arXiv subjects

Benjamin Rempfer

Publications and source records attributed to Benjamin Rempfer.

7 recordsLinked to original sources

arXiv2026

Harmonic sequence state-preparation

We demonstrate an efficient circuit to prepare a quantum state with amplitudes proportional to a harmonic sequence. We do this by first preparing a large quantum state with linearly related amplitudes and then applying a quantum Fourier transform; this has a direct analogy to the fact that the Fourier coefficients of a sawtooth wave follow a harmonic sequence. We then consider an extension of this problem by block-encoding a matrix with a harmonic sequence along its diagonal. The cost of both circuits is dominated by the costs associated with the quantum Fourier transform.

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arXiv2025

Efficient block-encodings require structure

Block-encodings are ubiquitous in quantum computing as a way to represent data within a unitary operator. While several unstructured methods are applicable to arbitrary data, these techniques are burdened by hidden costs and poor accuracy. In this paper, we demonstrate that, even for a small 6-qubit encoding, these structure-agnostic techniques require wildly intractable resources. We compare these resources with an encoding method which respects a mathematical representation of the block, leading to the conclusion that unstructured encoding mehtods should only be used in the most extenuating circumstances. This finding runs contrary to existing literature on quantum algorithms which often employ structure-agnostic methods.

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arXiv2025

A simpler Gaussian state-preparation

The ability to efficiently state-prepare Gaussian distributions is critical to the success of numerous quantum algorithms. The most popular algorithm for this subroutine (Kitaev-Webb) has favorable polynomial resource scaling, however it faces enormous resource overheads making it functionally impractical. In this paper, we present a new, more intuitive method which uses exactly $n-1$ rotations, $(n-1)(n-2)/2$ two-qubit controlled rotations, and $\lfloor(n-1)/2\rfloor$ ancilla to state-prepare an $n$-qubit Gaussian state. We then apply optimizations to the circuit to render it linear in T-depth. This method can be extended to state-preparations of complex functions with polynomial phase.

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arXiv2024

Manipulating quasi-bound states in a photonic crystal with periodic impurities to store quantum information

We analytically model a one-dimensional lattice with periodic impurities representing a photonic crystal from first principles. We then investigate bound states in the continuum by computing the transmission and reflection coefficients. It turns out that when there are more impurities in our designed system then there exists a wider range of wavenumber where particles become essentially trapped. A perturbative-based explanation is shown to verify this phenomenon quantitatively. Due to this window of wavenumbers quantum information could be encoded in our system by constructing differently shaped wave packets that are bound by the tuning of parameters in our system.

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arXiv2024

Comparison of Superconducting NISQ Architectures

Advances in quantum hardware have begun the noisy intermediate-scale quantum (NISQ) computing era. A pressing question is: what architectures are best suited to take advantage of this new regime of quantum machines? We study various superconducting architectures including Google's Sycamore, IBM's Heavy-Hex, Rigetti's Aspen, and Ankaa in addition to a proposed architecture we call bus next-nearest neighbor (busNNN). We evaluate these architectures using benchmarks based on the quantum approximate optimization algorithm (QAOA) which can solve certain quadratic unconstrained binary optimization (QUBO) problems. We also study compilation tools that target these architectures, which use either general heuristic or deterministic methods to map circuits onto a target topology defined by an architecture.

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arXiv2024

Detailed assessment of calculating drag force with quantum computers: Explicit time-evolution precludes exponential advantage for nonlinear differential equations

This study examines the potential for fault-tolerant quantum computers to provide utility in fluid dynamics simulations, with a focus on drag force calculations for ship hull design. We assess whether quantum algorithms can surpass classical computational limits by generating detailed quantum resource estimates (QREs) in terms of logical qubits and $T$-gate counts. Our analysis is based on a quantum algorithm leveraging Carleman linearization of the lattice Boltzmann method (LBM), which has been suggested to offer exponential speedup. We develop efficient block encodings for LBM matrices and a method for amplitude-encoding drag force. We apply the method to the simple case of fluid flow past a sphere across a range of Reynolds numbers ($\mathrm{Re}$). We estimate the required (logical qubits)$\times$($T$-gates), finding them to be prohibitively large, ranging from $10^{21}$ to $10^{39}$. While classical simulations scale as $O(\mathrm{Re}^3)$, our QREs exhibit a modest polynomial scaling of $O(\mathrm{Re}^{2.68})$, indicating no exponential quantum advantage. We attribute this limitation to an intrinsic power-law relationship between spatial grid resolution and time-stepping requirements that is a fundamental characteristic of explicit methods for evolving nonlinear differential equations. Thus, quantum computers are unlikely to provide utility in applications that require time-evolving fluids and other systems of nonlinear differential equations.

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arXiv2024

S-FABLE and LS-FABLE: Fast approximate block-encoding algorithms for unstructured sparse matrices

The Fast Approximate BLock-Encoding algorithm (FABLE) is a technique to block-encode arbitrary $N\times N$ dense matrices into quantum circuits using at most $O(N^2)$ one and two-qubit gates and $\mathcal{O}(N^2\log{N})$ classical operations. The method nontrivially transforms a matrix $A$ into a collection of angles to be implemented in a sequence of $y$-rotation gates within the block-encoding circuit. If an angle falls below a threshold value, its corresponding rotation gate may be eliminated without significantly impacting the accuracy of the encoding. Ideally many of these rotation gates may be eliminated at little cost to the accuracy of the block-encoding such that quantum resources are minimized. In this paper we describe two modifications of FABLE to efficiently encode sparse matrices; in the first method termed Sparse-FABLE (S-FABLE), for a generic unstructured sparse matrix $A$ we use FABLE to block encode the Hadamard-conjugated matrix $H^{\otimes n}AH^{\otimes n}$ (computed with $\mathcal{O}(N^2\log N)$ classical operations) and conjugate the resulting circuit with $n$ extra Hadamard gates on each side to reclaim a block-approximation to $A$. We demonstrate that the FABLE circuits corresponding to block-encoding $H^{\otimes n}AH^{\otimes n}$ significantly compress and that overall scaling is empirically favorable (i.e. using S-FABLE to block-encode a sparse matrix with $\mathcal{O}(N)$ nonzero entries requires approximately $\mathcal{O}(N)$ rotation gates and $\mathcal{O}(N\log N)$ CNOT gates). In the second method called `Lazy' Sparse-FABLE (LS-FABLE), we eliminate the quadratic classical overhead altogether by directly implementing scaled entries of the sparse matrix $A$ in the rotation gates of the S-FABLE oracle. This leads to a slightly less accurate block-encoding than S-FABLE, while still demonstrating favorable scaling to FABLE similar to that found in S-FABLE.

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