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Amidst rapid technological advancements, Tiny AI is emerging as a silent powerhouse. Imagine algorithms compressed to fit microchips yet capable of recognizing faces, translating languages, and predicting market trends. Tiny AI operates discreetly within our devices, orchestrating smart homes and propelling advancements in personalized medicine.

Tiny AI excels in efficiency, adaptability, and impact by utilizing compact neural networks, streamlined algorithms, and edge computing capabilities. It represents a form of artificial intelligence that is lightweight, efficient, and positioned to revolutionize various aspects of our daily lives.

Looking into the future, quantum computing and neuromorphic chips are new technologies taking us into unexplored areas. Quantum computing works differently than regular computers, allowing for faster problem-solving, realistic simulation of molecular interactions, and quicker decryption of codes. It is not just a sci-fi idea anymore; it's becoming a real possibility.

On the other hand, neuromorphic chips are small silicon-based entities designed to mimic the human brain. Beyond traditional processors, these chips act as synaptic storytellers, learning from experiences, adapting to new tasks, and operating with remarkable energy efficiency. The potential applications include real-time decision-making for robots, swift medical diagnoses, and serving as a crucial link between artificial intelligence and the intricacies of biological systems.

Quantum computing, a groundbreaking field at the intersection of physics and computer science, promises to revolutionize computation as we know it. At its core lies the concept of qubits, the quantum counterparts to classical bits. Unlike classical bits, which can only be in one of two states (0 or 1), qubits can simultaneously exist in a superposition of both states. This property enables quantum computers to perform complex calculations exponentially faster than classical computers.

Superposition allows qubits to explore multiple possibilities simultaneously, leading to parallel processing. Imagine a coin spinning in the airbefore it lands, it exists in a superposition of heads and tails. Similarly, a qubit can represent both 0 and 1 until measured.

However, qubits do not stop there. They also exhibit a phenomenon called entanglement. When two qubits become entangled, their states become intrinsically linked. Changing the state of one qubit instantaneously affects the other, even if they are light-years apart. This property opens exciting possibilities for secure communication and distributed computing.

Classical bits are like light switcheseither on or off. They follow deterministic rules, making them predictable and reliable. However, their limitations become apparent when tackling complex problems. For instance, simulating quantum systems or factoring large numbers (essential for encryption breaking) is computationally intensive for classical computers.

In 2019, Google achieved a significant milestone known as quantum supremacy. Their quantum processor, Sycamore, solved a specific problem faster than the most advanced classical supercomputer. While this achievement sparked excitement, challenges remain. Quantum computers are notoriously error-prone due to decoherenceinterference from the environment that disrupts qubits.

Researchers are working on error correction techniques to mitigate decoherence and improve scalability. As quantum hardware advances, applications emerge. Quantum computers could revolutionize drug discovery by simulating molecular interactions, optimize supply chains by solving complex logistics problems, and break classical encryption algorithms.

Neuromorphic chips mimic the complex structure of the human brain. They are designed to perform tasks in a brain-inspired way. These chips aim to replicate the brains efficiency and adaptability. Inspired by its neural networks, these chips intricately weave silicon synapses, seamlessly connecting in a cerebral dance.

Unlike conventional computers, neuromorphic chips redefine the paradigm by integrating computation and memory within a single unitdistinct from the traditional separation in Central Processing Units (CPUs) and Graphics Processing Units (GPUs).

Unlike traditional CPUs and GPUs, which follow a von Neumann architecture, these chips intertwine computation and memory. They process information locally, like human brains, leading to remarkable efficiency gains.

Neuromorphic chips excel at edge AIperforming computations directly on devices rather than cloud servers. Consider your smartphone recognizing faces, understanding natural language, or even diagnosing diseases without sending data to external servers. Neuromorphic chips make this possible by enabling real-time, low-power AI at the edge.

A significant stride in neuromorphic technology is the NeuRRAM chip, which emphasizes in-memory computation and energy efficiency. In addition, NeuRRAM embraces versatility, adapting seamlessly to various neural network models. Whether for image recognition, voice processing, or predicting stock market trends, NeuRRAM confidently asserts its adaptability.

NeuRRAM chips run computations directly in memory, consuming less energy than traditional AI platforms. It supports various neural network models, including image recognition and voice processing. The NeuRRAM chip bridges the gap between cloud-based AI and edge devices, empowering smartwatches, VR headsets, and factory sensors.

The convergence of quantum computing and neuromorphic chips holds immense promise for the future of Tiny AI. These seemingly disparate technologies intersect in fascinating ways. Quantum computers, with their ability to process vast amounts of data in parallel, can enhance the training of neuromorphic networks. Imagine a quantum-enhanced neural network that mimics the brains functions while leveraging quantum superposition and entanglement. Such a hybrid system could revolutionize generative AI, enabling faster and more accurate predictions.

As we head toward the continuously evolving artificial intelligence discipline, several additional trends and technologies bring opportunities for integration into our daily lives.

Customized Chatbots are leading in a new era of AI development by democratizing access. Now, individuals without extensive programming experience can craft personalized chatbots. Simplified platforms allow users to focus on defining conversational flows and training models. Multimodal capabilities empower chatbots to engage in more nuanced interactions. We can think of it as an imaginary real estate agent seamlessly blending responses with property images and videos, elevating user experiences through a fusion of language and visual understanding.

The desire for compact yet powerful AI models drives the rise of Tiny AI, or Tiny Machine Learning (Tiny ML). Recent research efforts are focused on shrinking deep-learning architectures without compromising functionality. The goal is to promote local processing on edge devices such as smartphones, wearables, and IoT sensors. This shift eliminates reliance on distant cloud servers, ensuring enhanced privacy, reduced latency, and energy conservation. For example, a health-monitoring wearable analyze vital signs in real time, prioritizing user privacy by processing sensitive data on the device.

Similarly, federated learning is emerging as a privacy-preserving method, allowing AI models to be trained across decentralized devices while keeping raw data local. This collaborative learning approach ensures privacy without sacrificing the quality of AI models. As federated learning matures, it is poised to play a pivotal role in expanding AI adoption across various domains and promoting sustainability.

From an energy efficiency standpoint, battery-less IoT Sensors are revolutionizing AI applications for Internet of Things (IoT) devices. Operating without traditional batteries, these sensors leverage energy harvesting techniques from ambient sources like solar or kinetic energy. The combination of Tiny AI and battery-less sensors transforms smart devices, enabling efficient edge computing and environmental monitoring.

Decentralized Network Coverage is also emerging as a key trend, guaranteeing inclusivity. Mesh networks, satellite communication, and decentralized infrastructure ensure AI services reach even the most remote corners. This decentralization bridges digital divides, making AI more accessible and impactful across diverse communities.

Despite the excitement surrounding these advancements, challenges persist. Quantum computers are notoriously error-prone due to decoherence. Researchers continuously struggle with error correction techniques to stabilize qubits and improve scalability. In addition, neuromorphic chips face design complexities, balancing accuracy, energy efficiency, and versatility. Additionally, ethical considerations arise as AI becomes more pervasive. Furthermore, ensuring fairness, transparency, and accountability remains a critical task.

In conclusion, the next generation of Tiny AI, driven by Quantum Computing, Neuromorphic Chips, and emerging trends, promises to reshape the technology. As these advancements unfold, the combination of quantum computing and neuromorphic chips symbolizes innovation. While challenges persist, the collaborative efforts of researchers, engineers, and industry leaders pave the way for a future where Tiny AI transcends boundaries, leading to a new era of possibilities.

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The Next Generation of Tiny AI: Quantum Computing, Neuromorphic Chips, and Beyond - Unite.AI

Here at Zeihan On Geopolitics we select a single charity to sponsor. We have two criteria:

First, we look across the world and use our skill sets to identify where the needs are most acute. Second, we look for an institution with preexisting networks for both materials gathering and aid distribution. That way we know every cent of our donation is not simply going directly to where help is needed most, but our donations serve as a force multiplier for a system already in existence. Then we give what we can.

Today, our chosen charity is a group called Medshare, which provides emergency medical services to communities in need, with a very heavy emphasis on locations facing acute crises. Medshare operates right in the thick of it. Until future notice, every cent we earn from every book we sell in every format through every retailer is going to Medshares Ukraine fund.

And then theres you.

Our newsletters and videologues are not only free, they willalwaysbe free. We also will never share your contact information with anyone. All we ask is that if you find one of our releases in any way useful, that you make a donation to Medshare. Over one third of Ukraines pre-war population has either been forced from their homes, kidnapped and shipped to Russia, or is trying to survive in occupied lands. This is our way to help who we can. Please, join us.

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In today's fast-paced and ever-changing world, staying ahead of market trends is crucial for businesses to thrive. Traditional methods of market analysis and prediction are often limited by their reliance on classical computing algorithms. However, a revolutionary technology called Quantum AI is changing the game, offering unprecedented capabilities for adapting to changing market dynamics.

The essence of Quantum AI lies in the convergence of quantum computing and artificial intelligence. To comprehend the power of Quantum AI in market analysis, it is imperative to grasp the fundamental concepts behind quantum computing.

Quantum AI represents a cutting-edge technological frontier that holds immense promise for revolutionizing various industries, and with this knowledge in mind, your Quantum AI journey begins. By leveraging the principles of quantum mechanics and artificial intelligence, Quantum AI opens up a realm of possibilities that were previously unimaginable. The fusion of these two advanced fields not only enhances computational capabilities but also paves the way for groundbreaking advancements in data analysis, machine learning, and predictive modeling.

