenges and provide visible benefit to end-users, this is your opportunity: Come work on the Amazon Prime Air team!We are looking for an outstanding computer vision / machine learning applied scientist who combines superb technical, research and analytical capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch applied scientists. We are looking for someone who innovates and loves solving hard problems. You will work hard, have fun, and of course, make history!Export Control License: This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf.About those internship roles:Are you inspired by innovation? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? If your answer is yes then you’ll fit right in here. We are a smart team of doers that work passionately to apply cutting edge advances in autonomous drone delivery and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Prime Air and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.Prime Air at Amazon is seeking a talented and motivated student to join the Prime Air team for an Internship assignment. The candidate will have the opportunity to work with senior engineering staff on existing and new modules and systems. The ideal candidate has solid coding skills, enjoys problem solving and has at least one of the following: strong computer vision skills, strong machine-learning skills, or, strong computer graphics skills.Applicants should have at a minimum one quarter/semester remaining after their internship concludes.As an Applied Scientist intern, you will be responsible for data-driven improvements to our product. Regardless of the team you join, your work will directly impact our customers. You will:· Collaborate with colleagues from science, engineering and business backgrounds.· Present proposals and results in a clear manner backed by data and coupled with actionable conclusions.· Push the state-of-the-art in computer vision, machine learning or computer graphics for large-scale real world problems.· Summarize and present your contributions in a white paper or peer reviewed scientific publication.
US, WA, Seattle
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, UNIX, and Sawtooth would be a plus.These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement.Roughly 50% of research assistants from previous cohorts have converted to full time data science or economics employment at Amazon. If you are interested, send your CV, transcripts, and a cover letter to our mailing list at econ-internship@amazon.com.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation