arXiv subjects

Melody Lee

Publications and source records attributed to Melody Lee.

5 recordsLinked to original sources

arXiv2026

Studying energy-resolved transport with wavepacket dynamics on quantum computers

Probing energy-dependent transport in quantum simulators requires preparing states with tunable energy and small energy variance. Existing approaches often study quench dynamics of simple initial states, such as computational basis states, which are far from energy eigenstates and therefore limit the achievable energy resolution. In this work, we propose using wavepackets to probe transport properties with improved energy resolution. To demonstrate the utility of this approach, we prepare and evolve wavepackets on Quantinuum's H2-2 quantum computer and identify an energy-dependent localization transition in the Anderson model on an 8x7 lattice--a finite-size mobility edge. We observe that a wavepacket initialized at low energy remains spatially localized under time evolution, while a high-energy wavepacket delocalizes, consistent with the presence of a mobility edge. Crucial to our experiments is an error mitigation strategy that infers the noiseless output bit string distribution using maximum-likelihood estimation. Compared to post-selection, this method removes systematic errors and reduces statistical uncertainty by up to a factor of 5. We extend our methods to the many-particle regime by developing a quantum algorithm for preparing quasiparticle wavepackets in a one-dimensional model of interacting fermions. This technique has modest quantum resource requirements, making wavepacket-based studies of transport in many-body systems a promising application for near-term quantum computers.

quant-ph↗

arXiv2025

Personalized Improvement of Standard Readout Error Mitigation using Low-Depth Circuits and Machine Learning

Quantum computers have shown promise in improving algorithms in a variety of fields. The realization of these advancements is limited by the presence of noise and high error rates, which become prominent especially with increasing system size. Mitigation techniques using matrix inversions, unfolding, and deep learning, among others, have been leveraged to reduce this error. However, these methods are not reflective of the entire gate set of the quantum device and may need further tuning depending on the distance from the most recent calibration time. This paper proposes a method of improvement to numerical readout error techniques, where the readout error model is further refined using measured probability distributions from a collection of low-depth circuits. We use machine learning to improve the readout error model for the quantum system, testing the circuits on the simulated IBM Perth backend using Qiskit. We demonstrate a median 6.6% improvement in fidelity, 29.9% improvement for mean-squared error, and 10.3% improvement in Hellinger distance over the standard error mitigation approach for a seven-qubit system with a circuit depth of four. With further focus directed towards such improvement of these error mitigation techniques, we are one step closer to the fault-tolerant quantum computing era.

quant-ph↗

arXiv2025

Hadamard Random Forest: Reconstructing real-valued quantum states with exponential reduction in measurement settings

Quantum tomography is a crucial tool for characterizing quantum states and devices and estimating nonlinear properties of the systems. Performing full quantum state tomography on an $N_\mathrm{q}$ qubit system requires an exponentially increasing overhead with $O(3^{N_\mathrm{q}})$ distinct Pauli measurement settings to resolve all complex phases and reconstruct the density matrix. However, many potential quantum computing applications, such as linear system solves, require only real-valued amplitudes. We introduce a readout method for real-valued quantum states that reduces measurement settings required for state vector reconstruction to $O(N_\mathrm{q})$; the post-processing cost remains exponential $\Omega(2^{N_\mathrm{q}})$. This approach offers a substantial speedup over conventional tomography. We experimentally validate our method up to 10 qubits on the latest available IBM quantum processor and demonstrate that it accurately extracts key properties such as entanglement and magic. Our method also outperforms the standard SWAP test for state overlap estimation. This calculation resembles a numerical integration in certain cases and can be applied to extract nonlinear properties, which are important in application fields. We further implement the method to readout the solution from a quantum linear solver.

quant-ph↗

arXiv2024

Modified Multiple Sequence Alignment Algorithm on Quantum Annealers (MAQ)

We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing. To understand the human genome, researchers compare extensive sets of these genetic sequences -- or their protein counterparts -- to identify patterns. This comparison begins with the alignment of the set of (multiple) sequences. However, this alignment problem is considered nondeterministically-polynomial time complete and, thus, current classical algorithms at best rely on brute force or heuristic methods to find solutions. Quantum annealing algorithms are able to bypass this need for sheer brute force due to their use of quantum mechanical properties. However, due to the novelty of these algorithms, many are rudimentary in nature and limited by hardware restrictions. We apply progressive alignment techniques to modify annealing algorithms, achieving a linear reduction in spin usage whilst introducing more complex heuristics to the algorithm. This opens the door for further exploration into quantum computing-based bioinformatics, potentially allowing for a deeper understanding of disease detection and monitoring.

quant-ph↗

arXiv2024

A multiple-circuit approach to quantum resource reduction with application to the quantum lattice Boltzmann method

This work proposes a multi-circuit quantum lattice Boltzmann method (QLBM) algorithm that leverages parallel quantum computing to reduce quantum resource requirements. Computational fluid dynamics (CFD) simulations often entail a large computational burden on classical computers. At present, these simulations can require up to trillions of grid points and millions of time steps. To reduce costs, novel architectures like quantum computers may be intrinsically more efficient for these computations. Current quantum algorithms for solving CFD problems are based on single quantum circuits and, in many cases, use lattice-based methods. Current quantum devices are adorned with sufficient noise to make large and deep circuits untenable. We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solve of the incompressible Navier--Stokes equations. The method, called QLBM-frugal, aims to create more practical quantum circuits and strategies for differential equation-based problems. The presented method is validated and demonstrated for 2D lid-driven cavity flow. The two-circuit algorithm shows a marked reduction in CNOT gates, which consume the majority of the runtime on quantum devices. Compared to the baseline QLBM technique, a two-circuit strategy shows increasingly large improvements in gate counts as the qubit size, or problem size, increases. For 64 lattice sites, the CNOT count was reduced by 35%, and the gate depth decreased by 16%. This strategy also enables concurrent circuit execution, further halving the seen gate depth.

quant-ph↗