25 years of QIP

As the major quantum computing conference celebrates its anniversary, we ask the conference chair and the head of Amazon’s quantum computing program to take stock.

In 1981, at a conference in Boston, the physicist Richard Feynman suggested that a computer that harnessed quantum-mechanical phenomena could easily perform computations that would be difficult — even prohibitively difficult — for a classical computer.

QIP 25.png
Thomas Vidick (left), a professor of computing and mathematical sciences at Caltech and chair of the 25th Annual Conference on Quantum Information Processing, and Simone Severini (right), director of quantum computing for Amazon Web Services.

In 1994, the Bell Labs mathematician Peter Shor showed that a quantum computer — still an entirely hypothetical device — could factor numbers exponentially faster than a classical computer can. “Shor’s algorithm constituted the killer app that got everybody interested,” the MIT quantum computing researcher Seth Lloyd once said.

Three years later, in 1998, the first Conference on Quantum Information Processing (QIP) was held in Aarhus, Denmark. Since then, quantum computing has become a major research initiative at leading tech companies, and QIP has become the premier conference in the field of quantum information processing.

Related content
Researchers affiliated with Amazon Web Services' Center for Quantum Computing are presenting their work this week at the Conference on Quantum Information Processing.

To mark QIP’s 25th anniversary, Amazon Science asked two prominent quantum information scientists — Thomas Vidick, a professor of computing and mathematical science at Caltech and chair of this year’s QIP, and Simone Severini, director of quantum computing at Amazon Web Services — a pair of questions about how far the field has come in the last 25 years and how far it still has to go.

What’s surprised you most about what we’ve learned about quantum information science in the past 25 years?

Thomas Vidick: Well, honestly, that we can run a 20-qubit quantum algorithm, and it actually looks like it is going as planned. While my whole research is premised on the assumption that quantum mechanics is a sufficiently accurate description of nature that it makes sense to study its consequences for computation, truly "seeing" such a computation take place was a revelation. (I need to use quotes because of course we can't see a quantum computation take place without affecting it. But for small computations we can plot outcome statistics in a very detailed way.) For me, the revelation came when I saw the results of an implementation of Simon's algorithm for a four-bit secret a few years ago, by the Monroe group working with ion traps. I couldn't believe it: it sampled exactly the right strings.

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

Going back not even 25 years, but 15 years, which is when I first learned, while studying for a master’s, that quantum computation was a thing, the fact that it could become a reality was absolutely not on my radar, nor I believe on most theorists', let alone experimentalists'. I think that learning that quantum computing works, as opposed to believing that it does, is having a major impact on how we approach quantum information science.

Simone Severini: Quantum information science contributed to a rich interplay between physics, mathematics, and computation. That interplay gave rise to new techniques that cross the boundaries of these fields.

Severini@QIP01.jpg
Ernesto F. Galvão, leader of the Quantum and Linear-Optical Computation group at the International Iberian Nanotechnology Laboratory; Iordanis Kerenidis, head of quantum algorithms for QC Ware, a senior researcher at the French National Center for Scientific Research, and director of the Paris Center for Quantum Computing; and Severini at the fourth QIP, in Amsterdam, 2001.
Courtesy of Simone Severini

A beautiful example is the application of quantum complexity theory to solve in the negative the Connes embedding problem, by Ji, Natarajan, Vidick, Wright, and Yuen, in 2020. Connes’ embedding problem is a problem in abstract algebra, where an “algebra” is a combination of a set, a group of operators, and axioms that describe how the operators are applied. The real numbers are one example of a set, and the arithmetic operators are one example of a group of operators, but in abstract algebra, these could be anything.

Connes’ problem asks whether one class of algebras is contained in another class. Alain Connes formulated it in 1976 in a paper that led to his Fields Medal in 1982. Since then, the problem has been reformulated in several different branches of mathematics; multiple conferences have been dedicated to just this problem.

Related content
New approach reduces the number of ancillary qubits required to implement the crucial T gate by at least an order of magnitude.

The result of Ji et al. is a surprising case where notions and techniques that are part of the quantum information science toolbox turned out to be impactful in other areas of mathematics and the natural sciences. And it’s just one of many exciting examples.

What do you see as the biggest remaining challenge in the field?

Thomas Vidick: The obvious challenges faced by the field are, on the experimental front, realizing a quantum computer, and in particular reducing error rates while scaling up system sizes, and on the theoretical front, finding applications for such a computer. While as a theorist I tend to think of the first as a hard, but definitely solvable, engineering challenge, I am less confident in the eventual outcome of the second: beyond niche applications in quantum simulation and the widespread deployment of post-quantum cryptography, will quantum computers make their way into daily consumer life?

This is the billion-dollar question; but to be honest, it's not the one I'm most preoccupied about. Closer to my heart, and perhaps less obvious, is the challenge of maintaining the coherence, vitality, and impact that quantum information science has had over the past quarter-century, all the way through the next quarter-century (and more!). When I look back to the first QIP programs, there was little concern for near-term applicability of the theoretical results. In contrast, I am probably not overestimating much by asserting that nearly half the scientific program of QIP in the past couple years has had some "near-term" motivation.

In the complex and fast-paced world of today, we should not forget that fundamental science is still the root of future innovation.
Simone Severini

This evolution reflects a genuine and justified enthusiasm for the potential practical impact of our work as researchers, which 25 years ago was such a distant prospect that it wasn't even in the back of our minds. What consequences this evolution will have on the health and diversity of our field remains to be seen. Will QIP split into "applied" and "theoretical" QIPs, and if so, will this split be done in a manner that maintains strong interaction between the two components? Will theoretical work in quantum information retain its strength and stature within the computer science community, independently of the success or failure of experimental approaches?

