Search · four archives
Search · four archives
24 papers · ranked by Valyu relevance
Tuyen Nguyen, Mária Kieferová
Variational quantum algorithms (VQAs) are prominent candidates for near-term quantum advantage but lack rigorous guarantees of convergence and generalization. By contrast, quantum phase estimation (QPE) provides provable performance under the guiding state assumption, where access to a state with non-trivial overlap…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Robert J. P. T. de Keijzer, Luke Y. Visser, Oliver Tse, Servaas J. J. M. F. Kokkelmans
We report an algorithm that is able to tailor qubit interactions for individual variational quantum algorithm problems. The algorithm leverages the unique ability of a neutral atom tweezer platform to realize arbitrary qubit position configurations. These configurations determine the degree of entanglement available to…
Wu Shaojun, Jin Shan, Bayat, Abolfazl + 1 more
Shaojun Wu,1, 2 Shan Jin,1, 2 Abolfazl Bayat,1, 2, 3, ∗ and Xiaoting Wang1, 2, † 1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China 2 Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education…
Yuxiang Liu, Sixuan Li, Fanxu Meng, Zaichen Zhang + 1 more
The automated design of parameterized quantum circuits for variational algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era faces a fundamental limitation, as conventional differentiable architecture search relies on classical models that fail to adequately represent quantum gate interactions under hardware…
Kang-Min Hu, Min Namkung, Hyang-Tag Lim
Quantum computers have the potential to deliver speed-ups for solving certain important problems that are intractable for classical counterparts, making them a promising avenue for advancing modern computation. However, many quantum algorithms require deep quantum circuits, which are challenging to implement on current…
Huili Zhang, Yibin Guo, Guanglei Xu, Yulong Feng + 3 more
Determining the ground and low-lying excited states is critical in numerous scenarios. Recent work has proposed the ancilla-entangled variational quantum eigensolver (AEVQE) that utilizes entanglement between ancilla and physical qubits to simultaneously tagert multiple low-lying energy levels. In this work, we report…
J. V. S. Scursulim, Gabriel M. Langeloh, Victor L. Beltran, Samuraí Brito
Combinatorial optimization is a fundamental challenge in various domains, with portfolio optimization standing out as a key application in finance. Despite numerous quantum algorithmic approaches proposed for this problem, most overlook a critical feature of realistic portfolios: diversification. In this work, we…
Thomas Dumontier, Robin Ollive, Stephane Louise
Quantum computing offers several algorithms to compute the ground state of a problem Hamiltonian. The most desirable algorithms belong to the Fault Tolerant QuantumComputing (FTQC) regime, such as quantum algorithms with repetitive structure like Quantum Phase Estimation (QPE) and Quantum Signal Processing (QSP).…
Georgios Arapantonis, Paraj Titum, Gregory Quiroz
Entanglement is widely regarded as a key resource underlying the power of quantum algorithms and their potential to achieve quantum advantage. With the emergence of variational quantum algorithms, however, questions have arisen regarding how entanglement relates to problem structure and algorithmic performance in…
Amir Hossein Salehi Shayegan
Time-fractional diffusion equations have emerged as powerful models for describing anomalous transport phenomena in physics, biology and engineering. To address the computational challenges arising from their non-local operators, we employ the WEB-spline finite element method, which provides a flexible and accurate…
Diego Tancara, Herbert Díaz-Moraga, Dardo Goyeneche
ADAPT-VQE for Molecular Simulation Authors: Diego Tancara, Herbert Díaz-Moraga, Dardo Goyeneche Among the variational quantum algorithms designed for NISQ devices, the adaptive derivative-assembled problem-tailored variational quantum eigensolver (ADAPT-VQE) stands out for its robustness against barren plateaus…
Xinwei Lee, Xinjian Yan, Ningyi Xie, Yoshiyuki Saito + 4 more
Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world…
Rami Gherib, Roya Radgohar, Anastasiia Pusenkova, Seyyed Mehdi Hosseini Jenab + 4 more
Gaussian wavepacket (GWP) methods are prevalent means of solving the nuclear time-dependent Schrodinger equation (TDSE) on classical computers. They consist of representing the nuclear wave function as a superposition of Gaussians. Here, we present a variational hybrid continuous-variable discrete-variable (CV-DV)…
Mohammad Aamir Sohail, Ranga R. Sudharshan, S. Sandeep Pradhan, Arvind Rao
We present a new Hamiltonian-learning framework based on time-resolved measurement data from a fixed local IC-POVM and its application to inferring gene regulatory networks. We introduce the quantum Hamiltonian-based gene-expression model (QHGM), in which gene interactions are encoded as a parameterized Hamiltonian…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Shota Yokoyama, Atsushi Sakaguchi, Jun-ichi Yoshikawa, Hironari Nagayoshi + 5 more
We experimentally demonstrate the continuous-variable quantum approximate optimization algorithm (CV-QAOA) for multi-variable problems and multiple QAOA depths using a measurement-based CV quantum computing platform on a quad-rail lattice (QRL) cluster state. We propose a systematic method to map arbitrary quadratic…
Anuj Guruacharya, Binita Rajbanshi
Near-term quantum algorithms such as the variational quantum eigensolver (VQE) have been widely explored for small-molecule electronic structure calculations, yet their relevance for biologically motivated peptide systems remains largely untested. Here, we apply a rigorously controlled, fragment-based VQE workflow to a…
Authors not listed
We present a comprehensive theoretical analysis of quantum subspace diagonalization methods for molecular electronic structure calculations, establishing rigorous complexity bounds and convergence guarantees. Building on recent developments in adaptive quantum algorithms for chemical systems, we formulate a general…
Azamat Salamatov, Gowtham Atluri
How long a drug stays bound to its target-the residence time - is now recognized as a stronger in vivo efficacy driver than binding affinity alone. Yet current machine learning (ML) models for dissociation kinetics (koff) ignore two critical sources of structure information: (1) the change in protein–ligand geometry…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
Rania Derouich, Nour El Houda Mathlouthi
We present the first systematic, hardware-executed benchmark of twelve distinct quantum data-encoding strategies for drug-response prediction on a real superconducting quantum processing unit (QPU). All experiments were conducted on the IQM Garnet 20-qubit QPU via the IQM Resonance cloud platform, using the Qrisp…
Authors not listed
The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
Christos Papalitsas, Ioannis Mouratidis, Michail Patsakis, Evangelos Stogiannos + 2 more
The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of…