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Masoud Mohseni

Publications and source records attributed to Masoud Mohseni.

At least 73 records · Page 4Linked to original sources

Minimising the heat dissipation of quantum information erasure

Quantum state engineering and quantum computation rely on information erasure procedures that, up to some fidelity, prepare a quantum object in a pure state. Such processes occur within Landauer's framework if they rely on an interaction between the object and a thermal reservoir. Landauer's principle dictates that this must dissipate a minimum quantity of heat, proportional to the entropy reduction that is incurred by the object, to the thermal reservoir. However, this lower bound is only reachable for some specific physical situations, and it is not necessarily achievable for any given reservoir. The main task of our work can be stated as the minimisation of heat dissipation given probabilistic information erasure, i.e., minimising the amount of energy transferred to the thermal reservoir as heat if we require that the probability of preparing the object in a specific pure state $|φ_1\rangle$ be no smaller than $p_{φ_1}^{\max}-δ$. Here $p_{φ_1}^{\max}$ is the maximum probability of information erasure that is permissible by the physical context, and $δ\geqslant 0$ the error. To determine the achievable minimal heat dissipation of quantum information erasure within a given physical context, we explicitly optimise over all possible unitary operators that act on the composite system of object and reservoir. Specifically, we characterise the equivalence class of such optimal unitary operators, using tools from majorisation theory, when we are restricted to finite-dimensional Hilbert spaces. Furthermore, we discuss how pure state preparation processes could be achieved with a smaller heat cost than Landauer's limit, by operating outside of Landauer's framework.

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Systematic Dimensionality Reduction for Quantum Walks: Optimal Spatial Search and Transport on Non-Regular Graphs

Continuous time quantum walks provide an important framework for designing new algorithms and modelling quantum transport and state transfer problems. Often, the graph representing the structure of a problem contains certain symmetries that confine the dynamics to a smaller subspace of the full Hilbert space. In this work, we use invariant subspace methods, that can be computed systematically using Lanczos algorithm, to obtain the reduced set of states that encompass the dynamics of the problem at hand without the specific knowledge of underlying symmetries. First, we apply this method to obtain new instances of graphs where the spatial quantum search algorithm is optimal: complete graphs with broken links and complete bipartite graphs, in particular, the star graph. These examples show that regularity and high-connectivity are not needed to achieve optimal spatial search. We also show that this method considerably simplifies the calculation of quantum transport efficiencies. Furthermore, we observe improved efficiencies by removing a few links from highly symmetric graphs. Finally, we show that this reduction method also allows us to obtain an upper bound for the fidelity of a single qubit transfer on an XY spin network.

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Disorder-assisted quantum transport in suboptimal decoherence regimes

We investigate quantum transport in binary tree structures and in hypercubes for the disordered Frenkel-exciton Hamiltonian under pure dephasing noise. We compute the energy transport efficiency as a function of disorder and dephasing rates. We demonstrate that dephasing improves transport efficiency not only in the disordered case, but also in the ordered one. The maximal transport efficiency is obtained when the dephasing timescale matches the hopping timescale, which represent new examples of the Goldilocks principle at the quantum scale. Remarkably, we find that in weak dephasing regimes, away from optimal levels of environmental fluctuations, the average effect of increasing disorder is to improve the transport efficiency until an optimal value for disorder is reached. Our results suggest that rational design of the site energies statistical distributions could lead to better performances in transport systems at nanoscale when their natural environments are far from the optimal dephasing regime.

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Computational Role of Multiqubit Tunneling in a Quantum Annealer

Quantum tunneling, a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself, has been hypothesized as an advantageous physical resource for optimization. Here we show that multiqubit tunneling plays a computational role in a currently available, albeit noisy, programmable quantum annealer. We develop a non-perturbative theory of open quantum dynamics under realistic noise characteristics predicting the rate of many-body dissipative quantum tunneling. We devise a computational primitive with 16 qubits where quantum evolutions enable tunneling to the global minimum while the corresponding classical paths are trapped in a false minimum. Furthermore, we experimentally demonstrate that quantum tunneling can outperform thermal hopping along classical paths for problems with up to 200 qubits containing the computational primitive. Our results indicate that many-body quantum phenomena could be used for finding better solutions to hard optimization problems.