Quantum computing is based on the principles of quantum mechanics, which deal with the behavior of matter and energy at the atomic and subatomic levels. Unlike classical computers that use bits to represent information as either a 0 or a 1, quantum computers utilize quantum bits, or qubits, which can exist in multiple states simultaneously. This enables quantum computers to perform complex calculations at an unimaginable speed.

At the core of quantum computing is the phenomenon of superposition, where qubits can exist in a state of 0, 1, or any quantum superposition of these states. This unique property allows quantum computers to explore multiple solutions to a problem simultaneously, leading to exponential speedup in certain computational tasks. Additionally, quantum entanglement, another key principle in quantum mechanics, enables qubits to be interconnected in such a way that the state of one qubit is dependent on the state of another, regardless of the physical distance between them.

Artificial intelligence, on the other hand, focuses on the development of intelligent machines capable of simulating human-like behavior. By combining the computational power of quantum computers with AI algorithms, Quantum AI harnesses the potential for enhanced problem-solving, optimization, and prediction capabilities.

Quantum AI algorithms have the capacity to process and analyze vast amounts of data with unprecedented efficiency, leading to more accurate insights and predictions. The synergy between quantum computing and AI not only accelerates the pace of innovation but also unlocks new frontiers in machine learning, natural language processing, and robotics. As Quantum AI continues to evolve, it is poised to redefine the boundaries of what is possible in the realm of advanced computing and artificial intelligence.

Market analysis involves examining vast amounts of data to identify patterns, make predictions, and inform decision-making. Quantum AI offers unique advantages in this regard, revolutionizing traditional approaches and opening up new possibilities.

Quantum AI combines the principles of quantum mechanics with artificial intelligence, creating a powerful tool for market analysis. By harnessing the properties of superposition and entanglement, Quantum AI can process and analyze data in ways that classical computers cannot. This allows for more sophisticated modeling of market dynamics and more accurate predictions of future trends.

One of the key strengths of Quantum AI lies in its predictive capabilities. By leveraging the immense computational power of quantum computers, it becomes possible to analyze extensive historical data and identify complex patterns and trends. Quantum AI algorithms can uncover hidden correlations and make highly accurate predictions, empowering businesses with actionable insights.

Furthermore, Quantum AI can handle non-linear relationships and high-dimensional data with ease, providing a more comprehensive understanding of market behavior. This enhanced predictive ability enables businesses to anticipate market shifts, optimize investment strategies, and mitigate risks effectively.

Another significant advantage of Quantum AI is its ability to adapt to real-time market changes and make informed decisions on the fly. Traditional market analysis methods often struggle to keep up with rapidly evolving trends. Quantum AI, however, excels in handling large volumes of data in real-time, enabling businesses to react promptly to emerging opportunities and risks.

Moreover, Quantum AI's adaptive nature allows for dynamic decision-making processes that can adjust strategies in response to changing market conditions. This agility is crucial in today's fast-paced and volatile business environment, where timely decisions can mean the difference between success and failure.

Understanding and capitalizing on market trends is vital for businesses to stay competitive. Quantum AI offers unique advantages in identifying and leveraging emerging market trends.

By processing vast amounts of data from multiple sources, Quantum AI can detect subtle shifts and patterns that may indicate emerging market trends. This provides businesses with a competitive edge, allowing them to anticipate changes and adapt their strategies proactively.

Market forecasting is an essential aspect of market analysis, helping businesses make informed decisions about future market conditions. Quantum AI's ability to process vast amounts of data and identify hidden patterns and correlations allows for more accurate and reliable market forecasting. This assists businesses in making strategic decisions to drive growth and profitability.

As Quantum AI continues to evolve, its potential applications in market analysis are vast. However, several challenges and considerations need to be addressed to fully realize the benefits of this revolutionary technology.

One of the main challenges in adopting Quantum AI for market analysis is the need for highly specialized skills and resources. Quantum computing is a complex field that requires expertise in quantum physics and computer science. Collaboration between different disciplines and investments in research and development are crucial to overcoming these challenges.

The impact of Quantum AI in market analysis extends across various industries. From finance and healthcare to retail and manufacturing, businesses can leverage the power of Quantum AI to gain a competitive advantage. The ability to gather valuable insights and make informed decisions based on accurate market analysis has the potential to transform industries and reshape market dynamics.

