Articles tagged with Quantum Algorithms
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Physicists at MIT and Caltech developed a new benchmarking protocol to characterize the fidelity of quantum analog simulators, enabling high precision characterization. The protocol analyzes random fluctuations in atomic-scale systems, revealing universal patterns that can be used to gauge the accuracy of these devices.
AQT at Berkeley Lab organized a workshop on classical control systems for quantum computing, bringing together industry leaders and researchers to share experimental control advances. The workshop highlighted the need for advanced features in classical control electronic systems to optimize quantum computer performance.
A new quantum algorithm allows for the direct calculation of energy derivatives, a crucial step in molecular geometry optimization, using only one query on a quantum computer. This breakthrough enables the computation of energy derivatives with respect to nuclear coordinates in a single calculation.
Researchers at Paderborn University developed a new algorithm for quantum computing in chemistry, reducing qubit count and increasing parallelisation. This allows for the simulation of larger molecules and improved accuracy despite 'quantum noise'.
Scientists from Paderborn and Ulm universities create a programmable optical quantum memory, enabling the efficient growth of large entangled states. This breakthrough milestone brings researchers closer to practical applications of useful quantum technologies.
Researchers at NICT have developed a new systematic method to identify the optimal quantum operation sequence, enabling efficient task execution and contributing to improving quantum computer performance and reducing environmental impact. The method uses GRAPE algorithm to analyze all possible sequences of elementary quantum operations.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
Researchers at Princeton University have discovered a new method to correct errors in quantum computers, potentially clearing a major obstacle. The technique increases the acceptable error rate four-fold, making it practical for current quantum systems.
Researchers from Aarhus and Berlin have developed an algorithm that can predict how complex molecules will bind to the surface of catalysts. This is achieved through a machine-learning approach inspired by 3D Tetris, allowing computers to quickly identify promising catalysts.
A research team from HKU discovered clear evidence of a highly entangled quantum matter, known as a quantum spin liquid (QSL), through large-scale simulations on supercomputers. The findings suggest the existence of QSLs in nature and provide new insights into topological order and quantum entanglement.
The guide introduces quantum algorithms and their implementation on existing hardware, providing a thorough introduction for would-be programmers. It surveys 20 quantum algorithms and guides readers through implementing them on IBM's 5-qubit quantum computer, covering the basics of quantum programming and in-depth algorithm explanations.
The University of Illinois Chicago has joined the Co-design Center for Quantum Advantage, a US Department of Energy-funded center focused on building scalable quantum computer systems. The partnership will open new opportunities for UIC students in quantum engineering and collaboration with researchers.
Researchers at the University of Innsbruck have successfully implemented a universal set of gates on encoded logical quantum bits, enabling fault-tolerant quantum computing. The demonstration showcases two essential gates: CNOT and T-gates, which are crucial for programming all algorithms.
The Berkeley Lab team has demonstrated a three-qubit native quantum gate, the iToffoli gate, with high fidelity of 98.26%. This breakthrough enables universal quantum computing and reduces circuit running times.
Researchers from Harvard University and QuEra Computing have demonstrated a breakthrough application of neutral-atom quantum processors to solve practical optimization problems. The team achieved unprecedented quantum hardware power, showcasing a super-linear quantum speed-up compared to classical algorithms.
The project explores symmetries underlying fundamental questions in computer science, statistics, and quantum information. Researchers aim to develop efficient numerical algorithms and new structural insights using a novel optimisation paradigm.
A Harvard-led team created a new method for processing quantum information that allows for the dynamic change of atoms' layout during computation, expanding capabilities and enabling self-correction of errors. This approach uses entanglement to connect atoms remotely and can process exponentially large amounts of information.
Researchers at the University of Innsbruck have proposed a method to solve optimization problems using neutral atoms and four-qubit operations. The algorithm can be realized on existing quantum hardware by optimizing laser pulse durations in a feedback loop.
Physicists at the University of Innsbruck have developed a programmable quantum sensor that can measure with even greater precision, using tailored entanglement to optimize performance. The sensor autonomously finds its optimal settings through free parameters, promising a significant advantage over classical computers.
Researchers unveil an algorithm that reduces statistical errors in quantum chemistry calculations, allowing for accurate ground state energy calculation. This enables chemists to develop new materials for sustainable goals such as nitrogen fixation and hydrolysis.
