Articles tagged with Quantum Algorithms
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A new project aims to develop robust logical quantum bits for scalable and fault-tolerant quantum computing. The snaQCs2025 project combines innovative simulation and integration methods to compensate for error susceptibility of physical qubits, bringing quantum computing closer to practical use.
Professor Keisuke Fujii, a researcher at The University of Osaka, has been selected as one of the Quantum 100 for his work on quantum computing. He was honored with this recognition in 2025, the centennial year of quantum mechanics.
Researchers at Cleveland Clinic and IBM developed a hybrid quantum-classical model to simulate molecular interactions. The study accurately simulated two supramolecular systems, water dimer and methane dimer, for the first time using quantum computers.
Kobe University's new web application combines quantum game theory with jazz improvisation to explore creativity. Users can interact in a 'quantum jam session', receiving real-time visual and auditory feedback on their strategies.
The EQUALITY project developed novel quantum approaches to representing and optimising quantum circuits with regard to hardware limitations. The consortium also achieved notable scientific advances aimed at the efficient utilisation of quantum resources.
Rice University computer scientists have developed algorithms that account for malicious noise in quantum states, which can collapse into a single random outcome. The new framework considers nonphysical and potentially malicious factors, delivering optimal results with sufficiently large copies of a quantum state.
Physicists have developed a breakthrough concept in quantum encryption that uses innovative protocols applied to tiny quantum dots to send encrypted information securely, even with imperfect light sources. The new approach outperforms current systems and has the potential to bring quantum-safe communication closer to everyday use.
A team of researchers developed a new quantum framework for analysing higher-order network data using topological signal processing. The Quantum Topological Signal Processing (QTSP) framework achieves linear scaling in signal dimension, opening the door to efficient quantum algorithms for problems previously considered out of reach.
A new quantum search algorithm has been developed to tackle continuous optimization and spectrum calculation problems, achieving a quadratic speedup. The algorithm's optimal query complexity has been established, making it suitable for real-world applications.
A team led by Kenneth Merz used IBM Quantum System One to run Sample-Based Quantum Diagonalization, a new method for simulating molecules in solvent. The approach achieved high chemical accuracy and demonstrated the ability to predict molecular energies and solvation free energy in aqueous solutions.
The EQUALITY project is developing advanced quantum computer algorithms for strategic industrial problems in areas like energy storage and aerodynamics. A webinar series will highlight these advancements, showcasing novel quantum approaches and their industrial applications.
Researchers at UC San Diego develop novel approach to extract essential information from quantum systems, outperforming traditional methods in accurately predicting diverse quantum state properties. Experimental validation demonstrates the effectiveness of this technique in characterizing quantum states despite realistic noise.
Researchers successfully simulated Google's 53-qubit Sycamore quantum circuit using sophisticated tensor network contraction techniques and advanced slicing methods. The approach reduced memory usage while maintaining computational effectiveness, enabling the simulation of complex quantum circuits with modest resources.
A novel approach combines large language models and quantum computing to predict Salmonella antimicrobial resistance. The SARPLLM algorithm outperforms other models in prediction accuracy.
Quantum Base, a Lancaster University spin-out, has successfully floated on the London Stock Exchange with a £4.8 million fundraising. The company aims to harness quantum technology to address real-world challenges through its patented Q-ID solution for anti-counterfeiting.
The Albert Einstein Jewish Brazilian Hospital launches a project to evaluate the application of quantum computing in developing new drugs and improving disease diagnosis. Researchers aim to use machine learning and quantum optimization algorithms to analyze rainfall data and predict heavy rainfall events.
Researchers developed a method to detect and protect quantum entanglement, a fundamental aspect of quantum computing. The variational entanglement witness (VEW) algorithm optimizes entanglement detection accuracy, differentiating between separable and entangled states.
Zuchongzhi-3 achieves quantum supremacy by outperforming classical supercomputers by 15 orders of magnitude, demonstrating the strongest quantum computational advantage in a superconducting system to date. The processor features 105 qubits and 182 couplers, with a coherence time of 72 μs and simultaneous gate fidelities exceeding 99%.
Researchers used Quantum Approximate Optimization Algorithm (QAOA) to cluster jets in high-energy particle collisions, achieving performance comparable to classical algorithms. The study demonstrates the potential of quantum computing in improving jet clustering for practical applications.
Researchers successfully linked two separate quantum processors to form a single, fully connected quantum computer using photonic network interface. This breakthrough enables computations to be distributed across the network, addressing quantum's scalability problem and paving the way for industry-disrupting quantum computers.
Researchers at the University of Innsbruck have developed a method to switch between two error correction codes in an error-tolerant manner, making it easier to implement all required gates for computing. This breakthrough enables the quantum computer to efficiently suppress errors and improve calculation accuracy.
A new multi-target quantum compilation algorithm developed by Tohoku University's Dr. Le Bin Ho improves the flexibility and performance of quantum computers. This allows for efficient handling of complex systems and tasks involving multiple variables in quantum machine learning.