In conclusion, Quantum AI represents a monumental leap forward in market analysis. By combining the computational power of quantum computing with the intelligence of AI algorithms, businesses can enhance their ability to adapt to changing market trends. The predictive capabilities, real-time adaptability, and accurate market analysis offered by Quantum AI can empower businesses to make informed decisions and stay ahead of the competition. Embracing Quantum AI is not only an investment in the future but also a means to drive innovation and growth in an ever-evolving market landscape.

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How Quantum AI Adapts to Changing Market Trends - Native News Online

Posted by Kate Weber and Shannon Leon, Google Research, Quantum AI Team

Today the 2024 March Meeting of the American Physical Society (APS) kicks off in Minneapolis, MN. A premier conference on topics ranging across physics and related fields, APS 2024 brings together researchers, students, and industry professionals to share their discoveries and build partnerships with the goal of realizing fundamental advances in physics-related sciences and technology.

This year, Google has a strong presence at APS with a booth hosted by the Google Quantum AI team, 50+ talks throughout the conference, and participation in conference organizing activities, special sessions and events. Attending APS 2024 in person? Come visit Googles Quantum AI booth to learn more about the exciting work were doing to solve some of the fields most interesting challenges.

You can learn more about the latest cutting edge work we are presenting at the conference along with our schedule of booth events below (Googlers listed in bold).

Session Chairs include: Aaron Szasz

This schedule is subject to change. Please visit the Google Quantum AI booth for more information.

Crumble: A prototype interactive tool for visualizing QEC circuits Presenter: Matt McEwen Tue, Mar 5 | 11:00 AM CST

Qualtran: An open-source library for effective resource estimation of fault tolerant algorithms Presenter: Tanuj Khattar Tue, Mar 5 | 2:30 PM CST

Qualtran: An open-source library for effective resource estimation of fault tolerant algorithms Presenter: Tanuj Khattar Thu, Mar 7 | 11:00 AM CST

$5M XPRIZE / Google Quantum AI competition to accelerate quantum applications Q&A Presenter: Ryan Babbush Thu, Mar 7 | 11:00 AM CST

Certifying highly-entangled states from few single-qubit measurements Presenter: Hsin-Yuan Huang Author: Hsin-Yuan Huang Session A45: New Frontiers in Machine Learning Quantum Physics

Toward high-fidelity analog quantum simulation with superconducting qubits Presenter: Trond Andersen Authors: Trond I Andersen, Xiao Mi, Amir H Karamlou, Nikita Astrakhantsev, Andrey Klots, Julia Berndtsson, Andre Petukhov, Dmitry Abanin, Lev B Ioffe, Yu Chen, Vadim Smelyanskiy, Pedram Roushan Session A51: Applications on Noisy Quantum Hardware I

Measuring circuit errors in context for surface code circuits Presenter: Dripto M Debroy Authors: Dripto M Debroy, Jonathan A Gross, lie Genois, Zhang Jiang Session B50: Characterizing Noise with QCVV Techniques

Quantum computation of stopping power for inertial fusion target design I: Physics overview and the limits of classical algorithms Presenter: Andrew D. Baczewski Authors: Nicholas C. Rubin, Dominic W. Berry, Alina Kononov, Fionn D. Malone, Tanuj Khattar, Alec White, Joonho Lee, Hartmut Neven, Ryan Babbush, Andrew D. Baczewski Session B51: Heterogeneous Design for Quantum Applications Link to Paper

Quantum computation of stopping power for inertial fusion target design II: Physics overview and the limits of classical algorithms Presenter: Nicholas C. Rubin Authors: Nicholas C. Rubin, Dominic W. Berry, Alina Kononov, Fionn D. Malone, Tanuj Khattar, Alec White, Joonho Lee, Hartmut Neven, Ryan Babbush, Andrew D. Baczewski Session B51: Heterogeneous Design for Quantum Applications Link to Paper

Calibrating Superconducting Qubits: From NISQ to Fault Tolerance Presenter: Sabrina S Hong Author: Sabrina S Hong Session B56: From NISQ to Fault Tolerance

Measurement and feedforward induced entanglement negativity transition Presenter: Ramis Movassagh Authors: Alireza Seif, Yu-Xin Wang, Ramis Movassagh, Aashish A. Clerk Session B31: Measurement Induced Criticality in Many-Body Systems Link to Paper

Effective quantum volume, fidelity and computational cost of noisy quantum processing experiments Presenter: Salvatore Mandra Authors: Kostyantyn Kechedzhi, Sergei V Isakov, Salvatore Mandra, Benjamin Villalonga, X. Mi, Sergio Boixo, Vadim Smelyanskiy Session B52: Quantum Algorithms and Complexity Link to Paper

Accurate thermodynamic tables for solids using Machine Learning Interaction Potentials and Covariance of Atomic Positions Presenter: Mgcini K Phuthi Authors: Mgcini K Phuthi, Yang Huang, Michael Widom, Ekin D Cubuk, Venkat Viswanathan Session D60: Machine Learning of Molecules and Materials: Chemical Space and Dynamics

