A5071505678- Quantum Computing Algorithms and Architecture
- Parallel Computing and Optimization Techniques
- Quantum Information and Cryptography
- Cloud Computing and Resource Management
- Stochastic Gradient Optimization Techniques
- Quantum and electron transport phenomena
- Religion and Sociopolitical Dynamics in Nigeria
- Neural Networks and Reservoir Computing
- Force Microscopy Techniques and Applications
- Paranormal Experiences and Beliefs
- Machine Learning and Algorithms
- Artificial Intelligence in Games
Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently complex data patterns. Despite this promise, most contemporary methods require significant resources variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential advantage. To address this, we develop general-purpose, gradient-free, scalable reservoir algorithm...
Quantum computing has potential to provide exponential speedups over classical for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking simulation circuits on is therefore essential advance understanding how programs operate, enabling both algorithm discovery that leads high-impact computation engineering improvements deliver more powerful systems. Unfortunately, nature...
We describe Superstaq, a quantum software platform that optimizes the execution of programs by tailoring to underlying hardware primitives. For benchmarks such as Bernstein-Vazirani algorithm and Qubit Coupled Cluster chemistry method, we find deep optimization can improve program performance at least 10x compared prevailing state-of-the-art compilers. To highlight versatility our approach, present results from several platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti),...
Quantum computing has potential to provide exponential speedups over classical for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking simulation circuits on is therefore essential advance understanding how programs operate, enabling both algorithm discovery that leads high-impact computation engineering improvements deliver more powerful systems. Unfortunately, nature...
Realizing universal fault-tolerant quantum computation is a key goal in information science. By encoding into logical qubits utilizing error correcting codes, physical errors can be detected and corrected, enabling substantial reduction rates. However, the set of operations that easily implemented on such encoded often constrained, necessitating use special resource states known as 'magic states' to implement universal, classically hard circuits. A method prepare high-fidelity magic perform...
Recent results on the Quantum Approximate Optimization Algorithm (QAOA) have cast pessimism its potential to exhibit practical quantum speedups. For instance, QAOA’s locality limits performance tasks such as coloring bipartite graphs—which is easy for classical methods. Motivated by these limitations, Recursive QAOA was introduced overcome and symmetry of QAOA. Despite being more powerful than QAOA, RQAOA fully classically simulable at level-1 depth (p = 1). We report in this regime,...
We describe Superstaq, a quantum software platform that optimizes the execution of programs by tailoring to underlying hardware primitives. For benchmarks such as Bernstein-Vazirani algorithm and Qubit Coupled Cluster chemistry method, we find deep optimization can improve program performance at least 10x compared prevailing state-of-the-art compilers. To highlight versatility our approach, present results from several platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti),...