3Q: Making silicon-vacancy centers practical for quantum networking

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

Quantum networking is a technology that promises to enable tamper-proof communication over optical networks. Synthetic-diamond chips with so-called silicon-vacancy centers are a promising technology for quantum networking because they’re natural light emitters, and they’re small, solid state, and relatively easy to manufacture at scale. But they’ve had one severe drawback, which is that they tend to emit light at a range of different frequencies, which makes exchanging quantum information difficult.

Last year, members of Amazon’s AWS Center for Quantum Computing, together with colleagues at Harvard University, the University of Hamburg, the Hamburg Centre for Ultrafast Imaging, and the Hebrew University of Jerusalem, demonstrated a technique that promises to overcome that drawback. They will present their results in a forthcoming paper in Physical Review Letters titled “Optical entanglement of distinguishable quantum emitters”.

The first author on the paper, David Levonian, a graduate student at Harvard and a quantum research scientist at Amazon, answered three questions about the research for Amazon Science.

Q: What is quantum networking?

David Levonian: A quantum network is a technology that allows you to send quantum bits over fiber optics using single photons — single particles of light. The original interest in it is that by sending these quantum bits between users, you can generate cryptographic keys in a way that's secure. 

As you're able to get higher bandwidth and throughput through these things, you can do really cool stuff. Say Amazon, for example, has a quantum computer, and you don't have one, and you'd like to run computations on Amazon’s, but you don't want to reveal what data you're actually using or what programs you're running. 

It turns out that if your computer can connect to a quantum network, without much special hardware on your side, you can actually send programs and data and have Amazon execute them and then come back to you with a guarantee that nobody looked at any of the stuff that you were doing.

People are trying to build these networks in a variety of ways. What we work on specifically is a hardware implementation that's built on these little chips of diamond. The diamond is made of a bunch of carbon atoms. Pluck out two of those and add a silicon atom. Now there are two holes in the crystal, and the silicon sits in between the two holes. It brings an extra electron, and that electron can absorb light, and it can also store quantum information.

You can use these silicon-vacancy centers as storage registers — people call them quantum memories — to catch light and route it and do quantum operations on it. And you need that quantum memory to build the quantum network for these security applications. People also use trapped atoms or ions and other stuff, but for those you need really big machines. Our system is a little chip, which is pretty cool. 

Q: What problem does your new research solve?

DL: One of the current problems with silicon-vacancy centers is that they're not as uniform as other quantum network hardware that people use. If you have a big network of these things, you want them to communicate between each other with light. And one of the things about silicon-vacancy centers — and actually most of the defect centers in crystals — is that one silicon atom doesn't always emit the same wavelength of light that another defect can receive, so you have trouble matching up your different bits. And that's been a big barrier to actually building these things.

What we came up with is a way of making silicon vacancies that emit light at different wavelengths talk to each other. It’s based on a thought experiment from the ’90s called a Elitzur–Vaidman bomb tester. The motivating idea is that there might be a bomb somewhere, and it's so sensitive that even if you hit it with a single particle of light, it's going to go off. So you want to test whether or not it's there without hitting it with any light at all. 

It turns out there's a quantum-mechanical way to check whether there's a thing blocking light at a position without having any light interact with it, which is pretty unbelievable. But I can give you an idea of how it works. 

There's this device for light called an interferometer. You take a laser beam, you split it into two paths, and then you recombine it at a later point. And where it recombines, you put a beam splitter — a half-transparent mirror that sometimes lets light through and sometimes reflects it 90 degrees. 

So there are two directions light can come out. But if the two paths are the same length, then when the beams get to the beam splitter, they cancel each other out in one direction, and they only come out in the other direction.

Beam splitter interference.png

The weird quantum-mechanical part about this is, even if my laser is only sending a single photon at a time — so there's just one particle of light at a time — those particles still behave the same way. They can split and then interfere with themselves, even though there's only one photon at a time.

So if you send a single photon in, and that photon comes out the port where it wasn't supposed to, I know that one of the paths of the interferometer is blocked. I'm able to detect that there was something blocking one of the paths without having my photon actually run into it. 

SiV experimental setup.png
In the researchers' variation of the Elitzur–Vaidman bomb tester, if a photon reaches the detector, it conveys information about the quantum state of one silicon-vacancy qubit (SiV B), even though it interacted only with the other qubit (SiV A).

We basically we do that, except instead of having the light take two different paths, we split it in frequency. So you can build an interferometer where instead of traveling two paths, the light turns into two different frequencies, and then it gets recombined at a central one. 

Q: How does that help you do secure communication?

DL: There are two vacancy centers, and based on their state they're going to be blocking light or letting light through. You send light in at those two frequencies, and then you combine it. 

If both vacancies are letting light through, then the two paths interfere, and no light comes out at the combined frequency. And if both vacancies are blocking light, no light comes out at the combined frequency. But if one is letting light through, and one is blocking it, then light does come out at that central frequency. 

So if you get light at that combined frequency, you know one was blocking and one wasn't blocking — but you don't know which one is which. And that process generates quantum entanglement between the two vacancies. You can now use one to talk to you and another one to talk to me.

For single quantum bits, if you measure them, you usually change them. So if there's some eavesdropper, they're going to change the bits that I'm sending to you. You can say, ‘Hey, here's the statistics on my information, here’s the statistics on your information.’ If the party in between is adversarial — if they're breaking the rules and just looking at our information and then computing stuff — they're not going to be able to push this statistic above a certain threshold. So as long as that number is higher than the threshold, we know they're not cheating, and our information is secure.