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Computational Role of Collective Tunneling in a Quantum Annealer

Quantum tunneling is a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself. Tunneling has been hypothesized as an advantageous physical resource for optimization. Here we present the first experimental evidence of a computational role of multiqubit quantum tunneling in the evolution of a programmable quantum annealer. We develop a theoretical model based on a NIBA Quantum Master Equation to describe the multiqubit dissipative tunneling effects under the complex noise characteristics of such quantum devices. We start by considering a computational primitive, an optimization problem consisting of just one global and one false minimum. The quantum evolutions enable tunneling to the global minimum while the corresponding classical paths are trapped in a false minimum. In our study the non-convex potentials are realized by frustrated networks of qubit clusters with strong intra-cluster coupling. We show that the collective effect of the quantum environment is suppressed in the "critical" phase during the evolution where quantum tunneling "decides" the right path to solution. In a later stage dissipation facilitates the multiqubit tunneling leading to the solution state. The predictions of the model accurately describe the experimental data from the D-Wave Two quantum annealer at NASA Ames. In our computational primitive the temperature dependence of the probability of success in the quantum model is opposite to that of the classical paths with thermal hopping. Specifically, we provide an analysis of an optimization problem with sixteen qubits, demonstrating eight qubit tunneling that increases success probabilities. Furthermore, we report results for larger problems with up to 200 qubits that contain the primitive as subproblems.

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Quantum brachistochrone curves as geodesics: obtaining accurate control protocols for time-optimal quantum gates

Most methods of optimal control cannot obtain accurate time-optimal protocols. The quantum brachistochrone equation is an exception, and has the potential to provide accurate time-optimal protocols for essentially any quantum control problem. So far this potential has not been realized, however, due to the inadequacy of conventional numerical methods to solve it. Here, using differential geometry, we reformulate the quantum brachistochrone curves as geodesics on the unitary group. With this identification we are able to obtain a numerical method that efficiently solves the brachistochrone problem. We apply it to two examples demonstrating its power.

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Quantum support vector machine for big data classification

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show 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. In cases when classical sampling algorithms require polynomial time, an exponential speed-up is obtained. At the core of this quantum big data algorithm is a non-sparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

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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 time polynomial in the number of vectors and the dimension of the space. Quantum computers are good at manipulating high-dimensional vectors in large tensor product spaces. This paper provides supervised and unsupervised quantum machine learning algorithms for cluster assignment and cluster finding. Quantum machine learning can take time logarithmic in both the number of vectors and their dimension, an exponential speed-up over classical algorithms.

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Quantum principal component analysis

The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyze the measurement results statistically. Here we show that the unknown quantum state can play an active role in its own analysis. In particular, given multiple copies of a quantum system with density matrix ρ, then it is possible to perform the unitary transformation e^{-iρt}. As a result, one can create quantum coherence among different copies of the system to perform quantum principal component analysis, revealing the eigenvectors corresponding to the large eigenvalues of the unknown state in time exponentially faster than any existing algorithm.

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Energy-scales convergence for optimal and robust quantum transport in photosynthetic complexes

Underlying physical principles for the high efficiency of excitation energy transfer in light-harvesting complexes are not fully understood. Notably, the degree of robustness of these systems for transporting energy is not known considering their realistic interactions with vibrational and radiative environments within the surrounding solvent and scaffold proteins. In this work, we employ an efficient technique to estimate energy transfer efficiency of such complex excitonic systems. We observe that the dynamics of the Fenna-Matthews-Olson (FMO) complex leads to optimal and robust energy transport due to a convergence of energy scales among all important internal and external parameters. In particular, we show that the FMO energy transfer efficiency is optimum and stable with respect to the relevant parameters of environmental interactions and Frenkel-exciton Hamiltonian including reorganization energy $λ$, bath frequency cutoff $γ$, temperature $T$, bath spatial correlations, initial excitations, dissipation rate, trapping rate, disorders, and dipole moments orientations. We identify the ratio of $λT/γ\*g$ as a single key parameter governing quantum transport efficiency, where g is the average excitonic energy gap.