IN-Situ Pulse Envelope Characterization Technique (INSPECT) Presenter: Zhang Jiang Authors: Zhang Jiang, Jonathan A Gross, lie Genois Session F50: Advanced Randomized Benchmarking and Gate Calibration

Characterizing two-qubit gates with dynamical decoupling Presenter: Jonathan A Gross Authors: Jonathan A Gross, Zhang Jiang, lie Genois, Dripto M Debroy, Ze-Pei Cian*, Wojciech Mruczkiewicz Session F50: Advanced Randomized Benchmarking and Gate Calibration

Statistical physics of regression with quadratic models Presenter: Blake Bordelon Authors: Blake Bordelon, Cengiz Pehlevan, Yasaman Bahri Session EE01: V: Statistical and Nonlinear Physics II

Improved state preparation for first-quantized simulation of electronic structure Presenter: William J Huggins Authors: William J Huggins, Oskar Leimkuhler, Torin F Stetina, Birgitta Whaley Session G51: Hamiltonian Simulation

Controlling large superconducting quantum processors Presenter: Paul V. Klimov Authors: Paul V. Klimov, Andreas Bengtsson, Chris Quintana, Alexandre Bourassa, Sabrina Hong, Andrew Dunsworth, Kevin J. Satzinger, William P. Livingston, Volodymyr Sivak, Murphy Y. Niu, Trond I. Andersen, Yaxing Zhang, Desmond Chik, Zijun Chen, Charles Neill, Catherine Erickson, Alejandro Grajales Dau, Anthony Megrant, Pedram Roushan, Alexander N. Korotkov, Julian Kelly, Vadim Smelyanskiy, Yu Chen, Hartmut Neven Session G30: Commercial Applications of Quantum Computing Link to Paper

Gaussian boson sampling: Determining quantum advantage Presenter: Peter D Drummond Authors: Peter D Drummond, Alex Dellios, Ned Goodman, Margaret D Reid, Ben Villalonga Session G50: Quantum Characterization, Verification, and Validation II

Attention to complexity III: learning the complexity of random quantum circuit states Presenter: Hyejin Kim Authors: Hyejin Kim, Yiqing Zhou, Yichen Xu, Chao Wan, Jin Zhou, Yuri D Lensky, Jesse Hoke, Pedram Roushan, Kilian Q Weinberger, Eun-Ah Kim Session G50: Quantum Characterization, Verification, and Validation II

Balanced coupling in superconducting circuits Presenter: Daniel T Sank Authors: Daniel T Sank, Sergei V Isakov, Mostafa Khezri, Juan Atalaya Session K48: Strongly Driven Superconducting Systems

Resource estimation of Fault Tolerant algorithms using Q Presenter: Tanuj Khattar Author: Tanuj Khattar, Matthew Harrigan, Fionn D. Malone, Nour Yosri, Nicholas C. Rubin Session K49: Algorithms and Implementations on Near-Term Quantum Computers

Discovering novel quantum dynamics with superconducting qubits Presenter: Pedram Roushan Author: Pedram Roushan Session M24: Analog Quantum Simulations Across Platforms

Deciphering Tumor Heterogeneity in Triple-Negative Breast Cancer: The Crucial Role of Dynamic Cell-Cell and Cell-Matrix Interactions Presenter: Susan Leggett Authors: Susan Leggett, Ian Wong, Celeste Nelson, Molly Brennan, Mohak Patel, Christian Franck, Sophia Martinez, Joe Tien, Lena Gamboa, Thomas Valentin, Amanda Khoo, Evelyn K Williams Session M27: Mechanics of Cells and Tissues II

Toward implementation of protected charge-parity qubits Presenter: Abigail Shearrow Authors: Abigail Shearrow, Matthew Snyder, Bradley G Cole, Kenneth R Dodge, Yebin Liu, Andrey Klots, Lev B Ioffe, Britton L Plourde, Robert McDermott Session N48: Unconventional Superconducting Qubits

Electronic capacitance in tunnel junctions for protected charge-parity qubits Presenter: Bradley G Cole Authors: Bradley G Cole, Kenneth R Dodge, Yebin Liu, Abigail Shearrow, Matthew Snyder, Andrey Klots, Lev B Ioffe, Robert McDermott, B.L.T. Plourde Session N48: Unconventional Superconducting Qubits

