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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

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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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Google at APS 2024 Google Research Blog - Google Research

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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

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Quantum Poker: The States of Colorado and Illinois are Betting on Quantum  Quantum Computing Report

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Quantum Poker: The States of Colorado and Illinois are Betting on Quantum - Quantum Computing Report

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Researchers at Penn State have introduced a groundbreaking material fusion that enables a new form of superconductivity, crucial for advancing quantum computing and exploring the theoretical chiral Majorana particles. Their study demonstrates how combining magnetic materials can lead to emergent superconductivity, marking a significant leap in creating chiral topological superconductors and potentially unlocking new avenues in quantum computing research.

A new fusion of materials, each with special electrical properties, has all the components required for a unique type of superconductivity that could provide the basis for more robust quantum computing. The new combination of materials, created by a team led by researchers at Penn State, could also provide a platform to explore physical behaviors similar to those of mysterious, theoretical particles known as chiral Majoranas, which could be another promising component for quantum computing.

The new study was recently published in the journal Science. The work describes how the researchers combined the two magnetic materials in what they called a critical step toward realizing the emergent interfacial superconductivity, which they are currently working toward.

Superconductors materials with no electrical resistance are widely used in digital circuits, the powerful magnets in magnetic resonance imaging (MRI) and particle accelerators, and other technology where maximizing the flow of electricity is crucial. When superconductors are combined with materials called magnetic topological insulators thin films only a few atoms thick that have been made magnetic and restrict the movement of electrons to their edges the novel electrical properties of each component work together to produce chiral topological superconductors. The topology, or specialized geometries and symmetries of matter, generates unique electrical phenomena in the superconductor, which could facilitate the construction of topological quantum computers.

Quantum computers have the potential to perform complex calculations in a fraction of the time it takes traditional computers because, unlike traditional computers which store data as a one or a zero, the quantum bits of quantum computers store data simultaneously in a range of possible states. Topological quantum computers further improve upon quantum computing by taking advantage of how electrical properties are organized to make the computers robust to decoherence, or the loss of information that happens when a quantum system is not perfectly isolated.

Creating chiral topological superconductors is an important step toward topological quantum computation that could be scaled up for broad use, said Cui-Zu Chang, Henry W. Knerr Early Career Professor and associate professor of physics at Penn State and co-corresponding author of the paper. Chiral topological superconductivity requires three ingredients: superconductivity, ferromagnetism, and a property called topological order. In this study, we produced a system with all three of these properties.

The researchers used a technique called molecular beam epitaxy to stack together a topological insulator that has been made magnetic and an iron chalcogenide (FeTe), a promising transition metal for harnessing superconductivity. The topological insulator is a ferromagnet a type of magnet whose electrons spin the same way while FeTe is an antiferromagnet, whose electrons spin in alternating directions. The researchers used a variety of imaging techniques and other methods to characterize the structure and electrical properties of the resulting combined material and confirmed the presence of all three critical components of chiral topological superconductivity at the interface between the materials.

Prior work in the field has focused on combining superconductors and nonmagnetic topological insulators. According to the researchers, adding in the ferromagnet has been particularly challenging.

Normally, superconductivity and ferromagnetism compete with each other, so it is rare to find robust superconductivity in a ferromagnetic material system, said Chao-Xing Liu, professor of physics at Penn State and co-corresponding author of the paper. But the superconductivity in this system is actually very robust against the ferromagnetism. You would need a very strong magnetic field to remove the superconductivity.

The research team is still exploring why superconductivity and ferromagnetism coexist in this system.

Its actually quite interesting because we have two magnetic materials that are non-superconducting, but we put them together and the interface between these two compounds produces very robust superconductivity, Chang said. Iron chalcogenide is antiferromagnetic, and we anticipate its antiferromagnetic property is weakened around the interface to give rise to the emergent superconductivity, but we need more experiments and theoretical work to verify if this is true and to clarify the superconducting mechanism.

The researchers said they believe this system will be useful in the search for material systems that exhibit similar behaviors as Majorana particles theoretical subatomic particles first hypothesized in 1937. Majorana particles act as their own antiparticle, a unique property that could potentially allow them to be used as quantum bits in quantum computers.

Providing experimental evidence for the existence of chiral Majorana will be a critical step in the creation of a topological quantum computer, Chang said. Our field has had a rocky past in trying to find these elusive particles, but we think this is a promising platform for exploring Majorana physics.

Reference: Interface-induced superconductivity in magnetic topological insulators by Hemian Yi, Yi-Fan Zhao, Ying-Ting Chan, Jiaqi Cai, Ruobing Mei, Xianxin Wu, Zi-Jie Yan, Ling-Jie Zhou, Ruoxi Zhang, Zihao Wang, Stephen Paolini, Run Xiao, Ke Wang, Anthony R. Richardella, John Singleton, Laurel E. Winter, Thomas Prokscha, Zaher Salman, Andreas Suter, Purnima P. Balakrishnan, Alexander J. Grutter, Moses H. W. Chan, Nitin Samarth, Xiaodong Xu, Weida Wu, Chao-Xing Liu and Cui-Zu Chang, 8 February 2024, Science. DOI: 10.1126/science.adk1270

In addition to Chang and Liu, the research team at Penn State at the time of the research included postdoctoral researcher Hemian Yi; graduate students Yi-Fan Zhao, Ruobing Mei, Zi-Jie Yan, Ling-Jie Zhou, Ruoxi Zhang, Zihao Wang, Stephen Paolini and Run Xiao; assistant research professors in the Materials Research Institute Ke Wang and Anthony Richardella; Evan Pugh University Professor Emeritus of Physics Moses Chan; and Verne M. Willaman Professor of Physics and Professor of Materials Science and Engineering Nitin Samarth. The research team also includes Ying-Ting Chan and Weida Wu at Rutgers University; Jiaqi Cai and Xiaodong Xu at the University of Washington; Xianxin Wu at the Chinese Academy of Sciences; John Singleton and Laurel Winter at the National High Magnetic Field Laboratory; Purnima Balakrishnan and Alexander Grutter at the National Institute of Standards and Technology; and Thomas Prokscha, Zaher Salman, and Andreas Suter at the Paul Scherrer Institute of Switzerland.

This research is supported by the U.S. Department of Energy. Additional support was provided by the U.S. National Science Foundation (NSF), the NSF-funded Materials Research Science and Engineering Center for Nanoscale Science at Penn State, the Army Research Office, the Air Force Office of Scientific Research, the state of Florida and the Gordon and Betty Moore Foundations EPiQS Initiative.

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