Physicists at the University of the Witwatersrand developed an innovative computing system harnessing laser beams and display technology to process multiple possibilities simultaneously. This approach could speed up complex calculations in fields like logistics and finance, with potential applications in quantum optimisation and machin...
Researchers developed innovative encoding methods that simplified quantum circuits for data encoding, reducing circuit depth by a factor of 100 while maintaining accuracy. These methods showed improved resilience against adversarial attacks, paving the way for practical application of quantum machine learning on current devices.
Researchers at NCSA have presented a novel post-quantum cryptography network instrument to measure PQC adoption rates and ensure secure data safeguarding. The project's findings indicate that only OpenSSH and Google Chrome have successfully implemented PQC, achieving an initial adoption rate of 0.029%.
Researchers at the University of Copenhagen's Quantum for Life Centre have developed a new mathematical recipe to make quantum simulators more scalable and efficient. This breakthrough could speed up the development of new medicines from years to months by predicting how molecules behave in the human body before laboratory trials.
MIT researchers have proposed a best-of-both-worlds approach to improve the speed of a 1994 quantum factoring algorithm while reducing memory requirements. The new algorithm is faster, requires fewer qubits, and has a higher tolerance to quantum noise.
Researchers have developed a chip-based quantum system that can detect unauthorized access in quantum communication, using entangled four-photon states. This technology has the potential to strengthen data security and protect sensitive information from cyber threats.
Two major projects led by INRS professors will develop scalable solid-state semiconductors for on-chip quantum communication and advance smart programmable photonics. The $7.4 million funding will support collaborations between academia and industry partners.
Researchers developed a symbolic model checking approach to verify quantum circuits, addressing the gap between model-checking quantum programs and quantum circuits. They used Maude programming language to formally specify and verify quantum circuits, confirming their correctness and paving the way for error-free quantum computing.
Researchers developed a quantum annealing approach to accelerate data assimilation, significantly reducing computational cost. The method achieves comparable accuracy to conventional approaches but in a fraction of the time.
Researchers at Lancaster University and Radboud University Nijmegen have discovered a novel pathway to modulate and amplify spin waves at the nanoscale, paving the way for dissipation-free quantum information technologies. The study's findings could lead to the development of fast and energy-efficient computing devices.
Cleveland Clinic and IBM researchers develop a hybrid framework combining quantum and classical computing methods for protein structure prediction. This approach overcomes limitations of current classical methods and demonstrates improved accuracy in predicting protein structures.
Researchers at JPMorgan Chase, Argonne National Laboratory and Quantinuum show a quantum algorithmic speedup for the QAOA algorithm on the Low Autocorrelation Binary Sequences problem. The team demonstrates a significant step towards reaching quantum advantage, laying the foundation for future impact in production.
Free-space QSDC enables secure communication over long distances without fiber cables, ideal for the
Researchers developed a probabilistic approach to generate optimal sequences for execution on quantum computers, reducing search time by several orders of magnitude. The new method enables efficient searches within classical computational resources, contributing to the realization of the quantum Internet and improved performance.
Researchers have proposed an innovative quantum algorithm that effectively solves combinatorial optimization problems with constraints in a short time. The pVSQA algorithm uses a quantum device to generate a variational quantum state and transform infeasible solutions into feasible ones, achieving near-optimal performance.
A team of researchers has shown that quantum computers can solve a specific class of combinatorial optimisation problems much faster than classical computers. This is due to the ability of qubits to take on any value in between zero and one, allowing for exponential polynomial time complexities.
Nai-Hui Chia, an assistant professor of computer science at Rice University, has received a National Science Foundation CAREER Award to develop a new theoretical framework for efficient quantum algorithms. The grant aims to enhance the security of quantum cryptography and tackle complex problems in physics and machine learning.
Classical computers outperform quantum ones in speed and accuracy thanks to a new algorithm that efficiently simulates tensor networks. The breakthrough could revolutionize computations by leveraging classical computing's strengths.
Researchers used quantum support vector machines to classify flow separation and angle of attack with increased accuracy, solving complex problems faster and more accurately than classical methods.
Researchers explore quantum optical technology to solve scalability and accuracy issues in quantum computing, aiming to develop new drugs faster and more efficiently. Photon-based systems offer a solution by reducing physical components, increasing opportunities for scaling and stability.
A Harvard University team has created the world's first logical quantum processor, which can encode up to 48 logical qubits and execute hundreds of gate operations. This breakthrough is a significant step toward reliable quantum computing and fault-tolerant quantum computation.
Researchers found that tiny timing errors can significantly impact quantum algorithms, limiting the technology's potential. Despite promising applications in fields like pharmaceutical discovery and materials science, quantum computers' fragility hinders their scalability.
A new parallel hybrid quantum neural network demonstrates improved performance by combining the strengths of both quantum and classical layers. The model outperforms traditional machine learning methods in processing complicated patterns and relationships from data inputs.
Cleveland Clinic is selected by Wellcome Leap to lead two quantum computing research projects in collaboration with IBM Quantum and Algorithmiq. The projects aim to accelerate the development of quantum computing applications for healthcare, with a focus on protein structure prediction and photon-drug interactions in cancer treatment.
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.