A €16 million project, PhotonQ, is developing a photonic quantum processor to process qubits and reduce error rates. The processor will enable rapid scaling to relevant qubit numbers for practical applications.
A team of researchers from Ritsumeikan University developed an unprecedented stream cipher using chaos theory to create highly secure cryptographic systems. The new system is resistant to statistical attacks and eavesdropping, even against quantum computers, making it a promising solution for post-quantum era cryptosystems.
Scientists at the University of Tokyo have created a novel machine learning algorithm that allows for efficient and accurate verification of time-dependent quantum devices. The algorithm, inspired by quantum reservoir computing, leverages memory effects in these systems to improve verification efficiency.
Researchers have successfully cooled a pair of highly charged ions to an unprecedentedly low temperature of 200 µK using quantum algorithms. This achievement brings the team closer to building an optical atomic clock with highly charged ions, which could potentially be more accurate than existing clocks.
Researchers at Lawrence Berkeley National Laboratory's Advanced Quantum Testbed demonstrated a method to reduce error rates in quantum algorithms, leading to more accurate and stable computations. The technique, known as randomized compiling, can suppress one of the most severe types of errors: coherent errors.
Scientists from TUM and Google Quantum AI used a highly controllable quantum processor to simulate exotic particles called anyons, which can emerge as collective excitations in two-dimensional systems. The study reveals the properties of these particles through braiding statistics, a key feature of topologically ordered states.
Researchers at University of Helsinki have developed a new method to speed up calculations on quantum computers, reducing the number of measurements required and increasing efficiency. This breakthrough could lead to faster and more sustainable quantum computing.
Researchers at Osaka City University developed a new quantum algorithm that calculates potential energy curves of molecules without controlled time evolutions. This addresses issues with conventional quantum phase estimation algorithms, enabling parallel processing and efficient full-CI calculations.
A recent study published in PRX Quantum reveals that quantum machine learning algorithms are hindered by excessive entanglement, leading to a phenomenon known as barren plateaus. By limiting depth and connectivity, researchers propose a solution to avoid these regimes and successfully train quantum neural networks.
Researchers used a supercomputer to emulate Google's quantum processor and discovered a reachability deficit, a performance limitation induced by a problem's constraint-to-variable ratio. The study showed that future experiments will require significantly more quantum resources to overcome this limit.
Researchers from Osaka City University have developed a Bayesian phase difference estimation (BPDE) algorithm that directly calculates the energy difference between two relevant quantum states. This breakthrough enables precise accuracy in chemistry problems and overcomes limitations of conventional full-CI calculations.
Phasecraft's new research improves Hamiltonian simulation for near-term quantum computers by five orders of magnitude, making it possible to simulate complex materials and chemistry applications within 2-3 years. The breakthrough algorithm can run on noisy, intermediate-scale quantum hardware, accelerating the pace of real-world impact.
The DTU researchers have developed a universal measurement-based optical quantum computer platform, enabling the execution of any arbitrary algorithm. The platform is scalable to thousands of qubits and can be connected directly to a future quantum Internet.
Hybrid classical/quantum algorithms enable the use of limited qubits and error-prone hardware for tasks such as simulations, factoring numbers, and big-data analysis. Researchers have developed variational quantum algorithms that adapt to hardware constraints.
Researchers at Q-CTRL and University of Sydney have developed a machine learning technique to identify sources of error in quantum computers. This technique enables hardware developers to pinpoint performance degradation with unprecedented accuracy, accelerating the development of useful quantum computers.
Researchers developed a miniaturized and high-speed quantum random number generator (QRNG) with an output rate of 18.8 Gbps, exceeding previous records. The QRNG uses a photonic integrated chip and optimized real-time post-processing to achieve this feat.
Researchers developed a new hybrid computing approach, combining reliability of classical computers with strength of quantum systems. This method enables near-term applications and discoveries in fields like carbon dioxide removal and pharmaceutical design.
Researchers at ETH Zurich have developed a new approach to prove the robustness conditions of certain quantum-based machine learning models, guaranteeing reliable results. The team's work explores protection against errors and hackers, paving the way for more accurate and trustworthy quantum machine learning applications.
A team from the University of Bristol's QETLabs developed an algorithm that uses machine learning to reverse engineer Hamiltonian models and formulate approximate models for quantum systems. This breakthrough enables the automated characterization of new devices, such as quantum sensors.
Researchers have identified a new technique to test the quality of quantum correlations in large systems, reducing resource intensity and increasing noise resilience. By combining two processes, they enable efficient certification of correlations in complex systems.
