Researchers have built a general mathematical model that describes how to implement an arbitrary N-qubit quantum gate optimally in any closed N-qubit system that satisfies controllability conditions. The work, published in Quantum Information Processing, addresses one of the central practical…
To accommodate the limitations of near-term quantum hardware, the researchers decomposed the scheduling problem into daily 30-qubit batches, and found that an entanglement-free approach produced feasible schedules at a fraction of the computational cost of entangled topologies. The results indicate…
Dr. Donovan is a futurist and technology writer covering the quantum revolution. Where classical computers manipulate bits that are either on or off, quantum machines exploit superposition and entanglement to process information in ways that classical physics cannot. Dr. Donovan…
Project Lead: Quantum GenomicsSector: HealthcareProject SummaryThe QResolve project addresses a major bottleneck in genomic sequencing: resolving complex Funded through UK Research and Innovation (UKRI), and sponsored by the Engineering and Physical Sciences Research Council (EPSRC) and the Science and Technology…
Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to…
Bioengineer.org Quantum Neural Networks Learn the Math Behind Optimal Portfolios Subject of Research: Quantum physics-informed neural networks for solving PDEs in portfolio optimization News Source: Katie Riggs. (November 5, 2026). Quantum Neural Networks Learn the Math Behind Optimal Portfolios. Scienmag.
By Ewan Scott | Nov 5, 2026 | Economy & Business, Featured, News & Analysis A single performance benchmark tells the story: D-Wave Quantum Inc.’s (NASDAQ: QBTS) upgraded Stride hybrid solver now delivers superior solutions in 15 seconds that its predecessor required 120 seconds to produce. That…
In real-coded genetic algorithms (RCGAs), search performance often deteriorates when the true global optimum lies outside the initial search range because crossover-based sampling remains concentrated around the initial population. We define this problem as outside-range optimization and introduce the outside-range…
Predicting hydration sites inside protein binding pockets is computationally demanding but essential for understanding how drugs interact with their targets and for improving drug discovery pipelines. projected practical utility with improved accuracy as quantum hardware scales to 900 qubits and beyond.
Q-CTRL partnered with JIJ Inc. to develop a hybrid quantum-classical workflow optimizing power grid configurations on real hardware for networks to 69 buses As renewable energy and electric vehicle adoption increase, power grids become more complex to manage. Traditional optimization methods…
Numerics, convexity, OR, and algorithms powering real‑world systems.
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