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Unlocking quantum computing power: Automated protocol design for quantum advantage How to Design a Classically Difficult Random Quantum Circuit for Quantum Computational Advantage Experiments. Researchers unveiled an automated protocol-design approach that could unlock the computational power of quantum devices sooner than we imagined. Finding the optimal random quantum circuit first requires exhausting all possible patterns, then estimating the classical simulation cost for each of them and selecting the one with the highest cost. The classical simulation cost is highly dependent on the algorithm used, but the traditional algorithm currently has the limitation that the estimation time is too long. The new method proposed by the authors uses the Schrödinger-Feynman algorithm. This algorithm divides the system into two subsystems and represents their quantum states as state vectors. The cost of the algorithm is determined by the entanglement generated between the two subsystems. Evaluating complexity using this algorithm requires much less time, and the advantages become more apparent as the random quantum circuit size increases. The authors experimentally proved the effectiveness of the random quantum circuit obtained by the proposed method compared with other algorithms. Five random quantum circuits were generated in the Zuchongzhi 2.0 quantum processor, each with a different Schrödinger-Feynman algorithm complexity. Experimental results show that circuits with higher complexity also have higher costs. The rivalry between classical and quantum computing is expected to conclude within a decade. This new approach maximizes the computational power of quantum computing without imposing new requirements on the quantum hardware. In addition, the main reason this new approach can obtain random quantum circuits with higher classical simulation costs may be the faster growth of quantum entanglement. In the future, understanding this phenomenon and its underlying physics may help researchers explore practical applications using quantum advantage experiments. https://lnkd.in/g9ZXYkhF

Quantum technology relam will introduce us the new factor in the knowledge of quantum computing were quantum computing will discover an advanced nature of computing for future in order to do that we have to go from a different step and foundation from quantum division to quantum computer here to technologies On a high level, quantum simulation is an emerging technology that stands as one of the most promising applications for quantum computing Computer simulation has long supported science and innovation by mimicking real-world phenomena. However, certain levels of physical simulation are beyond the computational reach of even the most powerful supercomputers.But where classical computing’s capabilities stop, quantum computing could start. Using quantum computers to simulate quantum systems could allow researchers to model molecular dynamics more accurately, triggering innovation and revolutionizing the process of materials science and discovery for numerous industries. QAI Ventures is a patient capital investor and accelerator, partnering with startups and spinouts to bring the positive transformational power of quantum technologies from labs to markets and societies. This article delves into the potential of quantum simulation and the need for investment to push this potential to practicality.Quantum simulations can be performed using classical computing systems to simulate physical processes and quantum computers to simulate physical phenomena. These are two distinct approaches to simulation that the quantum community often and counterintuitively uses interchangeably without clarification.While quantum simulations are currently achieved by combining classical computing and artificial intelligence (AI), simulations powered by quantum computing offer an intriguing new approach. Quantum computers, once robust enough, may have the advantage because they harness the unique properties of quantum mechanics.To effectively model natural phenomena at the molecular level, researchers must capture how matter and energy operate, which is an incredibly computationally intensive task. To do that, scientists use classical computers and machine learning, with or without quantum computation, to develop applicable models.The reason for integrating quantum computers into these processes is that they are uniquely suited for precise quantum simulation as they use quantum bits that use quantum phenomena as part of their information processing.computing and AI delivers value to numerous industries, some experts believe that quantum computer-based simulations could be one of the more near-term realizable applications. For relatively small-scale quantum systems, tens or hundreds of qubits may be all that is needed for practical physical simulation by quantum computers.Quantum computing is still in the early stages of development, but interest in the computing software and hardware i sunny faridi pause the classification of programming in today's computing

Microsoft has demonstrated a case study combining HPC, quantum computing, and AI to study catalytic reactions, using logical qubits to improve the reliability of quantum simulations 👏🏻 The study involved over a million density functional theory (DFT) calculations using the Azure Quantum Elements platform to map out reaction networks, identifying over 3,000 unique molecular configurations. https://lnkd.in/esfps8hC #QuantumComputing #Microsoft #Ai #ChemicalReactions

Quantum Computing What is Quantum Computing? Quantum computing uses specialized technology—including computer hardware and algorithms that take advantage of quantum mechanics—to solve complex problems that classical computers or supercomputers can’t solve, or can’t solve quickly enough. Fact about IBM Quantum System ***Today, IBM Quantum makes real quantum hardware—a tool that scientists only began to imagine three decades ago—available to hundreds of thousands of developers. Their engineers deliver ever-more-powerful superconducting quantum processors at regular intervals, alongside crucial advances in software and quantum-classical orchestration. This work drives toward the quantum computing speed and capacity necessary to change the world.*** Why do need Quantum Computers ? When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores capable of running very large calculations and advanced artificial intelligence. However, even supercomputers are binary code-based machines reliant on 20th-century transistor technology. They struggle to solve certain kinds of problems. If a supercomputer gets stumped, that's probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it's often due to complexity. Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Identifying subtle patterns of fraud in financial transactions or new physics in a supercollider are also complex problems. There are some complex problems that we do not know how to solve with classical computers at any scale. The real world runs on quantum physics. Computers that make calculations by using the quantum states of quantum bits should in many situations be our best tools for understanding it. Here are top 5 uses of Quantum Computing 1. Finance 2. Drug analysis and development 3. Fertilization 4. Task optimization 5. Material science Why quantum computers are faster that it can beat classical and supercomputers ? A classical computer might be great at difficult tasks like sorting through a big database of molecules. But it struggles to solve more complex problems, like simulating how those molecules behave. Here quantum computers plays efficient role in calculating complex problems that classical computers take so much time to do it. Quantum algorithms take a new approach to these sorts of complex problems—creating multidimensional computational spaces. This turns out to be a much more efficient way of solving complex problems like chemical simulations.

