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Quantum algorithms for supervised and unsupervised machine learning
Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take
Environment-assisted quantum transport
Transport phenomena at the nanoscale are of interest due to the presence of both quantum and classical behavior. In this work, we demonstrate that quantum transport efficiency can be enhanced by a
Quantum support vector machine for big feature and big data classification
This work shows that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples, and an exponential speedup is obtained.
Quantum principal component analysis
Characterizing an unknown quantum state typically relies on analysing the outcome of a large set of measurements. Certain quantum-processing tasks are now shown to be realizable using only
Quantum machine learning
The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
Environment-assisted quantum walks in photosynthetic energy transfer.
A theoretical framework for studying the role of quantum interference effects in energy transfer dynamics of molecular arrays interacting with a thermal bath within the Lindblad formalism is developed and an effective interplay between the free Hamiltonian evolution and the thermal fluctuations in the environment is demonstrated.
Simple pulses for elimination of leakage in weakly nonlinear qubits.
This work provides an easy to implement analytic formula that inhibits leakage from any single-control analog or pixelated pulse, based on adding a second control that is proportional to the time derivative of the first.
Quantum singular-value decomposition of nonsparse low-rank matrices
A method to exponentiate nonsparse indefinite low-rank matrices on a quantum computer that preserves the phase relations between the singular spaces allowing for efficient algorithms that require operating on the entire singular-value decomposition of a matrix.
Quantum computational finance: quantum algorithm for portfolio optimization
A quantum algorithm for portfolio optimization is presented, given quantum access to the historical record of returns, that determines the optimal risk-return tradeoff curve and allows one to sample from the optimal portfolio.
Role of quantum coherence and environmental fluctuations in chromophoric energy transport.
This work quantifies the biological importance of fundamental physical processes, such as the excitonic Hamiltonian evolution and phonon-induced decoherence, by their contribution to the efficiency of the primary photosynthetic event.