Articles tagged with Quantum Algorithms
The project, funded by a $927,203 grant, uses virtual reality and machine learning to identify misconceptions in quantum information science. UCF will develop desktop and smartphone versions of QubitVR for broader impacts, aiming to empower students and professionals to harness the power of quantum computing.
A Harvard team has successfully developed a self-correcting quantum computer using neutral atom arrays, achieving near-flawless performance with extremely low error rates. The breakthrough enables the creation of large-scale, error-corrected devices based on neutral atoms.
Researchers from Tokyo Institute of Technology have successfully tested quantum annealing on a D-Wave 2000Q quantum computer for optimizing continuous-variable functions. The study found that QA can significantly outperform state-of-the-art classical algorithms, especially when the energy barrier is high.
Rice University researchers have been awarded a 4-year, $1.2 million grant from the Department of Energy to evaluate different physical systems used to build quantum computers. The project aims to provide a framework for comparing the viability and computational potential of various approaches to building quantum computers.
A team of Cornell researchers has found a promising quantum state called a 'quantum spin-glass' while studying random algorithms for error correction in quantum computing. This discovery could lead to new strategies for protecting qubits from environmental noise and errors.
Theoretical physicists at Los Alamos National Laboratory have developed a new quantum computing paradigm that uses natural quantum interactions to process real-world problems faster than classical computers. The approach eliminates many challenging requirements for quantum hardware.
A new quantum-classical hybrid algorithm has been developed to accelerate dynamic mode decomposition for high-dimensional time series analysis. The algorithm can operate with a small number of samples and has a quantum advantage in the analysis of high-dimensional time series.
Researchers developed a new technique called zero noise extrapolation (ZNE) that allows noisy quantum computers to produce accurate results for specific calculations. This breakthrough could enable the use of quantum computing for cutting-edge physics problems and improve classical algorithms.
Researchers at the University of Innsbruck have developed reversible parity gates for integer factorization using quantum computers. This breakthrough enables the solution of a crucial pillar of cryptography, allowing for faster and more efficient factorization.
Researchers developed an algorithm using quantum computing to study amine reactions and find new compounds for carbon capture. The algorithm can quickly screen thousands of molecules and structures, vital for practical applications in fields like carbon capture.
Researchers and industry leaders from around the world will gather in Sydney to discuss key areas of quantum computing, communications, sensing, training, entrepreneurship, and policy. The three-day event is expected to feature insights on cyber security, sustainability, and commercialization, with over 700 attendees.
A new programming technique in quantum computing could help solve complex optimization problems in global supply chains. The FALQON framework uses feedback to adapt the structure of the algorithm, allowing the quantum computer to efficiently reroute shipping fleets and manage logistics.
A team of researchers developed a model-free approach using deep reinforcement learning to optimize estimation of multiple parameters in quantum sensors. The protocol achieved significantly better estimations compared to nonadaptive strategies, demonstrating enhanced performance in resource-limited regimes.
A new Swedish quantum computer is being made available to the industry, accompanied by a test bed and a quantum helpdesk. The test bed will allow companies and researchers to solve problems using quantum technology at a significantly lower cost than existing commercial options.
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.
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.
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 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 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.
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.
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.
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.
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 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.