arXiv subjects

Wibe Albert de Jong

Publications and source records attributed to Wibe Albert de Jong.

17 recordsLinked to original sources

arXiv2026

$T$-efficient unitary synthesis via block-sparse Hamiltonian simulation

We construct Clifford+$T$ circuits implementing arbitrary $n$-qubit unitaries with worst-case $T$-count $\widetilde{O}(N^{8/7})$ at constant operator-norm error, where $N=2^n$, improving the previous exponent $5/4$. The construction uses $\widetilde{O}(N^{6/7})$ clean ancillas. The main idea is to reduce the data addressed by each query to the block-encoding circuit for a sparse piece while controlling the additional simulation work needed to combine the pieces. We simulate a Hermitian logarithm of the target by flattening its traceless part with a two-layer random similarity transformation, randomly partitioning the transformed generator into block-sparse Hermitian pieces, and combining their evolutions using qDRIFT. For a target operator-norm error $ε\in(0,1/2)$, let $L=\log_2(N/ε)$, the qubit count plus the requested bits of accuracy. By successively correcting the approximation error, we obtain a circuit using $\widetilde{O}(N^{8/7}L^{3/7}+NL)$ $T$ gates and $\widetilde{O}(N)$ clean ancillas whenever $L\le N$. We also prove that, at constant precision, the exponent $8/7$ is optimal within a sequential block-SELECT model.

quant-ph↗

arXiv2026

A hybrid method for quantum dynamics simulation

We propose a hybrid approach to simulate quantum many-body dynamics by combining Trotter-based quantum evolution with classical dynamic mode decomposition. In quantum simulation, the interest often lies in estimating observables rather than explicitly obtaining the wave function. Instead of characterizing a quantum state, our method predicts observables of the quantum state at later times by using data from a set of short-time measurements from a quantum computer. For a fixed set of measurements, the global error bound for our method scales at most as $\mathcal{O}(t^{3/2})$, with constants determined by the DMD approximation error and the conditioning of the learned modes. We apply our method to quench dynamics in the Hubbard model and nearest neighbor spin systems and show that the observable properties can be predicted up to a reasonable error by controlling the number of data points obtained from the quantum measurements.

quant-ph↗

arXiv2026

Exponentially Compressed and Garbage-Free Alias Sampling for Polynomial State Preparation

Coherent alias sampling implement an important subroutine for block encoding and qubitization based quantum simulation using quantum lookup table. Related constructions exploit data lookup and matrix structure to implement block encodings for use in quantum singular value transformation. For a state on $n$ qubits, constructing and storing the alias table requires both classical and quantum resources exponential in $n$. We show that when the amplitudes are samples of a polynomial of degree at most $d$, the table can be represented and evaluated coherently with cost polynomial in $n$, $d$, exponentially reduced from $Θ(2^n)$ classical and quantum cost. We construct a quantum circuit for coherently computing the alias table for polynomial states using reversible integer arithmetic. By employing fast arithmetic, we construct a deterministic circuit for preparing polynomial states over Clifford+$T$ with a complete gate count of $\tilde{O}(ndB+B^2)$, where arithmetic width $B=\tilde{O}(dn+d\log(d+1))$. In the high precision limit for state fidelity $ε$ with fixed $n$ and $d$, it is sufficient have $B=Θ(\log(1/ε))$ and the complete gate count becomes $O(\log^3(1/ε)\log\log(1/ε))$. Additionally, the effective representation of the alias table can also be used for classical sampling algorithms. Furthermore, we develop a novel technique for uncomputing the auxilary register generated by coherent alias sampling. This technique opens a research direction in sampling based initial state preparation.

quant-ph↗

arXiv2026

AC/DC: Automated Compilation for Dynamic Circuits

Dynamic quantum circuits incorporate mid-circuit measurements (MCMs) and feed-forward operations are crucial for manipulating quantum information. They have been broadly used in quantum error correction and quantum teleportation. Recently, they are utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms. In this paper, we present AC/DC, a novel Automated Compilation framework for generating Dynamic quantum Circuits that prepare any unitary operators or states, leveraging numerical optimization-based circuit synthesis methods. The first contribution is introducing optimization objective functions incorporating MCMs and feed-forward operations. The second contribution is embedding these into a popular open-source quantum circuit synthesis framework. We demonstrate generating dynamic circuits for long range entangling gates, circuit optimization, lattice simulations, and state preparation, with validation through simulation and quantum hardware. Furthermore, we perform a noise analysis to assess the impact of MCM and gate errors, identifying scenarios where dynamic circuits provide significant benefits. The dynamic circuits generated by our framework show substantial improvements in reducing circuit depth and, in some cases, the number of gates. To our knowledge, this is the first practical procedure to generate dynamic quantum circuits, paving the way for enhanced circuit generation and optimization methods for near-term quantum computers.

