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Researcher profile

Enrique Solano

Enrique Solano contributes to research discovery and scholarly infrastructure.

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quant-phastro-ph.IMastro-ph.SRcond-mat.mes-hallastro-ph.EPEmerging Technologiesastro-ph.GAastro-ph.COastro-ph.HEcond-mat.quant-gasCryptography and Securitygr-qcMachine LearningNeural and Evolutionary ComputingNeurons and Cognitionphysics.pop-ph

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Published work

28 published item(s)

preprint2026arXiv

Constant Depth Digital-Analog Counterdiabatic Quantum Computing

We introduce a digital-analog quantum computing framework that enables counterdiabatic protocols to be implemented at constant circuit depth, allowing fast and resource-efficient quantum state preparation on current quantum hardware. Counterdiabatic protocols suppress diabatic excitations in finite-time adiabatic evolution, but their practical application is limited by the non-local structure of the required Hamiltonians and the resource overhead of fully digital implementations. Counterdiabatic terms can be expressed as truncated expansions of nested commutators of the adiabatic Hamiltonian and its parametric derivative. Here, we show how this algebraic structure can be efficiently realized in a digital-analog setting using commutator product formulas. Using native multi-qubit analog interactions augmented by local single-qubit rotations, this approach enables higher-order counterdiabatic protocols whose implementation requires a constant number of analog blocks for any fixed truncation order, independent of system size. We demonstrate the method for two-dimensional spin models and analyze the associated approximation errors. These results show that digital-analog quantum computing enables a qualitatively new resource scaling for counterdiabatic protocols and related quantum control primitives, with direct implications for quantum simulation, optimization, and algorithmic state preparation on current quantum devices.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware

We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies beyond standard quadratic encodings. In contrast to QUBO-based approaches, the proposed model includes one-, two-, and three-body interaction terms derived from mutual-information measures, enabling the objective function to capture feature relevance, pairwise redundancy, and higher-order statistical structure within a unified energy model. To suppress trivial all-selected solutions, we further include structured linear penalties that promote sparsity while preserving informative variables. The resulting HUBO instances are optimized with digitized counterdiabatic quantum optimization on IonQ Forte and compared against noiseless quantum simulation as well as two classical dimensionality-reduction baselines: SelectKBest based on mutual information and principal component analysis (PCA). We evaluate the proposed workflow on two benchmark classification datasets, namely the Gallstone dataset and the Spambase dataset, and analyze both predictive performance and selected-subset structure. The results show good qualitative agreement between hardware executions and noiseless simulations, supporting the feasibility of implementing higher-order feature-selection Hamiltonians on current trapped-ion processors. In addition, the quantum approach yields competitive classification performance while producing compact and informative feature subsets, highlighting the potential of higher-order quantum optimization for machine-learning preprocessing tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Random access codes via quantum contextual redundancy

We propose a protocol to encode classical bits in the measurement statistics of many-body Pauli observables, leveraging quantum correlations for a random access code. Measurement contexts built with these observables yield outcomes with intrinsic redundancy, something we exploit by encoding the data into a set of convenient context eigenstates. This allows to randomly access the encoded data with few resources. The eigenstates used are highly entangled and can be generated by a discretely-parametrized quantum circuit of low depth. Applications of this protocol include algorithms requiring large-data storage with only partial retrieval, as is the case of decision trees. Using $n$-qubit states, this Quantum Random Access Code has greater success probability than its classical counterpart for $n\ge 14$ and than previous Quantum Random Access Codes for $n \ge 16$. Furthermore, for $n\ge 18$, it can be amplified into a nearly-lossless compression protocol with success probability $0.999$ and compression ratio $O(n^2/2^n)$. The data it can store is equal to Google-Drive server capacity for $n= 44$, and to a brute-force solution for chess (what to do on any board configuration) for $n= 100$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A glint in the eye: photographic plate archive searches for non-terrestrial artefacts

In this paper, we present a simple strategy to identify Non-Terrestrial artefacts \citep[NTAs;][]{Kopparapu} in or near geosynchronous Earth orbits (GEOs). We show that even the small pieces of reflective debris in orbit around the Earth can be identified through searches for multiple transients in old photographic plate material exposed before the launch of first human satellite in 1957. In order to separate between possible false point-like sources on photographic plates from real reflections, we present calculations to quantify the associated probabilities of alignments. We show that in an image with nine "simultaneous transients" at least four or five point sources along a line within a $10 \ast 10$ arcmin$^{2}$ image box are a strong indicator of NTAs, corresponding to significance levels of $2.5$ to $3.9 σ$. This given methodology can then be applied to set an upper limit to the prevalence of NTAs with reflective surfaces in geosynchronous orbits.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adaptive Random Quantum Eigensolver

We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general method to parametrize and optimize the probability density function of a random number generator, which is the core of stochastic algorithms. We follow a bioinspired evolutionary mutation method to introduce changes in the involved matrices. Our optimization is based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum computers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An approach to interfacing the brain with quantum computers: practical steps and caveats

We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed a method to encode neural correlates of mental activity as instructions for a quantum computer. Brain signals are detected utilizing electrodes placed on the scalp of a person, who learns how to produce the required mental activity to issue instructions to rotate and measure a qubit. Currently, our proof-of-concept runs on a software simulation of a quantum computer. At the time of writing, available quantum computing hardware and brain activity sensing technology are not sufficiently developed for real-time control of quantum states with the brain. But we are one step closer to interfacing the brain with real quantum machines, as improvements in hardware technology at both fronts become available in time to come. The paper ends with a discussion on some of the challenging problems that need to be addressed before we can interface the brain with quantum hardware.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Analogue Penrose process in rotating acoustic black Hole

Analogue gravity stands today as an important tool for the investigation of gravitational phenomena that would be otherwise out of reach considering our technology. We consider here the analogue Penrose process in a rotating acoustic black hole based on a quantum fluid described by the draining bathtub model. Because of the rotating nature of this acoustic spacetime, a particle, that is a phonon travelling on the quantum fluid, will experience the well known frame dragging effect. By defining the effective Komar mass and angular momentum of this acoustic black hole, we found that energy can be extracted from it. At the same time, we show that the black hole angular momentum and mass are reduced, in complete analogy with the Penrose effect.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Detached eclipsing binaries from the Kepler field: radii and photometric masses of components in short-period systems

The characterisation of detached eclipsing binaries with low mass components has become important when verifying the role of convection in stellar evolutionary models, which requires model-independent measurements of stellar parameters with great precision. However, spectroscopic characterisation depends on single-target radial velocity observations and only a few tens of well-studied low-mass systems have been diagnosed in this way. We characterise eclipsing detached systems from the {\it Kepler} field with low mass components by adopting a purely-photometric method. Based on an extensive multi-colour dataset, we derive effective temperatures and photometric masses of individual components using clustering techniques. We also estimate the stellar radii from additional modelling of the available {\it Kepler} light curves. Our measurements confirm the presence of an inflation trend in the mass-radius diagram against theoretical stellar models in the low-mass regime.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Digitized-Counterdiabatic Quantum Optimization

We propose digitized-counterdiabatic quantum optimization (DCQO) to achieve polynomial enhancement over adiabatic quantum optimization for the general Ising spin-glass model, which includes the whole class of combinatorial optimization problems. This is accomplished via the digitization of adiabatic quantum algorithms that are catalysed by the addition of non-stoquastic counterdiabatic terms. The latter are suitably chosen, not only for escaping classical simulability, but also for speeding up the performance. Finding the ground state of a general Ising spin-glass Hamiltonian is used to illustrate that the inclusion of k-local non-stoquastic counterdiabatic terms can always outperform the traditional adiabatic quantum optimization with stoquastic Hamiltonians. In particular, we show that a polynomial enhancement in the ground-state success probability can be achieved for a finite-time evolution, even with the simplest 2-local counterdiabatic terms. Furthermore, the considered digitization process, within the gate-based quantum computing paradigm, provides the flexibility to introduce arbitrary non-stoquastic interactions. Along these lines, using our proposed paradigm on current NISQ computers, quantum speed-up may be reached to find approximate solutions for NP-complete and NP-hard optimization problems. We expect DCQO to become a fast-lane paradigm towards quantum advantage in the NISQ era.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

European Virtual Observatory Schools

The European Virtual Observatory (VO) initiative organises regular VO schools since 2008. The goals are twofold: i) to expose early-career European astronomers to the variety of currently available VO tools and services so that they can use them efficiently for their own research and; ii) to gather their feedback on the VO tools and services and the school itself. During the schools, VO experts guide participants on the usage of the tools through a series of predefined real science cases, an activity that took most of the allocated time. Participants also have the opportunity to develop their own science cases under the guidance of VO tutors. These schools have demonstrated to be very useful for students, since they declare to regularly use the VO tools in their research afterwards, and for us, since we have first hand information about the user needs. Here, we introduce our VO schools, the approach we follow, and present the training materials that we have developed along the years.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Gaia0007-1605: an old triple system with an inner brown dwarf-white dwarf binary and an outer white dwarf companion

We identify Gaia0007-1605AC as the first inner brown dwarf-white dwarf binary of a hierarchical triple system in which the outer component is another white dwarf (Gaia0007-1605B). From optical/near-infrared spectroscopy obtained at the Very Large Telescope with the X-Shooter instrument and/or from Gaia photometry plus SED fitting, we determine the effective temperatures and masses of the two white dwarfs (12018+-68 K, 0.54+-0.01 Msun for Gaia0007-1605A and 4445+-116 K, 0.56+-0.05 Msun for Gaia0007-1605B) and the effective temperature of the brown dwarf (1850+-50 K; corresponding to a spectral type L3+-1). By analysing the available TESS light curves of Gaia0007-1605AC we detect a signal at 1.0446+-0.0015 days with an amplitude of 6.25 ppt, which we interpret as the orbital period modulated from irradiation effects of the white dwarf on the brown dwarf's surface. This drives us to speculate that the inner binary evolved through a common envelope phase in the past. Using the outer white dwarf as a cosmochronometer and analysing the kinematic properties of the system, we conclude that the triple system is about 10 Gyr old.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Is there a background population of high-albedo objects in geosynchronous orbits around Earth?

Old, digitized astronomical images taken before the human spacefaring age offer a unique view of the sky devoid of known artificial satellites. In this paper, we have carried out the first optical searches ever for non-terrestrial artifacts near the Earth following the method proposed in Villarroel et al. (2022). We use images contained in the First Palomar Sky Survey to search for simultaneous (during a plate exposure time) transients that in addition to being point-like, are aligned. We provide a shortlist of the most promising candidates of aligned transients, that must be examined with the help of a microscope to separate celestial sources from plate defects with coincidentally star-like brightness profiles. We further explore one possible, but not unique, interpretation in terms of fast reflections off high-albedo objects in geosynchronous orbits around Earth. If a future study rules out each multiple transient candidate, the estimated surface density becomes an upper limit of $<10^{-9}$ objects km$^{-2}$ non-terrestrial artifacts in geosynchronous orbits around Earth. Finally, we conclude that observations and analysis of multiple, simultaneously appearing and vanishing light sources on the sky merit serious further attention, regardless of their origin.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Meta-Learning Digitized-Counterdiabatic Quantum Optimization

Solving optimization tasks using variational quantum algorithms has emerged as a crucial application of the current noisy intermediate-scale quantum devices. However, these algorithms face several difficulties like finding suitable ansatz and appropriate initial parameters, among others. In this work, we tackle the problem of finding suitable initial parameters for variational optimization by employing a meta-learning technique using recurrent neural networks. We investigate this technique with the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA) that utilizes counterdiabatic protocols to improve the state-of-the-art QAOA. The combination of meta learning and DC-QAOA enables us to find optimal initial parameters for different models, such as MaxCut problem and the Sherrington-Kirkpatrick model. Decreasing the number of iterations of optimization as well as enhancing the performance, our protocol designs short depth circuit ansatz with optimal initial parameters by incorporating shortcuts-to-adiabaticity principles into machine learning methods for the near-term devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

VVVX near-IR photometry for 99 low-mass stars in the Gaia EDR3 Catalog of Nearby Stars

Red dwarf stars, which represent 75% of stars in the Milky Way, can be studied in great detail in the solar neighborhood where the sample is more complete. We intend to better characterize red dwarf candidates selected from the Gaia Catalog of Nearby Stars using optical and near-infrared photometry from the VVVX Survey, DECaPS, Pan-STARRS, and WISE. We performed a cross-matching procedure among the positions of a color-selected sample of M dwarfs in the VVVX Survey and the Gaia EDR3 sub-catalog of nearby stars. We explored their stellar parameters and spectral types using VOSA. Radii were also obtained using the Stefan-Boltzmann equation. Masses and ages were computed for some of the objects using evolutionary tracks and isochrones. Additional mass estimations were obtained with the MKs - M* relation. We then validated our results for the stellar parameters of two of our objects with spectra obtained with the TripleSpec instrument at the SOAR telescope, as well as those of our total amount of stars through a direct comparison with an independent sample from the literature. We revised the objects in our sample and compared their proper motion vectors with other sources within 30&#34; to identify possible companions and probed their RUWE values to identify unresolved companions. We present a catalog of physical parameters for 99 low-mass objects with distances from 43.2 to 111.3 pc. Teffs range from 2500 to 3400 K, with the majority of stars in the sample compatible with being M4 dwarfs. We obtained a good agreement between the stellar parameters computed with VOSA and the estimations from observed spectra, also when comparing with an independent sample from the literature. The distribution of masses obtained with VOSA is concentrated toward the very low-mass regime. Eight objects present values of RUWE >= 1.4 and seven are consistent with being part of a binary system.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design

The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain&#39;s infrastructures and facilities based on customer demand. Key economic decisions are taken about the location, number, and size of manufacturing facilities and warehouses based on the optimal solution. Therefore, improvements in the methods to address this question, which is known to be in the NP-hard complexity class, would have relevant financial consequences. Here, we implement in the D-Wave quantum annealer a hybrid classical-quantum annealing algorithm. The cost function with constraints is translated to a spin Hamiltonian, whose ground state encodes the searched result. As a benchmark, we measure the accuracy of results for a set of paradigmatic problems against the optimal published solutions (the error is on average below $1\%$), and the performance is compared against the classical algorithm, showing a remarkable reduction in the number of iterations. This work shows that state-of-the-art quantum annealers may codify and solve relevant supply-chain problems even still far from useful quantum supremacy.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Spreading the word -- current status of VO tutorials and schools

With some telescopes standing still, now more than ever simple access to archival data is vital for astronomers and they need to know how to go about it. Within European Virtual Observatory (VO) projects, such as AIDA (2008-2010), ICE (2010-2012), CoSADIE (2013-2015), ASTERICS (2015-2018) and ESCAPE (since 2019), we have been offering Virtual Observatory schools for many years. The aim of these schools are twofold: teaching (early career) researchers about the functionalities and possibilities within the Virtual Observatory and collecting feedback from the astronomical community. In addition to the VO schools on the European level, different national teams have also put effort into VO dissemination. The team at the Centre de Données astronomiques de Strasbourg (CDS) started to explore more and new ways to interact with the community: a series of blog posts on AstroBetter.com or a lunch time session at the virtual EAS meeting 2020. The Spanish VO has conducted virtual VO schools. GAVO has supported online archive workshops and maintains their Virtual Observatory Text Treasures. In this paper, we present the different formats in more detail, and report on the resulting interaction with the community as well as the estimated reach.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Digital-Analog Quantum Computation

Digital quantum computing paradigm offers highly-desirable features such as universality, scalability, and quantum error correction. However, physical resource requirements to implement useful error-corrected quantum algorithms are prohibitive in the current era of NISQ devices. As an alternative path to performing universal quantum computation, within the NISQ era limitations, we propose to merge digital single-qubit operations with analog multi-qubit entangling blocks in an approach we call digital-analog quantum computing (DAQC). Along these lines, although the techniques may be extended to any resource, we propose to use unitaries generated by the ubiquitous Ising Hamiltonian for the analog entangling block and we prove its universal character. We construct explicit DAQC protocols for efficient simulations of arbitrary inhomogeneous Ising, two-body, and $M$-body spin Hamiltonian dynamics by means of single-qubit gates and a fixed homogeneous Ising Hamiltonian. Additionally, we compare a sequential approach where the interactions are switched on and off (stepwise~DAQC) with an always-on multi-qubit interaction interspersed by fast single-qubit pulses (banged DAQC). Finally, we perform numerical tests comparing purely digital schemes with DAQC protocols, showing a remarkably better performance of the latter. The proposed DAQC approach combines the robustness of analog quantum computing with the flexibility of digital methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum Advantage in Cryptography with a Low-Connectivity Quantum Annealer

The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. However, quantum computers, and particularly quantum annealers, can also be helpful to construct secure cryptographic keys. Indeed, finding robust Boolean functions for cryptography is an important problem in sequence ciphers, block ciphers, and hash functions, among others. Due to the super-exponential size $\mathcal{O}(2^{2^n})$ of the associated space, finding $n$-variable Boolean functions with global cryptographic constraints is computationally hard. This problem has already been addressed employing generic low-connected incoherent D-Wave quantum annealers. However, the limited connectivity of the Chimera graph, together with the exponential growth in the complexity of the Boolean function design problem, limit the problem scalability. Here, we propose a special-purpose coherent quantum annealing architecture with three couplers per qubit, designed to optimally encode the bent function design problem. A coherent quantum annealer with this tree-type architecture has the potential to solve the $8$-variable bent function design problem, which is classically unsolved, with only $127$ physical qubits and $126$ couplers. This paves the way to reach useful quantum supremacy within the framework of quantum annealing for cryptographic purposes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computer

As the building block in symmetric cryptography, designing Boolean functions satisfying multiple properties is an important problem in sequence ciphers, block ciphers, and hash functions. However, the search of $n$-variable Boolean functions fulfilling global cryptographic constraints is computationally hard due to the super-exponential size $\mathcal{O}(2^{2^n})$ of the space. Here, we introduce a codification of the cryptographically relevant constraints in the ground state of an Ising Hamiltonian, allowing us to naturally encode it in a quantum annealer, which seems to provide a quantum speedup. Additionally, we benchmark small $n$ cases in a D-Wave machine, showing its capacity of devising bent functions, the most relevant set of cryptographic Boolean functions. We have complemented it with local search and chain repair to improve the D-Wave quantum annealer performance related to the low connectivity. This work shows how to codify super-exponential cryptographic problems into quantum annealers and paves the way for reaching quantum supremacy with an adequately designed chip.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Retrieving Quantum Information with Active Learning

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing

Shortcuts to adiabaticity are well-known methods for controlling the quantum dynamics beyond the adiabatic criteria, where counter-diabatic (CD) driving provides a promising means to speed up quantum many-body systems. In this work, we show the applicability of CD driving to enhance the digitized adiabatic quantum computing paradigm in terms of fidelity and total simulation time. We study the state evolution of an Ising spin chain using the digitized version of the standard CD driving and its variants derived from the variational approach. We apply this technique in the preparation of Bell and Greenberger-Horne-Zeilinger states with high fidelity using a very shallow quantum circuit. We implement this proposal in the IBM quantum computer, proving its usefulness for the speed up of adiabatic quantum computing in noisy intermediate-scale quantum devices.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

White Paper on MAAT@GTC

MAAT is proposed as a visitor mirror-slicer optical system that will allow the OSIRIS spectrograph on the 10.4-m Gran telescopio CANARIAS (GTC) the capability to perform Integral Field Spectroscopy (IFS) over a seeing-limited FoV 14.20&#39;&#39;x10&#39;&#39; with a slice width of 0.303&#39;&#39;. MAAT@GTC will enhance the resolution power of OSIRIS by 1.6 times as compared to its 0.6&#39;&#39; wide long-slit. All the eleven OSIRIS grisms and volume-phase holographic gratings will be available to provide broad spectral coverage with moderate resolution (R=600 up to 4100) in the 3600 - 10000 Å wavelength range. MAAT unique observing capabilities will broaden its use to the needs of the GTC community to unveil the nature of most striking phenomena in the universe well beyond time-domain astronomy. The GTC equipped with OSIRIS+MAAT will also play a fundamental role in synergy with other facilities, some of them operating on the northern ORM at La Palma. This White Paper presents the different aspects of MAAT@GTC - including scientific and technical specifications, outstanding science cases, and an outline of the instrument concept.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Enhanced connectivity of quantum hardware with digital-analog control

Quantum computers based on superconducting circuits are experiencing a rapid development, aiming at outperforming classical computers in certain useful tasks in the near future. However, the currently available chip fabrication technologies limit the capability of gathering a large number of high-quality qubits in a single superconducting chip, a requirement for implementing quantum error correction. Furthermore, achieving high connectivity in a chip poses a formidable technological challenge. Here, we propose a hybrid digital-analog quantum algorithm that enhances the physical connectivity among qubits coupled by an arbitrary inhomogeneous nearest-neighbour Ising Hamiltonian and generates an arbitrary all-to-all Ising Hamiltonian only by employing single-qubit rotations. Additionally, we optimize the proposed algorithm in the number of analog blocks and in the time required for the simulation. These results take advantage of the natural evolution of the system by combining the flexibility of digital steps with the robustness of analog quantum computing, allowing us to improve the connectivity of the hardware and the efficiency of quantum algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Exoplanet host-star properties: the active environment of exoplanets

The primary objectives of the ExoplANETS-A project are to: establish new knowledge on exoplanet atmospheres; establish new insight on influence of the host star on the planet atmosphere; disseminate knowledge, using online, web-based platforms. The project, funded under the EU&#39;s Horizon-2020 programme, started in January 2018 and has a duration ~3 years. We present an overview of the project, the activities concerning the host stars and some early results on the host stars.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Towards Pricing Financial Derivatives with an IBM Quantum Computer

Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates. Several stochastic dynamics have been proposed in the literature to model either the instantaneous interest rate or the instantaneous forward rate. A successful approach to model the latter is the celebrated Heath-Jarrow-Morton framework, in which its dynamics is entirely specified by volatility factors. On its multifactor version, this model considers several noisy components to capture at best the dynamics of several time-maturing forward rates. However, as no general analytical solution is available, there is a trade-off between the number of noisy factors considered and the computational time to perform a numerical simulation. Here, we employ the quantum principal component analysis to reduce the number of noisy factors required to accurately simulate the time evolution of several time-maturing forward rates. The principal components are experimentally estimated with the $5$-qubit IBMQX2 quantum computer for $2\times 2$ and $3\times 3$ cross-correlation matrices, which are based on historical data for two and three time-maturing forward rates. This manuscript is a first step towards the design of a general quantum algorithm to fully simulate on quantum computers the Heath-Jarrow-Morton model for pricing interest-rate financial derivatives. It shows indeed that practical applications of quantum computers in finance will be achievable in the near future.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

A Quantum Algorithm for Solving Linear Differential Equations: Theory and Experiment

We present and experimentally realize a quantum algorithm for efficiently solving the following problem: given an $N\times N$ matrix $\mathcal{M}$, an $N$-dimensional vector $\textbf{\emph{b}}$, and an initial vector $\textbf{\emph{x}}(0)$, obtain a target vector $\textbf{\emph{x}}(t)$ as a function of time $t$ according to the constraint $d\textbf{\emph{x}}(t)/dt=\mathcal{M}\textbf{\emph{x}}(t)+\textbf{\emph{b}}$. We show that our algorithm exhibits an exponential speedup over its classical counterpart in certain circumstances. In addition, we demonstrate our quantum algorithm for a $4\times4$ linear differential equation using a 4-qubit nuclear magnetic resonance quantum information processor. Our algorithm provides a key technique for solving many important problems which rely on the solutions to linear differential equations.

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preprint2018arXiv

Perceptrons from Memristors

Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for a network in which both the synapses and the neurons are implemented using memristors has been proposed so far. In the present work we introduce models for single and multilayer perceptrons based exclusively on memristors. We adapt the delta rule to the memristor-based single-layer perceptron and the backpropagation algorithm to the memristor-based multilayer perceptron. Our results show that both perform as expected for perceptrons, including satisfying Minsky-Papert&#39;s theorem. As a consequence of the Universal Approximation Theorem, they also show that memristors are universal function approximators. By using memristors for both the neurons and the synapses, our models pave the way for novel memristor-based neural network architectures and algorithms. A neural network based on memristors could show advantages in terms of energy conservation and open up possibilities for other learning systems to be adapted to a memristor-based paradigm, both in the classical and quantum learning realms.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Probabilistic Eigensolver with a Trapped-Ion Quantum Processor

Quantum simulation of complex quantum systems and their properties often requires the ability to prepare initial states in an eigenstate of the Hamiltonian to be simulated. In addition, to compute the eigenvalues of a Hamiltonian is in general a non-trivial problem. Here, we propose a hybrid quantum-classical probabilistic method to compute eigenvalues and prepare eigenstates of Hamiltonians which are simulatable with a trapped-ion quantum processor.

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