Related content
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.

Researchers in our field have always fought, with great success, for demonstrating the importance of the ideas of quantum information, much more so than its possible practical relevance. Now that the latter is becoming reality, we should not forget the former.

Simone Severini: It’s gripping to observe how quantum information science has overflowed from academia into industry. The broader interest that we are seeing today in this field is a great opportunity, but there are risks. I believe that the biggest nontechnical challenge for the field is to grow organically and steadily in an environment that tries to balance scientific research and engineering, while proposing commercial routes with future impact. In the complex and fast-paced world of today, we should not forget that fundamental science is still the root of future innovation. To realize the long-term promises of quantum technologies, like processors and communication devices that can outperform classical engineering, it’s important to set the right expectations today. In this context, it's essential to support education and scientific discovery and stress the need for long-term visions.

Research areas

Related content

US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
US, WA, Seattle
Amazon Prime Video is changing the way millions of customers enjoy digital content. Prime Video delivers premium content to customers through purchase and rental of movies and TV shows, unlimited on-demand streaming through Amazon Prime subscriptions, add-on channels like Showtime and HBO, and live concerts and sporting events like NFL Thursday Night Football. In total, Prime Video offers nearly 200,000 titles and is available across a wide variety of platforms, including PCs and Macs, Android and iOS mobile devices, Fire Tablets and Fire TV, Smart TVs, game consoles, Blu-ray players, set-top-boxes, and video-enabled Alexa devices. Amazon believes so strongly in the future of video that we've launched our own Amazon Studios to produce original movies and TV shows, many of which have already earned critical acclaim and top awards, including Oscars, Emmys and Golden Globes. The Global Consumer Engagement team within Amazon Prime Video builds product and technology solutions that drive customer activation and engagement across all our supported devices and global footprint. We obsess over finding effective, programmatic and scalable ways to reach customers via a broad portfolio of both in-app and out-of-app experiences. We would love to have you join us to build models that can classify and detect content available on Prime Video. We need you to analyze the video, audio and textual signal streams and improve state-of-art solutions while being scalable to Amazon size data. We need to solve problems across many cultures and languages, working alongside an operations team generating labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art. As a member of our team, you will apply your deep knowledge of Computer Vision and Machine Learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on addressing fundamental computer vision models like video understanding and video summarization in addition to building appropriate large scale datasets. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions. We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.
US, NY, New York
Amazon is looking for an outstanding Data Scientist to help build the next generation of selection systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection systems that determine which products Amazon offers in our fastest delivery programs. We build state-of-the-art models leveraging tools from machine learning, numerical optimization, natural language processing, and causal inference to automate the management of Amazon's sub-same day (SSD) selection at scale. We sit as a part of one of the largest and most sophisticated supply chains in the world. We operate a highly cross-functional team across software, science, analytics, and product to define and scalably execute the strategic direction of SSD and speed selection more broadly. As a Data Scientist on the team, you will work with scientists, engineers, product managers, and business stakeholders to conduct analyses that reveal key business insights and leverage data science and machine learning techniques to develop new models and solutions to emergent business problems. Key job responsibilities Understanding business problems and translate them to appropriate scientific solutions; Using data to provide new insights and clarity to ambiguous situations; Designing effective, scalable, and achievable solutions to key business problems; Developing the right set of metrics to evaluate efficacy of your models and solutions; Prototyping and analyzing new models and business logic; Communicating, both written and verbally, with both technical and business audiences throughout each project; Contributing to the scientific community across the organization
US, CA, Palo Alto
Join a team working on cutting-edge science to innovate search experiences for Amazon shoppers! Amazon Search helps customers shop with ease, confidence and delight WW. We aim to transform Search from an information retrieval engine to a shopping engine. In this role, you will build models to generate and recommend search queries that can help customers fulfill their shopping missions, reduce search efforts and let them explore and discover new products. You will also build models and applications that will increase customer awareness of related products and product attributes that might be best suited to fulfill the customer needs. Key job responsibilities On a day-to-day basis, you will: Design, develop, and evaluate highly innovative, scalable models and algorithms; Design and execute experiments to determine the impact of your models and algorithms; Work with product and software engineering teams to manage the integration of successful models and algorithms in complex, real-time production systems at very large scale; Share knowledge and research outcomes via internal and external conferences and journal publications; Project manage cross-functional Machine Learning initiatives. About the team The mission of Search Assistance is to improve search feature by reducing customers’ effort to search. We achieve this through three customer-facing features: Autocomplete, Spelling Correction and Related Searches. The core capability behind the three features is backend service Query Recommendation.
US, CA, Palo Alto
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning (ML) pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for energetic, entrepreneurial, and self-driven science leaders to join the team. Key job responsibilities As a Principal Applied Scientist in the team, you will: Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our ML innovations to the broader internal & external scientific community.
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!"?
US, CA, Santa Clara
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: * Hardware-informed efficient model architecture, training objective and curriculum design * Distributed training, accelerated optimization methods * Continual learning, multi-task/meta learning * Reasoning, interactive learning, reinforcement learning * Robustness, privacy, model watermarking * Model compression, distillation, pruning, sparsification, quantization About Us Inclusive Team Culture Here 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 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. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on 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. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.