quant-ph↗

arXiv2026

Scalable Benchmarking Framework for Dynamic Quantum Circuits

Dynamic quantum circuits with mid-circuit measurements (MCMs) and feed-forward operations play a crucial role in various applications, such as quantum error correction and quantum algorithms. With advancements in quantum hardware enabling the implementation of MCM and feed-forward loops, the use of dynamic circuits has become increasingly prevalent. There is a significant need for a benchmarking framework specially designed for dynamic circuits to capture their unique properties, as current benchmarking tools are designed primarily for unitary circuits and cannot be trivially extended to dynamic circuits. We propose dynamarq, a scalable and hardware-agnostic benchmarking framework for dynamic circuits. We collect a set of dynamic circuit benchmarks spanning various applications and propose a broad set of circuit features to characterize the structure of these dynamic circuits. We run them on two IBM quantum processors and the Quantinuum Helios-1E emulator, and propose scalable, application-dependent fidelity scores for each benchmark based on hardware execution results. We perform statistical modeling to identify correlations between circuit features and fidelity scores, and demonstrate highly accurate fidelity prediction using our model. Our model parameters are also transferable across hardware backends and calibration cycles. Our framework facilitates the understanding of dynamic circuit structures and provides insights for designing and optimizing dynamic circuits to achieve high execution fidelity on quantum hardware.

quant-ph↗

arXiv2026

Parallel Circuit Execution for Scalable Quantum Computation

Today's quantum processors have tens to hundreds of physical qubits, but reliable execution of arbitrary circuits remains limited to fewer than 30 entangled qubits across hardware modalities. Building upon prior work, we introduce an error- and topology-aware method for mapping multiple independent circuits onto disjoint regions of a single large-scale QPU for parallel circuit execution. For applications with many similarly sized circuits, such as observable estimation for Hamiltonian simulation, this approach can reduce billed QPU execution time, with ideal speedup proportional to the number of usable partitions. We demonstrate the approach on IBM's 156-qubit ibm_boston processor using standard QED-C benchmark and Hamiltonian-based observable-estimation workloads. Compared with standard sequential execution, parallel execution reduces billed execution time by 3.5-5.5x while retaining 83-92% of the sequential fidelity. We further evaluate the scaling of parallel circuit execution using GPU-accelerated classical simulation, distributing measurement circuits across GPUs via MPI. Using CUDA-Q on the NERSC Perlmutter system, we achieve up to 13.8x speedup on 16 GPUs (86% parallel efficiency) for an H2 electronic-structure simulation, with scaling evaluated across multiple Hamiltonians and circuit counts. These results provide an indication of the performance ceiling that parallel execution on future quantum hardware may eventually approach. Both execution modes are implemented as a runtime option within the QED-C Application-Oriented Benchmark suite. Together, the results show that circuit-level parallelism can reduce execution cost on current quantum hardware and simulation time on GPU clusters, with the potential for greater benefits as device quality and qubit counts increase.

quant-ph↗

arXiv2025

Predicting open quantum dynamics with data-informed quantum-classical dynamics

We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (Calcium Fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (Rubrene) with accuracy comparable to nearly exact numerical methods.

quant-ph↗

arXiv2025

Quantum Sensing using Geometrical Phase in Qubit-Oscillator Systems

We present a quantum sensing protocol for coupled qubit-oscillator systems that surpasses the standard quantum limit (SQL) by exploiting a geometrical phase. The signal is encoded in the geometrical phase that is proportional to the area enclosed in oscillator phase space. This area is amplified through squeezing, enabling sensitivities beyond the SQL. Our method is independent of oscillator's initial state, amenable to sensing with high-temperature or logical error-corrected states. The protocol shows robustness to qubit Markovian noise and preserves its state-independence, underscoring its practicality for next-generation quantum metrology. We demonstrate application to force sensing beyond the SQL in longitudinally coupled systems, and to high-precision measurements of couplings and pulse calibration surpassing SQL in dispersively coupled circuit quantum electrodynamics (cQED) architectures.

quant-ph↗

arXiv2025

Combining Error Detection and Mitigation: A Hybrid Protocol for Near-Term Quantum Simulation