Qruise and Quantum Machines Integrate Solutions to Refine Precision in Quantum Research and Development by Cierra Choucair via The Quantum Daily ([Global] Quantum Computing) URL: https://ift.tt/OuIaJ46 Insider Brief: Qruise and Quantum Machines announced a partnership to integrate Qruise’s machine learning software with Quantum Machines’ hardware. Recent joint research between the companies demonstrated an improved fidelity of superconducting quantum gates using Qruise’s Model Learning algorithm to characterize and correct signal distortions. This partnership will provide quantum researchers with additional tools for higher control and potentially faster device setup, which could, in turn, accelerate R&D cycles. PRESS RELEASE — In a recent release, Qruise, a leader in machine learning software for physics-based R&D, and Quantum Machines, a provider of processor-based quantum controllers, announced a partnership dedicated to the advancement of quantum technology. This collaboration will integrate Qruise’s software with Quantum Machines’ hardware, creating a solution for researchers and developers in the quantum computing field. The partnership follows recent joint research between the two companies that achieved notable enhancements in the fidelity of superconducting quantum gates. Building on this success, Quantum Machines will offer Qruise’s software as an add-on to its OPX Control Platform, enabling users to perform high-precision control, calibration, and characterisation of quantum devices. Advancing Quantum Gate Fidelity through Joint Research In a collaborative study conducted earlier this year, Qruise and Quantum Machines applied Qruise’s advanced Model Learning algorithms to improve superconducting flux-based entangling gates. This joint research focused on one of the key challenges highlighted in the study: scaling quantum computing through achieving high-fidelity two-qubit gates for reliable quantum operations. According to the research, the distortion of control signals caused by imperfections in control electronics can lead to inaccuracies in quantum gate operations and ultimately hinder the performance and reliability of quantum systems. Traditional approaches to resolving this focus on correcting specific signal distortions. However, these methods often require running multiple complex experiments, each targeting a different aspect of the signal. As noted in the research, Qruise’s Model Learning algorithm offers a more efficient solution by integrating data from multiple experiments to develop a highly accurate digital twin of the quantum system, allowing researchers to pinpoint and correct various sources of noise and distortion in one go. Using this method, the research team identified and learned 83 system parameters, including the qubit-qubit coupling and flux periodicity, by analyzing a single plot. The results were validated through three methods and revealed improved phase shift correction by 10%....

Quantum Computing and Machine Learning: Promising Intersection Explored in New Study The intersection of quantum computing and machine learning is a promising area of research, with the potential to revolutionize many industries. Quantum computers, known as Noisy Intermediate Scale Quantum (NISQ) devices, can overcome limitations of classical computing, but are susceptible to noise. Machine learning, a problem-solving approach where machines learn to tackle tasks by processing large volumes of data, faces challenges due to the substantial need for data and computational resources. This study explores the effectiveness of hybrid quantum-classical algorithms on small-scale quantum devices, revealing comparable or superior performance to classical algorithms. https://lnkd.in/ec8dJvcg

Integrating quantum computing, digital twins, photonic chips, and artificial intelligence (AI) presents exciting possibilities for advancing technology. By integrating these aspects researchers and engineers can unlock new possibilities for advancing quantum and addressing complex challenges across a wide range of fields, from materials science and chemistry to cryptography and optimization. Quantum Computing for Simulation and Optimization: Quantum computing can be used to enhance the capabilities of digital twins by performing complex simulations and optimizations more efficiently where the algorithms can model quantum systems and phenomena with high accuracy, allowing digital twins to simulate real-world scenarios with unprecedented detail. AI algorithms can be employed to interpret the results of quantum simulations, extract insights, and optimize system performance.  In materials science, AI could analyze this aspect to discover new materials with desirable properties for specific applications. Photonic Chips for Quantum Information Processing: Photonic chips offer a promising platform for implementing quantum computing hardware.  Photonic qubits, which are quantum bits of information encoded in photons, can be manipulated and processed using integrated photonic circuits.  Photonic chips have advantages such as low noise, high speed, and scalability, making them suitable for quantum information processing tasks. AI techniques can be utilized to optimize the design and operation of photonic chips for quantum computing applications.  Machine learning algorithms can analyze experimental data, identify patterns, and suggest improvements to enhance the performance of photonic quantum processors. Integration with Digital Twins: Digital twins can serve as virtual testbeds for designing and optimizing photonic chips for quantum computing.  By creating digital replicas of photonic chip designs, engineers can simulate their behavior, analyze performance metrics, and identify areas for improvement before fabrication. Quantum-enhanced digital twins can leverage computing capabilities to simulate the behavior of photonic quantum circuits with high fidelity. These simulations can help researchers explore novel algorithms, evaluate the impact of noise and errors, and optimize the performance of photonic quantum processors. AI for Optimization and Control: AI algorithms can play a crucial role in optimizing the operation of photonic quantum systems.  Machine learning techniques can be used to adaptively control the parameters of photonic circuits, compensate for errors, and maximize the success rates of operations. AI-driven control strategies can also be employed to enhance the robustness and reliability of photonic quantum processors.  Reinforcement learning algorithms, for example, can learn optimal control policies by interacting with the quantum hardware in real-time, leading to more efficient and stable operation.