A Skoltech researcher has discovered a new model of quantum computation, the variational model, which enables universal computation using limited control over a quantum simulator. This breakthrough bridges the gap between traditional quantum simulators and quantum computers.
Researchers have established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers. This breakthrough solves a key problem of useability for quantum machine learning and takes an important step toward achieving quantum advantage.
Researchers at Osaka City University have developed a new quantum algorithm, BxB, which calculates energy differences directly to predict electronic states of atoms and molecules with chemical precision. The algorithm achieves this with half the number of qubits required by the existing Quantum Phase Estimation (QPE) method.
Researchers at Florida State University developed a method to automatically infer parameters used in quantum Boltzmann machines, which can be applied to train artificial neural networks for tasks like image recognition and drug discovery.
The Wallenberg Centre for Quantum Technology is doubling its annual budget to SEK 80 million, enabling the development of a more powerful quantum computer. The new funding will focus on improving qubit quality and software, with plans to increase the number of researchers from 60 to 100.
A Berkeley Lab team successfully simulated a complex aspect of particle collisions using a quantum algorithm, accounting for neglected quantum effects. The researchers' approach meshes quantum and classical computing, allowing for efficient resources and improved accuracy.
A team from Osaka City University developed a quantum algorithm that can accurately calculate energy differences between the electronic ground and excited spin states of open-shell molecular systems. This breakthrough enables efficient calculations for complex molecules, potentially revolutionizing chemical and industrial applications.
Researchers at Chalmers University successfully execute QAOA algorithm on 2-qubit quantum computer to solve aircraft route assignment problem, demonstrating potential for practical applications. The algorithm's scalability suggests it could handle larger problems, paving the way for a useful quantum computer.
The Association for Computing Machinery has published the first issue of its new peer-reviewed journal, Transactions on Quantum Computing, focusing on the theory and practice of quantum computing. The journal aims to publish high-impact research papers and surveys on topics in quantum information science.
Researchers from the University of Bristol and Phasecraft have developed new strategies to solve the Fermi-Hubbard model using optimised quantum circuits with limited device size. The study suggests that current supercomputers are unable to solve instances of the model, but near-term quantum devices can.
Researchers from Tokyo University of Science design a new quantum circuit that calculates the fast Fourier transform, a key algorithm in engineering. The QFFT circuit exploits superposition of states to greatly increase computational speed and is more versatile than traditional QFT.
A new algorithm called Variational Fast Forwarding (VFF) can simulate quantum systems for longer periods than current quantum computers can handle. This allows scientists to tackle complex problems that were previously unsolvable due to decoherence, which degrades quantum coherence.
Researchers at Osaka City University have developed a quantum algorithm that removes pesky spin contaminants from chemical calculations on quantum computers. This breakthrough enables precise and accurate predictions of atomic and molecular behavior, which is crucial for applications such as pharmaceuticals and materials research.
Zhao is developing numerical algorithms to describe superfluidity and magnetic orders in repulsively interacting Fermi gases of ultracold atoms. His work aims to understand complex quantum matter, enabling scientists to design better materials.
Researchers from Kazan Federal University developed a quantum algorithm to solve the Dyck problem, which is crucial for parsers and compilers. The new algorithm can solve the problem in just 40 seconds on a quantum computer.
Researchers at Purdue University have developed a new theory that may lead to systematic design of quantum algorithms, outperforming classical computers. The theory identifies large groups of quantum states with polynomial complexity, allowing for efficient coefficient sampling procedures to determine their suitability.
The team created a new routing algorithm that allows qubits to directly interact with many more qubits, giving rise to higher expected computational power. This approach outperforms the 'superconducting' devices in calculating the expected computational power.
A University of Arizona team advances low-density parity check codes for quantum computers, enabling fault-tolerant and ultra-fast computation. The development is crucial for solving complex equations and analyzing phenomena that classical computers can't handle.
Researchers have developed a quantum algorithm that can diagnose noise in large quantum systems, enabling the creation of more reliable and scalable quantum computers. The algorithm was tested on a 14-qubit machine and discovered correlations not previously detected.
The ACM SIGCOMM conference emphasizes the importance of communications technologies in maintaining daily life. The virtual event showcases research papers on various topics, including programmable switches and video applications. Keynote speakers are recognized for their contributions to data network architectures, and the conference a...