Practical implementation of quantum error correction is currently limited by near-term quantum hardware. In contrast, quantum error mitigation has demonstrated strong promise for improving the performance of noisy quantum circuits without the requirement of full fault tolerance. In this work, we develop a hybrid error suppression protocol that integrates Pauli twirling, probabilistic error cancellation, and the $[[n, n-2, 2]]$ quantum error detecting code. In addition, to reduce overhead from error mitigation components of our method, we modify Pauli twirling by lowering the number of Pauli operators in the twirling set, and apply probabilistic error cancellation at the end of the encoded circuit to remove undetectable errors. Finally, we demonstrate our protocol on a non-Clifford variational quantum eigensolver circuit that estimates the ground state energy of $\rm H_2$ using both \texttt{qiskit} AerSimulator and the IBM quantum processor \texttt{ibm\_brussels}.

quant-ph↗

arXiv2025

Ground state energy and magnetization curve of a frustrated magnetic system from real-time evolution on a digital quantum processor

Models of interacting many-body quantum systems that may realize new exotic phases of matter, notably quantum spin liquids, are challenging to study using even state-of-the-art classical methods such as tensor network simulations. Quantum computing provides a promising route for overcoming these difficulties to find ground states, dynamics, and more. In this paper, we argue that recently developed hybrid quantum-classical algorithms based on real-time evolution are promising methods for solving a particularly important model in the search for spin liquids, the antiferromagnetic Heisenberg model on the two-dimensional kagome lattice. We show how to construct efficient quantum circuits to implement time evolution for the model and to evaluate key observables on the quantum computer, and we argue that the method has favorable scaling with increasing system size. We then restrict to a 12-spin star plaquette from the kagome lattice and a related 8-spin system, and we give an empirical demonstration on these small systems that the hybrid algorithms can efficiently find the ground state energy and the magnetization curve. For these demonstrations, we use four levels of approximation: exact state vectors, exact state vectors with statistical noise from sampling, noisy classical emulators, and (for the 8-spin system only) real quantum hardware, specifically the Quantinuum H1-1 processor; for the noisy simulations and hardware demonstration, we also employ error mitigation strategies based on the symmetries of the Hamiltonian. Our results strongly suggest that these hybrid algorithms present a promising direction for studying quantum spin liquids and more generally for resolving important unsolved problems in condensed matter theory and beyond.

quant-ph↗

arXiv2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

cs.LG↗

arXiv2024

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

quant-ph↗

arXiv2024

AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.

math.OC↗

arXiv2024

Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies

This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning models, and optimization algorithms. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. It leverages a machine learning model trained with low-fidelity simulation data, in each optimization cycle, thereby proficiently predicting a target variable and discerning whether a high-fidelity simulation is necessitated, which notably conserves computational resources. Additionally, the machine learning model is strategically deployed prior to optimization to compress the design space boundaries, thereby further accelerating convergence toward the optimal solution. The methodology has been employed to enhance two optimization algorithms, namely Differential Evolution and Particle Swarm Optimization. Comparative analyses illustrate performance improvements across both algorithms. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.}

cs.CE↗

arXiv2024

Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.

cs.LG↗

arXiv2024

Adaptive variational simulation for open quantum systems

Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop quantum algorithms that can effectively simulate open quantum systems. Here we present an adaptive variational quantum algorithm for simulating open quantum system dynamics described by the Lindblad equation. The algorithm is designed to build resource-efficient ansatze through the dynamical addition of operators by maintaining the simulation accuracy. We validate the effectiveness of our algorithm on both noiseless simulators and IBM quantum processors and observe good quantitative and qualitative agreement with the exact solution. We also investigate the scaling of the required resources with system size and accuracy and find polynomial behavior. Our results demonstrate that near-future quantum processors are capable of simulating open quantum systems.

quant-ph↗

arXiv2023

Powerful Quantum Circuit Resizing with Resource Efficient Synthesis

In the noisy intermediate-scale quantum era, mid-circuit measurement and reset operations facilitate novel circuit optimization strategies by reducing a circuit's qubit count in a method called resizing. This paper introduces two such algorithms. The first one leverages gate-dependency rules to reduce qubit count by 61.6% or 45.3% when optimizing depth as well. Based on numerical instantiation and synthesis, the second algorithm finds resizing opportunities in previously unresizable circuits via dependency rules and other state-of-the-art tools. This resizing algorithm reduces qubit count by 20.7% on average for these previously impossible-to-resize circuits.

quant-ph↗