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Lower Bounding the Secret Key Capacity of Bosonic Gaussian Channels via Optimal Gaussian Measurements
Giuseppe Ortolano, Stefano Pirandola, Leonardo Banchi
We find the maximum rate achievable in the private communication over a bosonic quantum channel with a fully Gaussian protocol based on optimal single-mode Gaussian measurements. T…
Unsupervised Learning to Recognize Quantum Phases of Matter
Mehran Khosrojerdi, Alessandro Cuccoli, Paola Verrucchi +1
Drawing the quantum phase diagram of a many-body system in the parameter space of its Hamiltonian can be seen as a learning problem, which implies labelling the corresponding groun…
Efficient tensor-network simulations of weakly-measured quantum circuits
Darren Pereira, Leonardo Banchi
We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the…
Overshifted Parameter-Shift Rules: Optimizing Complex Quantum Systems with Few Measurements
Leonardo Banchi, Dominic Branford, Chetan Waghela
Gradient-based optimization is a key ingredient of variational quantum algorithms, with applications ranging from quantum machine learning to quantum chemistry and simulation. The…
Analysis of Quantum Image Representations for Supervised Classification
Marco Parigi, Mehran Khosrojerdi, Filippo Caruso +1
In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Solving MNIST with a globally trained Mixture of Quantum Experts
Paolo Alessandro Xavier Tognini, Leonardo Banchi, Giacomo De Palma
We propose a new quantum neural network for image classification, which is able to classify the parity of the MNIST dataset with full resolution with a test accuracy of up to 97.5%…
Chernoff Information Bottleneck for Covert Quantum Target Sensing
Giuseppe Ortolano, Ivano Ruo-Berchera, Leonardo Banchi
The paradigm of quantum metrology and sensing aims to identify a quantum advantage in precision at a fixed energy of the probe state. However, in practice, employing high-energy cl…
Out-of-distribution generalisation for learning quantum channels with low-energy coherent states
Jason L. Pereira, Quntao Zhuang, Leonardo Banchi
When experimentally learning the action of a continuous variable quantum process by probing it with inputs, there will often be some restriction on the input states used. One exper…
Myths around quantum computation before full fault tolerance: What no-go theorems rule out and what they don't
Zoltán Zimborás, Bálint Koczor, Zoë Holmes +19
In this perspective article, we revisit and critically evaluate prevailing viewpoints on the capabilities and limitations of near-term quantum computing and its potential transitio…