Overcoming leakage in quantum error correction Presenter: Kevin C. Miao Authors: Kevin C. Miao, Matt McEwen, Juan Atalaya, Dvir Kafri, Leonid P. Pryadko, Andreas Bengtsson, Alex Opremcak, Kevin J. Satzinger, Zijun Chen, Paul V. Klimov, Chris Quintana, Rajeev Acharya, Kyle Anderson, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Joseph C. Bardin, Alexandre Bourassa, Jenna Bovaird, Leon Brill, Bob B. Buckley, David A. Buell, Tim Burger, Brian Burkett, Nicholas Bushnell, Juan Campero, Ben Chiaro, Roberto Collins, Paul Conner, Alexander L. Crook, Ben Curtin, Dripto M. Debroy, Sean Demura, Andrew Dunsworth, Catherine Erickson, Reza Fatemi, Vinicius S. Ferreira, Leslie Flores Burgos, Ebrahim Forati, Austin G. Fowler, Brooks Foxen, Gonzalo Garcia, William Giang, Craig Gidney, Marissa Giustina, Raja Gosula, Alejandro Grajales Dau, Jonathan A. Gross, Michael C. Hamilton, Sean D. Harrington, Paula Heu, Jeremy Hilton, Markus R. Hoffmann, Sabrina Hong, Trent Huang, Ashley Huff, Justin Iveland, Evan Jeffrey, Zhang Jiang, Cody Jones, Julian Kelly, Seon Kim, Fedor Kostritsa, John Mark Kreikebaum, David Landhuis, Pavel Laptev, Lily Laws, Kenny Lee, Brian J. Lester, Alexander T. Lill, Wayne Liu, Aditya Locharla, Erik Lucero, Steven Martin, Anthony Megrant, Xiao Mi, Shirin Montazeri, Alexis Morvan, Ofer Naaman, Matthew Neeley, Charles Neill, Ani Nersisyan, Michael Newman, Jiun How Ng, Anthony Nguyen, Murray Nguyen, Rebecca Potter, Charles Rocque, Pedram Roushan, Kannan Sankaragomathi, Christopher Schuster, Michael J. Shearn, Aaron Shorter, Noah Shutty, Vladimir Shvarts, Jindra Skruzny, W. Clarke Smith, George Sterling, Marco Szalay, Douglas Thor, Alfredo Torres, Theodore White, Bryan W. K. Woo, Z. Jamie Yao, Ping Yeh, Juhwan Yoo, Grayson Young, Adam Zalcman, Ningfeng Zhu, Nicholas Zobrist, Hartmut Neven, Vadim Smelyanskiy, Andre Petukhov, Alexander N. Korotkov, Daniel Sank, Yu Chen Session N51: Quantum Error Correction Code Performance and Implementation I Link to Paper

Modeling the performance of the surface code with non-uniform error distribution: Part 1 Presenter: Yuri D Lensky Authors: Yuri D Lensky, Volodymyr Sivak, Kostyantyn Kechedzhi, Igor Aleiner Session N51: Quantum Error Correction Code Performance and Implementation I

Modeling the performance of the surface code with non-uniform error distribution: Part 2 Presenter: Volodymyr Sivak Authors: Volodymyr Sivak, Michael Newman, Cody Jones, Henry Schurkus, Dvir Kafri, Yuri D Lensky, Paul Klimov, Kostyantyn Kechedzhi, Vadim Smelyanskiy Session N51: Quantum Error Correction Code Performance and Implementation I

Highly optimized tensor network contractions for the simulation of classically challenging quantum computations Presenter: Benjamin Villalonga Author: Benjamin Villalonga Session Q51: Co-evolution of Quantum Classical Algorithms

Teaching modern quantum computing concepts using hands-on open-source software at all levels Presenter: Abraham Asfaw Author: Abraham Asfaw Session Q61: Teaching Quantum Information at All Levels II

New circuits and an open source decoder for the color code Presenter: Craig Gidney Authors: Craig Gidney, Cody Jones Session S51: Quantum Error Correction Code Performance and Implementation II Link to Paper

Performing Hartree-Fock many-body physics calculations with large language models Presenter: Eun-Ah Kim Authors: Eun-Ah Kim, Haining Pan, Nayantara Mudur, William Taranto, Subhashini Venugopalan, Yasaman Bahri, Michael P Brenner Session S18: Data Science, AI and Machine Learning in Physics I

New methods for reducing resource overhead in the surface code Presenter: Michael Newman Authors: Craig M Gidney, Michael Newman, Peter Brooks, Cody Jones Session S51: Quantum Error Correction Code Performance and Implementation II Link to Paper

Challenges and opportunities for applying quantum computers to drug design Presenter: Raffaele Santagati Authors: Raffaele Santagati, Alan Aspuru-Guzik, Ryan Babbush, Matthias Degroote, Leticia Gonzalez, Elica Kyoseva, Nikolaj Moll, Markus Oppel, Robert M. Parrish, Nicholas C. Rubin, Michael Streif, Christofer S. Tautermann, Horst Weiss, Nathan Wiebe, Clemens Utschig-Utschig Session S49: Advances in Quantum Algorithms for Near-Term Applications Link to Paper

