3 questions with Antia Lamas-Linares about the Nobel Prize in physics

The head of Amazon Web Services’ quantum communication program on the Nobel winners’ influence on her field.

Last week, the Swedish Academy announced that John Clauser, Alain Aspect, and Anton Zeilinger had won the Nobel Prize in physics “for experiments with entangled photons, establishing the violation of Bell inequalities, and pioneering quantum information science”.

Antia Lamas-Linares, quantum networking lead at Amazon Web Services and an expert in quantum optics, is seen looking into the camera
Antia Lamas-Linares, a principal research scientist with Amazon Web Services (AWS) and head of the AWS Center for Quantum Networking.

When two quantum subsystems — particles, atoms, or molecules — are “entangled”, then measurements performed on one are correlated with measurements performed on the other. The experimental setup that Clauser, Aspect, and Zeilinger investigated — in which entangled quantum subsystems have been widely separated — is closely related to those used in modern quantum networks.

The first application of quantum networking is likely to be quantum key distribution, in which two parties exchange private cryptographic keys and use quantum entanglement to ensure that their communication channel hasn’t been compromised.

On the occasion of the Swedish Academy’s announcement, Amazon Science asked Antia Lamas-Linares, who leads Amazon Web Services’ Center for Quantum Networking, three questions about Clauser, Aspect, and Zeilinger’s work and its implications for her own field.

  1. Q. 

    One of the things that Clauser and Aspect did to win the Nobel Prize was a set of experiments that challenged the “hidden-variable hypothesis”. What is the hidden-variable hypothesis, and what were their experiments?

    A. 

    At the beginnings of quantum mechanics, when people started looking at this theory that was really successful at doing things like predicting the spectra coming out of atoms, and they started looking at the implications for how we understand physical theories, there was this epic battle that went on for years between Einstein and Bohr, two giants of early 20th-century physics.

    The problem was that as you look deeply into the theory, you realize that there is, for example, inherent randomness. You can prepare a couple of systems in identical ways, and you measure something about the system, and the outcomes are different. And they are different for very fundamental reasons. It's not just, oh, we just didn't have enough information. No, even with perfect information, the outcomes of identical measurement can be different. This was not compatible with how physical theories were supposed to work.

    Entanglement has made this progression from being an uncomfortable property of quantum systems — and a philosophical question — to something that constitutes the basis of quantum technologies.
    Antia Lamas-Linares

    It became a sort of hobby of Einstein’s to come up with little paradoxes based on these things that challenged the way we understand physical theories, from a very foundational point of view. In quantum mechanics, you can formally write something that we call an entangled state, and this is a state that involves more than one particle or more than one subsystem. It describes correlations between parts of that system.

    Einstein’s point was, if I take the two subsystems and separate them very far away, then I can measure something on one and know the outcome in the other one instantaneously, or faster than light communication, which is not compatible with relativity. His conclusion was that there must be an underlying theory that “explained” the quantum-mechanical correlations and the apparent randomness of measurement outcomes.

    Essentially, the results look random because there are these hidden variables that we don't know about. But if we knew their values, the results would be predictable. These theories became known as “hidden variable” theories.

    It wasn't until the ’60s that John Bell wrote a beautiful, very simple theorem where he said, let's assume that there is some underlying information, some underlying physical theory that works in this general way. Can I have a set of measurements that have different outcomes from the quantum-mechanical model?

    Related content
    New method enables entanglement between vacancy centers tuned to different wavelengths of light.

    This is called the Bell inequality. It says, if you measure a particular sequence of correlations and you get a value below a certain threshold, then there could be an underlying hidden-variable theory of quantum mechanics. But if the value is above this threshold, then quantum mechanics cannot be explained by hidden variables. At this point, no matter how uncomfortable we are with the implications of quantum mechanics, we don’t get to pretend there is an underlying theory that will explain it all away.

    It really changes our perception of the nature of reality. The randomness in measurement results is fundamental to nature. It's not just an accident from the lack of information about the hidden variables.

    Eventually, people said, Well, okay, let's test these things. For that, you need to make entangled particles, and you need to measure them in the way prescribed by Bell’s theorem. That's essentially what John Clauser and Alain Aspect did during the late ’70s and early ’80s, with increasing levels of sophistication. Anton Zeilinger wasn't part of those Bell inequality measurements, but he used entanglement in a multitude of ground-breaking experiments, such as quantum teleportation, entanglement swapping, and the generation of tripartite entangled states.

    Clauser experiment
    In his original experiment, John Clauser used irradiated calcium atoms to emit pairs of entangled photons and polarizing filters to measure correlations between their polarizations.
    Adapted from a figure by Johan Jarnestad for the Royal Swedish Academy of Sciences

  2. Q. 

    So what does this all have to do with quantum networking?

    A. 

    With quantum networking, the two sides start by sharing entangled particles. The idea is that the outcomes on both sides are perfectly correlated, but if a malicious party tries to measure in-between, the correlations are broken, and this is detectable by the legitimate parties. It's related to the fact that quantum mechanics has properties like the no-cloning theorem that ensure that unknown quantum states cannot be perfectly copied. So you cannot just take any particle that is coming by and make a copy, because this inevitably alters the original state.