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Experimental characterization of quantum dynamics through many-body interactions

We report on the implementation of a quantum process tomography (QPT) technique known as direct characterization of quantum dynamics (DCQD) applied on coherent and incoherent single- qubit processes in a system of trapped calcium 40 ions. Using quantum correlations with an ancilla qubit, DCQD reduces exponentially the number of experimental configurations required for standard QPT. With this technique, the system's relaxation times T1 and T2 were measured with a single experimental configuration. We further show the first complete characterization of single-qubit processes using a single generalized measurement realized through multi-body correlations with three ancilla qubits.

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Hyperentanglement-enabled Direct Characterization of Quantum Dynamics

We use hyperentangled photons to experimentally implement an entanglement-assisted quantum process tomography technique known as Direct Characterization of Quantum Dynamics. Specifically, hyperentanglement-assisted Bell-state analysis enabled us to characterize a variety of single-qubit quantum processes using far fewer experimental configurations than are required by Standard Quantum Process Tomography (SQPT). Furthermore, we demonstrate how known errors in Bell-state measurement may be compensated for in the data analysis. Using these techniques, we have obtained single-qubit process fidelities as high as 98.2% but with one-third the number experimental configurations required for SQPT. Extensions of these techniques to multi-qubit quantum processes are discussed.

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Efficient estimation of energy transfer efficiency in light-harvesting complexes

The fundamental physical mechanisms of energy transfer in photosynthetic complexes is not yet fully understood. In particular, the degree of efficiency or sensitivity of these systems for energy transfer is not known given their non-perturbative and non-Markovian interactions with proteins backbone and surrounding photonic and phononic environments. One major problem in studying light-harvesting complexes has been the lack of an efficient method for simulation of their dynamics in biological environments. To this end, here we revisit the second-order time-convolution (TC2) master equation and examine its reliability beyond extreme Markovian and perturbative limits. In particular, we present a derivation of TC2 without making the usual weak system-bath coupling assumption. Using this equation, we explore the long time behaviour of exciton dynamics of Fenna-Matthews-Olson (FMO) protein complex. Moreover, we introduce a constructive error analysis to estimate the accuracy of TC2 equation in calculating energy transfer efficiency, exhibiting reliable performance for environments with weak and intermediate memory and strength. Furthermore, we numerically show that energy transfer efficiency is optimal and robust for the FMO protein complex of green sulphur bacteria with respect to variations in reorganization energy and bath correlation time-scales.

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The quantum Goldilocks effect: on the convergence of timescales in quantum transport

Excitonic transport in photosynthesis exhibits a wide range of time scales. Absorption and initial relaxation takes place over tens of femtoseconds. Excitonic lifetimes are on the order of a nanosecond. Hopping rates, energy differences between chromophores, reorganization energies, and decoherence rates correspond to time scales on the order of picoseconds. The functional nature of the divergence of time scales is easily understood: strong coupling to the electromagnetic field over a broad band of frequencies yields rapid absorption, while long excitonic lifetimes increase the amount of energy that makes its way to the reaction center to be converted to chemical energy. The convergence of the remaining time scales to the centerpoint of the overall temporal range is harder to understand. In this paper we argue that the convergence of timescales in photosynthesis can be understood as an example of the `quantum Goldilocks effect': natural selection tends to drive quantum systems to the degree of quantum coherence that is `just right' for attaining maximum efficiency. We provide a general theory of optimal and robust, efficient transport in quantum systems, and show that it is governed by a single parameter.

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Quantum State and Process Tomography of Energy Transfer Systems via Ultrafast Spectroscopy

The description of excited state dynamics in multichromophoric systems constitutes both a theoretical and experimental challenge in modern physical chemistry. An experimental protocol which can systematically characterize both coherent and dissipative processes at the level of the evolving quantum state of the chromophores is desired. In this article, we show that a carefully chosen set of polarization controlled two-color heterodyned photon-echo experiments can be used to reconstruct the time-evolving density matrix of the one-exciton manifold of a heterodimer. This possibility in turn allows for a complete description of the excited state dynamics via quantum process tomography (QPT). Calculations on the dimer show that QPT can reveal rich information about system-bath interactions, which otherwise appear nontrivially hidden in the polarization monitored in standard four-wave mixing experiments. Our study presents a novel method for analyzing condensed phase experiments with a quantum information processing perspective.

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Excitonic diffusion length in complex quantum systems: The effects of disorder and environmental fluctuations on symmetry-enhanced supertransfer

Symmetric couplings among aggregates of $n$ chromophores increase the transfer rate of excitons by a factor $n^2$, a quantum mechanical phenomenon called "supertransfer." In this work we demonstrate how supertransfer effects induced by geometrical symmetries can enhance the exciton diffusion length by a factor $n$ along cylindrically symmetric structures, consisting of arrays of rings of chromophores, and along spiral arrays. We analyse both closed system dynamics and open quantum dynamics, modelled by combining a random bosonic bath with static disorder. In the closed system case, we use the symmetries of the system within a short-time approximation to obtain a closed analytical expression for the diffusion length that explicitly reveals the supertransfer contribution. When subject to disorder, we show that supertransfer can enhance excitonic diffusion lengths for small disorders and characterize the crossover from coherent to incoherent motion. Owing to the quasi-1D nature of the model, disorder ultimately localizes the excitons, diminishing but not destroying the effects of supertransfer. When dephasing effects are included, we study the scaling of diffusion with both time and number of chromophores and observe that the transition from a coherent, ballistic regime to an incoherent, random-walk regime occurs at the same point as the change from supertransfer to classical scaling.

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Symmetry-enhanced supertransfer of delocalized quantum states

Coherent hopping of excitation rely on quantum coherence over physically extended states. In this work, we consider simple models to examine the effect of symmetries of delocalized multi-excitation states on the dynamical timescales, including hopping rates, radiative decay, and environmental interactions. While the decoherence (pure dephasing) rate of an extended state over N sites is comparable to that of a non-extended state, superradiance leads to a factor of N enhancement in decay and absorption rates. In addition to superradiance, we illustrate how the multi-excitonic states exhibit `supertransfer' in the far-field regime: hopping from a symmetrized state over N sites to a symmetrized state over M sites at a rate proportional to MN. We argue that such symmetries could play an operational role in physical systems based on the competition between symmetry-enhanced interactions and localized inhomogeneities and environmental interactions that destroy symmetry. As an example, we propose that supertransfer and coherent hopping play a role in recent observations of anomolously long diffusion lengths in nano-engineered assembly of light-harvesting complexes.

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Towards Quantum Chemistry on a Quantum Computer

The fundamental problem faced in quantum chemistry is the calculation of molecular properties, which are of practical importance in fields ranging from materials science to biochemistry. Within chemical precision, the total energy of a molecule as well as most other properties, can be calculated by solving the Schrodinger equation. However, the computational resources required to obtain exact solutions on a conventional computer generally increase exponentially with the number of atoms involved. This renders such calculations intractable for all but the smallest of systems. Recently, an efficient algorithm has been proposed enabling a quantum computer to overcome this problem by achieving only a polynomial resource scaling with system size. Such a tool would therefore provide an extremely powerful tool for new science and technology. Here we present a photonic implementation for the smallest problem: obtaining the energies of H2, the hydrogen molecule in a minimal basis. We perform a key algorithmic step - the iterative phase estimation algorithm - in full, achieving a high level of precision and robustness to error. We implement other algorithmic steps with assistance from a classical computer and explain how this non-scalable approach could be avoided. Finally, we provide new theoretical results which lay the foundations for the next generation of simulation experiments using quantum computers. We have made early experimental progress towards the long-term goal of exploiting quantum information to speed up quantum chemistry calculations.

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