Quantum AlgorithmsView All Blogs

Explore the concept of Adiabatic Quantum Computing (AQC), its principles, applications, and challenges. Understand the adiabatic theorem, problem Hamiltonian, quantum annealing, and the potential of AQC in solving complex problems across various domains.

Exploring the software and tools ecosystem in quantum technology.

Unveiling services in the quantum domain.

This article explores the promising application of quantum computers in drug design, highlighting the potential for significant advancements in quantum chemical calculations critical for computational drug discovery.

ICOSA and NEC have developed Vector Annealing (VA), a quantum-inspired computing method that significantly accelerates solution finding in financial portfolio optimization, leveraging Markowitzs Modern Portfolio Theory for enhanced performance over traditional methods.

IBM researchers have demonstrated a quantum advantage in machine learning, revealing that quantum kernels can identify patterns in data sets that appear as random noise to classical computers, offering a new pathway for quantum machine learning.

A groundbreaking study by Los Alamos National Laboratory reveals that quantum entanglement significantly enhances the scalability of quantum machine learning, overcoming the previous assumption of needing exponentially large datasets for training quantum neural networks.

Whats really important is that if we keep making these devices bigger and bigger, we also need the performance to continue improving, in order for us to be able to realize some of these really complex algorithms, where each qubit is needed to be able to implement billions of gates, said Bluvstein. To be able to implement these billions of gates, we really need to start, now in the field, switching over to not only testing our algorithms with physical qubits and learning about how quantum algorithms work, but begin testing our algorithms with logical qubits. We need to determine how error-corrected algorithms work because we know that in order to, for example, solve really big problems, such as really big chemistry problems, we need things on the scale of more than a billion gates. And were never going to be able to achieve this with our physical qubits, so we need to focus on error-correcting algorithms

Quantum computing shows great potential for faster problem-solving, among other benefits. Discover key areas where the enterprise could implement the rapidly advancing tech.

Quantum computing, with its ability to run millions of calculations in a fraction of the time as traditional binary code, poses a real threat to todays most widely used encryption algorithms.

Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level.