    Related content
    Collaboration will seek to advance the development of a quantum internet.

    So the two sides take a subset of the results, and using classical communication, they say, Hey, let's check that nobody has interfered with our measurements. What did you get for the first one? How about number 3,047? And they can check that the correlations are as expected from quantum theory. They sacrifice maybe 10% of their data to check this, and if the correlations hold, they can be sure that nobody has otherwise tampered with the entangled particles.

    Now to be clear, they're not sharing a message. They're sharing correlated random numbers, obtained from measurements on the entangled pair of particles. Correlated random numbers are exactly what you need for a symmetric cryptographic key. When you use a key as part of a cryptographic system, you need two parties to share a secret. It doesn't matter what the secret is, as long as it is not predictable. In this way quantum mechanics gives us the two requirements for a cryptographic key: randomness and privacy.

  3. Q. 

    It’s been 50 years since Clauser’s original experiment. How has our thinking about these questions changed in the interim?

    A. 

    What’s exciting about this is that entanglement has made this progression from being an uncomfortable property of quantum systems — and a philosophical question — to something that constitutes the basis of quantum technologies. We no longer consider entanglement an uncomfortable consequence of quantum mechanics. It is now a resource.

    Related content
    Among the ‘first wave’ of scientists to gain a PhD in quantum technology, the senior manager of research science discusses her two-decade-long career journey.

    Alain Aspect has said that when he was doing his PhD, he was repeatedly discouraged from pursuing these experiments because working on the foundations of physics was a career killer. This is not what you do if you want to be successful.

    My own PhD centered on building entanglement sources, which is a very engineering-focused task. I needed to engineer a system — in my case, a nonlinear crystal — to give me higher-quality entangled photon pairs. I wasn’t trying to prove philosophical questions regarding entanglement.

    You could consider large parts of what Oskar [Painter, head of quantum hardware at Amazon Web Services] does and what I do as “entanglement engineering”. Our groups in AWS do quantum engineering, and entanglement is a vital resource that we produce, channel, and measure. That is a pretty radical evolution from where things stood when John, Alain, and Anton did their groundbreaking work.

Research areas

Related content

US, WA, Seattle
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies to deliver game changing value to our customers. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon’s Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon’s on-line retail business. As an economist on our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies with the potential to deliver game changing value to our customers. This is an opportunity for a high-energy individual to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.
US, CA, San Francisco
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of statistical inference, experimentation design, economic theory and machine learning to design new methods and pricing strategies for assessing pricing innovations. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon's Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon's on-line retail business. As an economist on our team, you will will have the opportunity to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in experimentation design, applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals.
US, WA, Bellevue
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 Python is necessary, and experience with SQL and UNIX 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, scientists, and product managers. 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 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US
The Amazon Supply Chain Optimization Technology (SCOT) organization is looking for an Intern in Economics to work on exciting and challenging problems related to Amazon's worldwide inventory planning. SCOT provides unique opportunities to both create and see the direct impact of your work on billions of dollars’ worth of inventory, in one of the world’s most advanced supply chains, and at massive scale. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Knowledge of econometrics and Stata/R/or Python is necessary, and experience with SQL, Hadoop, and Spark 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, scientists, and product managers. 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 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for an Intern Economist with excellent coding skills to design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. As a Science Intern, you will have access to large datasets with billions of transactions and will translate ambiguous fee related business problems into rigorous scientific models. You will work on real world problems which will help to inform strategic direction and have the opportunity to make an impact for both Amazon and our Selling Partners.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, Hadoop, Spark and Python 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, scientists, and product managers. 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 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, artificial intelligence, human-robot interaction, optimization and more.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, artificial intelligence, human-robot interaction, optimization and more.
US, WA, Bellevue
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 Python is necessary, and experience with SQL and UNIX 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, scientists, and product managers. 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 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, MA, Boston
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics, a wholly owned subsidiary of Amazon.com, empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas. AR is seeking uniquely talented and motivated data scientists to join our Global Services and Support (GSS) Tools Team. GSS Tools focuses on improving the supportability of the Amazon Robotics solutions through automation, with the explicit goal of simplifying issue resolution for our global network of Fulfillment Centers. The candidate will work closely with software engineers, Fulfillment Center operation teams, system engineers, and product managers in the development, qualification, documentation, and deployment of new - as well as enhancements to existing - operational models, metrics, and data driven dashboards. As such, this individual must possess the technical aptitude to pick-up new BI tools and programming languages to interface with different data access layers for metric computation, data mining, and data modeling. This role is a 6 month co-op to join AR full time (40 hours/week) from July – December 2023. The Co-op will be responsible for: Diving deep into operational data and metrics to identify and communicate trends used to drive development of new tools for supportability Translating operational metrics into functional requirements for BI-tools, models, and reporting Collaborating with cross functional teams to automate AR problem detection and diagnostics