Dispatches from Google's hunt for super-quadratic quantum advantage in new applications Presenter: Ryan Babbush Author: Ryan Babbush Session T45: Recent Advances in Quantum Algorithms

Qubit as a reflectometer Presenter: Yaxing Zhang Authors: Yaxing Zhang, Benjamin Chiaro Session T48: Superconducting Fabrication, Packaging, & Validation

Random-matrix theory of measurement-induced phase transitions in nonlocal Floquet quantum circuits Presenter: Aleksei Khindanov Authors: Aleksei Khindanov, Lara Faoro, Lev Ioffe, Igor Aleiner Session W14: Measurement-Induced Phase Transitions

Continuum limit of finite density many-body ground states with MERA Presenter: Subhayan Sahu Authors: Subhayan Sahu, Guifr Vidal Session W58: Extreme-Scale Computational Science Discovery in Fluid Dynamics and Related Disciplines II

Dynamics of magnetization at infinite temperature in a Heisenberg spin chain Presenter: Eliott Rosenberg Authors: Eliott Rosenberg, Trond Andersen, Rhine Samajdar, Andre Petukhov, Jesse Hoke*, Dmitry Abanin, Andreas Bengtsson, Ilya Drozdov, Catherine Erickson, Paul Klimov, Xiao Mi, Alexis Morvan, Matthew Neeley, Charles Neill, Rajeev Acharya, Richard Allen, Kyle Anderson, Markus Ansmann, Frank Arute, Kunal Arya, Abraham Asfaw, Juan Atalaya, Joseph Bardin, A. Bilmes, Gina Bortoli, Alexandre Bourassa, Jenna Bovaird, Leon Brill, Michael Broughton, Bob B. Buckley, David Buell, Tim Burger, Brian Burkett, Nicholas Bushnell, Juan Campero, Hung-Shen Chang, Zijun Chen, Benjamin Chiaro, Desmond Chik, Josh Cogan, Roberto Collins, Paul Conner, William Courtney, Alexander Crook, Ben Curtin, Dripto Debroy, Alexander Del Toro Barba, Sean Demura, Agustin Di Paolo, Andrew Dunsworth, Clint Earle, E. Farhi, Reza Fatemi, Vinicius Ferreira, Leslie Flores, Ebrahim Forati, Austin Fowler, Brooks Foxen, Gonzalo Garcia, lie Genois, William Giang, Craig Gidney, Dar Gilboa, Marissa Giustina, Raja Gosula, Alejandro Grajales Dau, Jonathan Gross, Steve Habegger, Michael Hamilton, Monica Hansen, Matthew Harrigan, Sean Harrington, Paula Heu, Gordon Hill, Markus Hoffmann, Sabrina Hong, Trent Huang, Ashley Huff, William Huggins, Lev Ioffe, Sergei Isakov, Justin Iveland, Evan Jeffrey, Zhang Jiang, Cody Jones, Pavol Juhas, D. Kafri, Tanuj Khattar, Mostafa Khezri, Mria Kieferov, Seon Kim, Alexei Kitaev, Andrey Klots, Alexander Korotkov, Fedor Kostritsa, John Mark Kreikebaum, David Landhuis, Pavel Laptev, Kim Ming Lau, Lily Laws, Joonho Lee, Kenneth Lee, Yuri Lensky, Brian Lester, Alexander Lill, Wayne Liu, William P. Livingston, A. Locharla, Salvatore Mandr, Orion Martin, Steven Martin, Jarrod McClean, Matthew McEwen, Seneca Meeks, Kevin Miao, Amanda Mieszala, Shirin Montazeri, Ramis Movassagh, Wojciech Mruczkiewicz, Ani Nersisyan, Michael Newman, Jiun How Ng, Anthony Nguyen, Murray Nguyen, M. Niu, Thomas O'Brien, Seun Omonije, Alex Opremcak, Rebecca Potter, Leonid Pryadko, Chris Quintana, David Rhodes, Charles Rocque, N. Rubin, Negar Saei, Daniel Sank, Kannan Sankaragomathi, Kevin Satzinger, Henry Schurkus, Christopher Schuster, Michael Shearn, Aaron Shorter, Noah Shutty, Vladimir Shvarts, Volodymyr Sivak, Jindra Skruzny, Clarke Smith, Rolando Somma, George Sterling, Doug Strain, Marco Szalay, Douglas Thor, Alfredo Torres, Guifre Vidal, Benjamin Villalonga, Catherine Vollgraff Heidweiller, Theodore White, Bryan Woo, Cheng Xing, Jamie Yao, Ping Yeh, Juhwan Yoo, Grayson Young, Adam Zalcman, Yaxing Zhang, Ningfeng Zhu, Nicholas Zobrist, Hartmut Neven, Ryan Babbush, Dave Bacon, Sergio Boixo, Jeremy Hilton, Erik Lucero, Anthony Megrant, Julian Kelly, Yu Chen, Vadim Smelyanskiy, Vedika Khemani, Sarang Gopalakrishnan, Toma Prosen, Pedram Roushan Session W50: Quantum Simulation of Many-Body Physics Link to Paper

