2 citations · 3 across the 23 of their papers we have counts for
23 papers
Blind Catalytic Quantum Error Correction: Target-State Estimation and Fidelity Recovery Without A Priori Knowledge
Hikaru Wakaura
Catalytic quantum error correction (CQEC) amplifies residual coherence with a reusable catalyst, giving threshold-free recovery whenever the target coherent modes survive in the no…
The -Peak: Covariant Recovery on Four Organic Qubit Platforms
Hikaru Wakaura, Taiki Tanimae
We characterize where, in the noise parameter space of the uniform dephasing--depolarizing channel , a deterministic, \emph{nonlin…
Markovian dephasing has no entanglement-breaking threshold, and a fixed tolerance will report one anyway
Hikaru Wakaura, Taiki Tanimae
When modelling decoherence in a biological spin system it is tempting to seek a critical rate beyond which the channel is entanglement-breaking (EB) and quantum resources are gone.…
Quantum Reservoir Autoencoder: Conditions, Protocol, and Noise Resilience
Hikaru Wakaura, Taiki Tanimae
Quantum reservoir computing exploits fixed quantum dynamics and a trainable linear readout to process temporal data, yet reversing the transformation -- reconstructing the input fr…
Catalytic Quantum Error Correction: Theory, Efficient Catalyst Preparation, and Numerical Benchmarks
Hikaru Wakaura, Taiki Tanimae
Quantum computers promise transformative speedups, but environmental noise destroys their fragile states. Conventional quantum error correction (QEC) encodes information redundantl…
Evaluation artifacts in reversible quantum reservoir protocols: a withdrawal of our own claims, and an entangled channel that survives the controls
Hikaru Wakaura, Taiki Tanimae
This replaces v1, which reported a ten-order shot-noise suppression from a quantum reservoir autoencoder and a two-phase protocol reconstructing unseen inputs at MSE ~ 10^-4. We wi…
Merged amplitude encoding for Chebyshev quantum Kolmogorov-Arnold networks
Hikaru Wakaura
Quantum Kolmogorov--Arnold networks evaluate each edge activation function as a quantum inner product, creating a trade-off between qubit count and the number of circuit executions…
Generative Adversarial Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura
Kolmogorov Arnold Networks is a novel multilayer neuromorphic network that can exhibit higher accuracy than a neural network. It can learn and predict more accurately than neural n…
Solving the traveling salesman problem on qubits with a variational quantum Kolmogorov--Arnold network: exact-gradient CVaR training and generalization over time-dependent graphs
Hikaru Wakaura
The one-hot encoding of the traveling salesman problem (TSP) on quantum hardware needs qubits for sites and penalty terms that dominate the cost landscape. Permutatio…
Quantum Reservoir GAN
Hikaru Wakaura
Quantum machine learning is known as one of the promising applications of quantum computers. Many types of quantum machine learning methods have been released, such as Quantum Anne…
Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground sta…
The effect of Quantum Time Crystal Computing to Quantum Machine Learning methods
Hikaru Wakaura, Andriyan B. Suksmono
Many body localization shows the robustness for external perturbations and time reversal symmetry on Time Crystal. This Time Crystal prolongs the coherence time, hence, it is used…
Enhanced Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
The Kolmogorov-Arnold Network (KAN) places the trainable functions on the synapses rather than on the neurons. Existing quantum implementations either lack accuracy (Variational Qu…
Few-sample regression with an adaptively grown variational quantum Kolmogorov--Arnold network
Hikaru Wakaura, Rahmat Mulyawan, Andriyan B. Suksmono
Kolmogorov-Arnold networks place learnable one-dimensional functions on the edges of a network rather than fixed activations on its nodes, and several quantum realisations have bee…
Derivation of Hamiltonians from time propagations using Born machines
Hikaru Wakaura, Andriyan Bayu Suksmono
Recently there are more promising qubit technology such as Majorana fermions Rydberg atoms and Silicon quantum dot have yet to be developed for realizing a quantum computer than Su…
Splitting of energy levels of Spin-vortex Induced Loop Currents by feeding external currents
Hikaru Wakaura, Takao Tomono
The spin-vortex-induced loop current (SVILC) is a nano-sized loop current predicted to exist in the CuO plane in the bulk of hole-doped cuprate superconductors. It is a persist…
Period and intrinsic noises of permanent spin vortex system centered by small polaron
Hikaru Wakaura, Takao Tomono
Some fascinating phenomena have been investigated, hence spin vortex systems are a hot subarea of solid-state physics. However, simulations of these systems require some simplifica…
A prescreening method for variational quantum state eigensolver
Hikaru Wakaura, Andriyan B. Suksmono
The development of Fault-Tolerant Quantum Computer (FTQC) gradually raises a possibility to implement the Quantum Phase Estimation (QPE) algorithm. However, QPE works only for norm…
Evaluation on Genetic Algorithms as an optimizer of Variational Quantum Eigensolver(VQE) method
Hikaru Wakaura, Takao Tomono, Shoya Yasuda
Variational-Quantum-Eigensolver(VQE) method on a quantum computer is a well-known hybrid algorithm to solve the eigenstates and eigenvalues that uses both quantum and classical com…
Genetic-Multi-initial Generalized VQE: Advanced VQE method using Genetic Algorithms then Local Search
Hikaru Wakaura, Takao Tomono
Variational-Quantum-Eigensolver (VQE) method has been known as the method of chemical calculation using quantum computers and classical computers. This method also can derive the e…
Frame Superposition Cluster: The method to derive the transition matrices in high accuracy
Hikaru Wakaura, Takao Tomono
Variational Quantum Eigensolver (VQE) method is the way to derive the wave functions from their eigen energies using quantum computers. But, the methods to utilize the superpositio…
Tangent Vector Variational Quantum Eigensolver: A Robust Variational Quantum Eigensolver against the inaccuracy of derivative
Hikaru Wakaura, Andriyan Bayu Suksmono
Observing rapid developments of both the number of qubits and quantum volume, especially with recent advances in ion-trap quantum computers, it is no doubt that Fault-Tolerant-Quan…
Effect of constraint and Tabu Search term on Variational Quantum Eigensolver and Subspace-Search Variational Quantum Eigensolver
Hikaru Wakaura, Takao Tomono
Subspace-Search Variational Quantum Eigen solver (SSVQE) is a searching method of multiple states and relies on the unitarity of transformations to ensure the orthogonality of outp…