Research areas

Related content

US, CA, Santa Clara
Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Guide technical direction for specific research initiatives, ensuring robust performance in production environments. - Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.
US, WA, Bellevue
At Amazon, we're working to be the world’s most customer-centric company. Driving innovation on behalf of customers is core to our mission, and this position supports one of our largest business to deliver on this mission. As member of the Operations Insights, Planning, Analytics and Technology (IPAT) team, this position owns monthly change management, Controllership and Governance, Risk and Compliance (GRC) process for World Wide Operations IPAT team. Key job responsibilities In the midst of our rapidly expanding scope, we are actively seeking a Data Scientist who possesses strategic thinking skills and a knack for creative problem-solving. This Data Scientist will play a pivotal role in supporting hyper-growth projects. Collaborating closely with cross-functional finance and business leaders within the WW Operations organization, this role should be skilled in ML models development, Optimization models, model implementation, hypothesis testing, high quality analysis, database design, be comfortable dealing with large and complex data sets, and using visualization tools. Join us on this captivating journey in an exhilarating domain, and become a part of making history!
US, NY, New York
Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Guide technical direction for specific research initiatives, ensuring robust performance in production environments. - Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.
US, CA, San Francisco
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). Key job responsibilities You will contribute directly to AI agent development in an engineering management role: leading a software development team focused on our internal platform for acquiring agentic experience at large scale. You will help set direction, align the team’s goals with the broader lab, mentor team members, recruit great people, and stay technically involved. You will be hired as a Member of Technical Staff. About the team Our lab is a small, talent-dense team with the resources and scale of Amazon. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up!
US, NY, New York
Are you a passionate Applied Scientist (AS) ready to shape the future of digital content creation? At Amazon, we're building Earth's most desired destination for creators to monetize their unique skills, inspire the next generation of customers, and help brands expand their reach. We build innovative products and experiences that drive growth for creators across Amazon's ecosystem. Our team owns the entire Creator product suite, ensuring a cohesive experience, optimizing compensation structures, and launching features that help creators achieve both monetary and non-monetary goals. Key job responsibilities As an AS on our team, you will: - Handle challenging problems that directly impact millions of creators and customers - Independently collect and analyze data - Develop and deliver scalable predictive models, using any necessary programming, machine learning, and statistical analysis software - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Participate in design and implementation across teams to contribute to initiatives and develop optimal solutions that benefit the creators organization The successful candidate is a self-starter, comfortable with a dynamic, fast-paced environment, and able to think big while paying careful attention to detail. You have deep knowledge of an area/multiple areas of science, with a track record of applying this knowledge to deliver science solutions in a business setting and a demonstrated ability to operate at scale. You excel in a culture of invention and collaboration.
US, WA, Seattle
The AWS Supply Chain organization is looking for a Sr. Manager of Applied Science to lead science and data teams working on innovative AI-powered supply chain solutions. As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Are you excited about developing state-of-the-art GenAI/Agentic AI based solutions for enterprise applications? As a Sr. Manager of Applied Scientist at AWS Supply Chain, you will bring AI advancements to customer facing enterprise applications. In this role, you will drive the technical vision and strategy for your team while fostering a culture of innovation and scientific excellence. You will be leading a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Key job responsibilities Building and mentoring teams of Applied Scientists, ML Engineers, and Data Scientists. Setting technical direction and research strategy aligned with business goals. Driving innovation in Supply Chains systems using AI/ML models and AI Agents. Collaborating with cross-functional teams to translate research into production. Managing project portfolios and resource allocation.
CA, ON, Toronto
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through 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. About our team The Targeting and Recommendations team within Sponsored Products and Brands empowers advertisers with intelligent targeting controls and one-click campaign recommendations that automatically populate optimal settings based on ASIN data. This comprehensive suite provides advanced targeting capabilities through AI-generated keyword and ASIN suggestions, sophisticated targeting controls including Negative Targeting, Manual Targeting with Product Attribute Targeting (PAT) and Keyword Targeting (KWT), and Automated Targeting (ATv2). Our vision is to build a revolutionary, highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across both conversational and traditional ad console experiences while scaling from natural language queries to proactive, intelligent guidance delivery based on deep advertiser understanding, ultimately enhancing both targeting precision and one-click campaign optimization. Through strategic partnerships across Ad Console, Sales, and Marketing teams, we identify high-impact opportunities spanning from strategic product guidance to granular keyword optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers. Key job responsibilities * Design and build targeting and 1 click recommendation agents to guide advertisers in conversational and non-conversational experience. * Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). * Collaborate with peers across engineering and product to bring scientific innovations into production. * Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. * Develop agentic architectures that integrate planning, tool use, and long-horizon reasoning. A day in the life As an Applied Scientist on our team, your days will be immersed in collaborative problem-solving and strategic innovation. You'll partner closely with expert applied scientists, software engineers, and product managers to tackle complex advertising challenges through creative, data-driven solutions. Your work will center on developing sophisticated machine learning and AI models, leveraging state-of-the-art techniques in natural language processing, recommendation systems, and agentic AI frameworks. From designing novel targeting algorithms to building personalized guidance systems, you'll contribute to breakthrough innovations
US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to support the development of algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Principal Quantum Research Scientist. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a deep and broad knowledge of experimental quantum computing and a track record of original scientific contributions. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As principal research scientist you will be expected to lead new ideas and stay abreast of the field of experimental quantum computation. Key job responsibilities Key job responsibilities In this role, you will work on improvements in all components of SC qubits quantum hardware, from qubits and resonators to quantum-limited amplifiers. You will also work on their integration into multiqubit chips. This will require designing new experiments, collecting statistically significant data through automation, analyzing the results, and summarizing conclusions in written form. Finally, you will work with hardware engineers, material scientists, and circuit designers to advance the state of the art of SC qubits hardware. About the team About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.