The fast multipole method on a quantum computer Presenter: Kianna Wan Authors: Kianna Wan, Dominic W Berry, Ryan Babbush Session W50: Quantum Simulation of Many-Body Physics

The quantum computing industry and protecting national security: what tools will work? Presenter: Kate Weber Author: Kate Weber Session Y43: Industry, Innovation, and National Security: Finding the Right Balance

Novel charging effects in the fluxonium qubit Presenter: Agustin Di Paolo Authors: Agustin Di Paolo, Kyle Serniak, Andrew J Kerman, William D Oliver Session Y46: Fluxonium-Based Superconducting Quibits

Microwave Engineering of Parametric Interactions in Superconducting Circuits Presenter: Ofer Naaman Author: Ofer Naaman Session Z46: Broadband Parametric Amplifiers and Circulators

Linear spin wave theory of large magnetic unit cells using the Kernel Polynomial Method Presenter: Harry Lane Authors: Harry Lane, Hao Zhang, David A Dahlbom, Sam Quinn, Rolando D Somma, Martin P Mourigal, Cristian D Batista, Kipton Barros Session Z62: Cooperative Phenomena, Theory

*Work done while at Google

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Scientists have created an error-free quantum bit, or qubit, from a single pulse of light, raising hopes for a light-based room-temperature quantum computer in the future.

While bits in classical computers store information as either 1 or 0, qubits in quantum computers can encode information as a superposition of 1 and 0, meaning one qubit can adopt both states simultaneously.

When quantum computers have millions of qubits in the future, they will process calculations in a fraction of the time that today's most powerful supercomputers can. But the most powerful quantum computers so far have only been built with roughly 1,000 qubits.

Most qubits are made from a superconducting metal, but these need to be cooled to near absolute zero to achieve stability for the laws of quantum mechanics to dominate. Qubits are also highly prone to failure, and if a qubit fails during a computation, the data it stores is lost, and a calculation is delayed.

One way to solve this problem is to stitch multiple qubits together using quantum entanglement, an effect Albert Einstein famously referred to as "spooky action at a distance. By connecting them intrinsically through space and time so they share a single quantum state, scientists can form one "logical qubit," storing the same information in all of the constituent physical qubits. If one or more physical qubits fails, the calculation can continue because the information is stored elsewhere.

Related: How could this new type of room-temperature qubit usher in the next phase of quantum computing?

But you need many physical qubits to create one logical qubit. Quantum computing company QuEra and researchers at Harvard, for example, recently demonstrated a breakthrough in quantum error correction using logical qubits, publishing their findings Dec. 6, 2023, in the journal Nature. This will lead to the launch of a quantum computer with 10 logical qubits later this year but it will be made using 256 physical qubits.

For that reason, researchers are looking at alternative ways to create qubits and have previously demonstrated that you can create a physical qubit from a single photon (particle of light). This can also operate at room temperature because it doesn't rely on the conventional way to make qubits, using superconducting metals that need to be cooled. But single physical photonic qubits are still prone to failure.

In a study published in August 2023 in the journal Nature, scientists showed that you can successfully entangle multiple photonic qubits. Building on this research, the same team has now demonstrated that you can create a de facto logical qubit which has an inherent capacity for error correction using a single laser pulse that contains multiple photons entangled by nature. They published their findings Jan. 18 in the journal Science.

"Our laser pulse was converted to a quantum optical state that gives us an inherent capacity to correct errors," Peter van Loock, a professor of theoretical quantum optics at Johannes Gutenberg University of Mainz in Germany and co-author of the Dec. 6 study, said in a statement. "Although the system consists only of a laser pulse and is thus very small, it can in principle eradicate errors immediately."

Based on their results, there's no need to create individual photons as qubits from different light pulses and entangle them afterward. You would need just one light pulse to create a "robust logical qubit," van Loock added.

Although the results are promising, the logical qubit they created experimentally wasn't good enough to achieve the error-correction levels needed to perform as a logical qubit in a real quantum computer. Rather, the scientists said this work shows you can transform a non-correctable qubit into a correctable qubit using photonic methods.

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Qubits are notoriously prone to failure but building them from a single laser pulse may change this - Livescience.com