This is a series of posts leading from considerations about Quantum Computing towards thoughts about Quantum Supported or Quantum Enabled Fusion. This is resulting from a "discussion" with GPT "Quantum Guide" which is aimed to better understand the foundations of Quantum Computing. My question: "If we consider now that every possible gate operation in Quantum Computing is indeed a symmetry operation over GNF transformations of the three quarks of the proton, how could this help us to understand quantum computing (operations) in a deeper way?" Part 3 / 6: 3. Quark-Level Computational Basis: If quantum gates act on quark-level GNF transformations, then the computational basis of quantum systems could be extended to include the internal quark structure of protons: - Qubits might not just be logical states |0⟩ and |1⟩ at the electron level, but they could also encode symmetry states of quarks. The binary states in a qubit might be projections of quark symmetries, hinting at a richer internal structure within each qubit. - The quark symmetries could introduce a new type of quantum degree of freedom at the proton level, leading to hidden quantum states that are currently not part of traditional quantum computing frameworks. This could explain why certain operations lead to probabilistic outcomes. The quark symmetries within the proton might introduce subtle influences that alter the outcome of a qubit operation, contributing to the probability distribution seen in quantum measurements. Images by GPT "Quantum Guide" Here are the three illustrations based on the points from the "New Computation Models" section: 1. **Quark-Level Computational Basis**: Illustrates how qubit states are projections of deeper quark symmetries within protons, providing a richer computational basis.

Quantum Computing and Machine Learning: Promising Intersection Explored in New Study The intersection of quantum computing and machine learning is a promising area of research, with the potential to revolutionize many industries. Quantum computers, known as Noisy Intermediate Scale Quantum (NISQ) devices, can overcome limitations of classical computing, but are susceptible to noise. Machine learning, a problem-solving approach where machines learn to tackle tasks by processing large volumes of data, faces challenges due to the substantial need for data and computational resources. This study explores the effectiveness of hybrid quantum-classical algorithms on small-scale quantum devices, revealing comparable or superior performance to classical algorithms. https://lnkd.in/eFT_7BSn

SO WHAT IS QUANTUM COMPUTING? (A quick read) A classical computer performs operations using classical bits (0 or 1), while a quantum computer uses quantum bits (qubits) that can represent both 0 and 1 simultaneously. This property endows quantum computers with superior computational power. Various physical objects can serve as qubits, such as a single photon, nucleus, or electron. So, how does this work? Electrons possess a magnetic field, essentially making them tiny bar magnets, a property known as spin. When placed in a magnetic field, they align with the field, representing the lowest energy state, also known as the zero state or spin down for the electron. We can also place the electron in another state, spin up, which requires some energy. Up to this point, this resembles a classical bit, with spin up (1) and spin down (0). However, quantum objects can exist in both states simultaneously. An electron can exist in a quantum superposition before measurement, where its coefficients indicate the relative probability of finding the electron in one state or the other. To understand this in terms of computing power, consider two interacting electrons. There are four possible states for these electrons, and quantum mechanics allows us to create superpositions of each with corresponding coefficients. In a classical example, you have only two bits, so two qubits contain the equivalent of four classical bits of information. With three spins, you'd have eight different states, whereas, classically, it’s just three bits. As the number of qubits increases, the amount of equivalent classical information grows exponentially. N qubits contain 2^n classical bits of information. For instance, with 300 qubits, you'd have 2^300 classical bits, an amount comparable to the number of particles in the universe. However, there’s a catch. Although quantum states can exist in any combination, measurement forces them into one of the basis states, losing all other information about the state before the measurement. Thus, you wouldn't want the final result of your quantum computation to be a complex superpositional state because you cannot measure it directly. Therefore, designing logical operations to reach a measurable final state is crucial. This is why quantum computers are not replacements for classical computers. They are not universally faster; they excel at specific types of calculations that leverage quantum superpositions for computational parallelism. For generic calculations, quantum computers may not offer significant improvement. Quantum computers might not perform every operation faster than classical computers; they could even be slower for some tasks. However, they significantly reduce the number of operations required to arrive at certain results. Source: https://lnkd.in/dEnb6cEp