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Efficient tensor network simulation of IBM's largest quantum processors
Siddhartha Patra, Saeed S. Jahromi, Sukhbinder Singh +1
We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 q…
Improving Gradient Methods via Coordinate Transformations: Applications to Quantum Machine Learning
Pablo Bermejo, Borja Aizpurua, Roman Orus
Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overa…
Financial Risk Management on a Neutral Atom Quantum Processor
Lucas Leclerc, Luis Ortiz-Guitierrez, Sebastian Grijalva +19
Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm sugges…
Variational Quantum Continuous Optimization: a Cornerstone of Quantum Mathematical Analysis
Pablo Bermejo, Roman Orus
Here we show how universal quantum computers based on the quantum circuit model can handle mathematical analysis calculations for functions with continuous domains, without any dig…
Variational Quantum Non-Orthogonal Optimization
Pablo Bermejo, Roman Orus
Current universal quantum computers have a limited number of noisy qubits. Because of this, it is difficult to use them to solve large-scale complex optimization problems. In this…
Financial Index Tracking via Quantum Computing with Cardinality Constraints
Samuel Palmer, Konstantinos Karagiannis, Adam Florence +4
In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackl…