A5085105920- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Stochastic Gradient Optimization Techniques
- Neural Networks and Reservoir Computing
- Quantum Mechanics and Applications
- Machine Learning and Algorithms
- Quantum-Dot Cellular Automata
- Supply Chain and Inventory Management
- Low-power high-performance VLSI design
- Advancements in Semiconductor Devices and Circuit Design
- Smart Grid Energy Management
- Parallel Computing and Optimization Techniques
- Electric Power System Optimization
- Advanced Thermodynamics and Statistical Mechanics
- Computability, Logic, AI Algorithms
- Sustainable Supply Chain Management
- Complexity and Algorithms in Graphs
- Stochastic processes and financial applications
- Advanced Manufacturing and Logistics Optimization
- Face and Expression Recognition
- Bioeconomy and Sustainability Development
- Microgrid Control and Optimization
- Advanced Bandit Algorithms Research
- Neural Networks and Applications
IBM Research - Zurich
2014-2024
École Polytechnique Fédérale de Lausanne
2023
Czech Academy of Sciences, Institute of Physics
2023
University of Wisconsin–Madison
2021
Nokia (Germany)
2018
Fraunhofer Institute for Systems and Innovation Research
2001-2002
Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization advantage often requires ability load data efficiently into quantum states. However, best known methods require $\mathcal{O}\left(2^n\right)$ gates an exact representation generic structure $n$-qubit state. This scaling can easily predominate complexity algorithm and, thereby, impair advantage. Our work presents hybrid quantum-classical for efficient, approximate state...
This article outlines our point of view regarding the applicability, state-of-the-art, and potential quantum computing for problems in finance. We provide an introduction to as well a survey on problem classes finance that are computationally challenging classically which algorithms promising. In main part, we describe detail specific applications arising financial services, such those involving simulation, optimization, machine learning problems. addition, include demonstrations IBM Quantum...
Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and used to find solutions for combinatorial optimization problems. Approaches discussed in the literature minimize expectation of problem Hamiltonian a parameterized trial state. The is estimated as sample mean set measurement outcomes, while parameters state are optimized classically. This procedure fully justified mechanical observables such molecular energies. In case...
We present a methodology to price options and portfolios of on gate-based quantum computer using amplitude estimation, an algorithm which provides quadratic speedup compared classical Monte Carlo methods. The that we cover include vanilla options, multi-asset path-dependent such as barrier options. put emphasis the implementation circuits required build input states operators needed by estimation different option types. Additionally, show simulation results highlight how implement contracts....
There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers such employ relaxations, which replace binary variables with continuous ones, instance the form higher-dimensional matrix-valued (semidefinite programming). Under Unique Games Conjecture, these relaxations often provide best performance ratios available classically polynomial time. Here, we discuss how to warm-start optimization initial state...
The computation of molecular excitation energies is essential for predicting photo-induced reactions chemical and technological interest. While the classical computing resources needed this task scale poorly, quantum algorithms emerge as promising alternatives. In particular, extension variational eigensolver algorithm to an attractive option. However, there currently a lack such correlated systems that amenable near-term, noisy hardware. work, we propose well-established equation motion...
Abstract We introduce a variant of Quantum Amplitude Estimation (QAE) , called Iterative QAE (IQAE), which does not rely on Phase (QPE) but is only based Grover’s Algorithm reduces the required number qubits and gates. provide rigorous analysis IQAE prove that it achieves quadratic speedup up to double-logarithmic factor compared classical Monte Carlo simulation with provably small constant overhead. Furthermore, we show an empirical study our algorithm outperforms other known variants...
Abstract Predicting the three-dimensional structure of a protein from its primary sequence amino acids is known as folding problem. Due to central role proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half century. Although classical algorithms provide practical solutions sampling conformation space small proteins, they cannot tackle intrinsic NP-hard complexity problem, even when reduced simplest Hydrophobic-Polar...
Quantum computers may provide good solutions to combinatorial optimization problems by leveraging the Approximate Optimization Algorithm (QAOA). The QAOA is often presented as an algorithm for noisy hardware. However, hardware constraints limit its applicability problem instances that closely match connectivity of qubits. Furthermore, must outpace classical solvers. Here, we investigate swap strategies map dense into linear, grid and heavy-hex coupling maps. A line-based strategy works best...
Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared any known classical algorithm for certain data sets. The training of such models corresponds solving convex optimization problem either via its primal or dual formulation. Due probabilistic nature mechanics, algorithms are affected by statistical uncertainty, which major impact on their complexity. We show can be solved in...
Real- and imaginary-time quantum state evolutions are crucial in physics chemistry for exploring dynamics, preparing ground states, computing thermodynamic observables. On near-term devices, variational time evolution is a promising candidate these tasks, as the required circuit model can be tailored to available devices' capabilities. Due evaluation of geometric tensor (QGT), however, this approach quickly becomes infeasible relevant system sizes. Here, we propose dual formulation...
A key requirement to perform simulations of large quantum systems on near-term hardware is the design algorithms with short circuit depth that finish within available coherence time. way stay limits reduce number gates by implementing a gate set matches requirements specific algorithm interest directly in hardware. Here, we show exchange-type are promising choice for simulating molecular eigenstates devices since these preserve excitations system. Complementing theoretical work Barkoutsos et...
In this paper we discuss Grover Adaptive Search (GAS) for Constrained Polynomial Binary Optimization (CPBO) problems, and in particular, Quadratic Unconstrained (QUBO) as a special case. GAS can provide quadratic speed-up combinatorial optimization problems compared to brute force search. However, requires the development of efficient oracles represent flag states that satisfy certain search criteria. general, be achieved using quantum arithmetic, however, is expensive terms Toffoli gates...
We present and analyze a quantum algorithm to estimate credit risk more efficiently than Monte Carlo simulations can do on classical computers. More precisely, we the economic capital requirement, i.e. difference between Value at Risk expected value of given loss distribution. The requirement is an important metric because it summarizes amount required remain solvent confidence level. implement this problem for realistic distribution its scaling size. In particular, provide estimates total...
Abstract We present a quantum algorithm that analyzes risk more efficiently than Monte Carlo simulations traditionally used on classical computers. employ amplitude estimation to price securities and evaluate measures such as Value at Risk Conditional gate-based computer. Additionally, we show how implement this trade-off the convergence rate of circuit depth. The shortest possible depth—growing polynomially in number qubits representing uncertainty—leads O ( M −2/3 ), where is samples. This...
Quantum machine learning could possibly become a valuable alternative to classical for applications in High Energy Physics by offering computational speed-ups. In this study, we employ support vector with quantum kernel estimator (QSVM-Kernel method) recent LHC flagship physics analysis: $t\bar{t}H$ (Higgs boson production association top quark pair). our simulation study using up 20 qubits and 50000 events, the QSVM-Kernel method performs as well its counterparts three different platforms...
The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Natural Gradient Descent and Variational Imaginary Time Evolution. Computing the full QFIM for model with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>parameters, however, computationally expensive generally requires<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic"...
Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant correction. For instance, quasiprobability method simulates a noise-free computer using noisy one, with caveat of only producing correct expected values observables. The cost this technique manifests as sampling overhead which scales exponentially in number corrected gates. In work, we present new algorithm based mathematical optimization that aims to choose decomposition...
In this article, we extend variational quantum optimization algorithms for quadratic unconstrained binary problems to the class of mixed problems. This allows us combine decision variables with continuous variables, which, instance, enables modeling inequality constraints via slack variables. We propose two heuristics and introduce transaction settlement problem demonstrate them. Transaction is defined as exchange securities cash between parties crucial financial market infrastructure. test...
Quantum amplitude estimation (QAE) can achieve a quadratic speedup for applications classically solved by Monte Carlo simulation. A key requirement to realize this advantage is efficient state preparation. If preparation too expensive, it diminish the quantum advantage. Preparing arbitrary states has exponential complexity with respect number of qubits, and thus, not applicable. Currently known techniques require problems based on log-concave probability distributions, involve learning an...
We present a quantum algorithm to solve systems of linear equations the form Ax = b , where A is tridiagonal Toeplitz matrix and results from discretizing an analytic function, with circuit complexity O (1/√ε, poly (log κ, log N )), denotes number equations, ε accuracy, κ condition number. The repeat-until-success has be run (κ/(1-ε)) times succeed, leveraging amplitude amplification, needs sampled (1/ε 2 ) times. Thus, achieves exponential improvement respect over classical methods. In...
We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as box. This contrast typical setting of algorithms for where classical provided Quadratic Unconstrained Binary Optimization problem and mapped sum Pauli operators. Furthermore, we provide theoretical justification our method based on convergence guarantees imaginary time evolution. To investigate performance its potential...