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The rate at which quantum computing is hitting the media stream is ever-increasing. This piece is a collection of recent articles and reports covering various aspects of quantum computing from the lens of algorithms and software. Mea Cubitt

QPath® is finally available for developing truly real agnostic quantum systems | The present decade (2020s) is considered to be the “quantum decade”, in which “quantum computing is poised to expand the scope and complexity of the business problems we can solve” [1] offering a true “quantum advantage”. According to the Institute for Business Value we will witness “the most important computing revolution in 60 years” as a result of the integration of classical computing, quantum computing and artificial intelligence.  Source: QuantumPath.   QPath® is finally available for developing truly real agnostic quantum systems…

Exploring Quantum Technology: Qiskit and RasQberry | Proponents of quantum technology believe its will change the world. Others remain skeptical, as they do of technologies like fusion energy. Speaking at a quantum developers’ forum, IBM Distinguished Engineer Jan-Rainer Lahmann retraced the history of quantum computing, reviewing IBM’s hardware and development roadmaps and describing the ingredients of “Raspberry Pi quantum”.   Source: EETimes.   Exploring Quantum Technology: Qiskit and RasQberry…

Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints | Lasso and Ridge are important minimization problems in machine learning and statistics. They are versions of linear regression with squared loss where the vector θ∈Rd of coefficients is constrained in either ℓ1-norm (for Lasso) or in ℓ2-norm (for Ridge). We study the complexity of quantum algorithms for finding ε-minimizers for these minimization problems. We show that for Lasso we can get a quadratic quantum speedup in terms of d by speeding up the cost-per-iteration of the Frank-Wolfe algorithm, while for Ridge the best quantum algorithms are linear in d, as are the best classical algorithms.  Source: arXiv.   Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints…

Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment | In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. Potential quantum advantage would require problem instances at this scale, with several hundred units.  Source: arXiv.   Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment…