arXiv subjects

Nick Chancellor

Publications and source records attributed to Nick Chancellor.

7 recordsLinked to original sources

arXiv2026

Using domain walls to benchmark fast quantum anneals

We consider the distribution of domain walls arising in ground states of frustrated 1-dimensional Ising spin chains with anti-parallel boundary spins. For ideal chains, a sine-squared particle-in-a-box distribution of domain walls is expected in the quantum setting, and a uniform distribution expected classically. However, for chains with random imperfections, prior work [Chancellor et al., npj Quantum Inf. 8, 73, 2022] found that the distribution in a physical implementation corresponded to neither. Instead, the domain wall distribution forms a U-shaped profile arising from a classical model with random fields, where the combination of averaging over these fields and thermal averaging produces the characteristic U-shape. With D-Wave's newer fast anneal feature, we access anneal times shorter than the coherence time and observe the expected coherent quantum behavior in the form of a sine-squared distribution. For the shortest available anneal time, the dynamics have not yet reached the sine-squared shape and instead display a different U-shape in the middle of the chains. This motivates using the extent of the domain wall distribution tail in this regime as a platform independent metric that provides a signature of both coherence time and noise levels.

quant-ph↗

arXiv2026

Entangled Futures: Quantum Computing's Potential and Challenges in Computer Science

Quantum computing represents a fundamental shift in computation, promising to solve problems currently intractable for classical computers. However, misconceptions persist; quantum computers are not universally faster, nor do they replace classical systems. Current Noisy Intermediate-Scale Quantum (NISQ) devices face significant challenges, including decoherence, high error rates, and limited scalability. Additionally, leveraging quantum systems requires rethinking traditional computing principles, adopting different programming models, and addressing the complexities of integrating quantum and classical workflows, making their practical use in computer science both demanding and non-trivial. This article provides a system-level perspective on quantum computing for computer scientists, software engineers, and IT professionals, examining its current capabilities, key architectural and software challenges, and potential paths toward quantum advantage in real-world applications within hybrid quantum-classical environments.

cs.ET↗

arXiv2026

Quantum Approximate Optimisation Algorithm for Protein Sidechain Packing

Sidechain packing is a critical stage in protein folding, with direct implications for structure-based drug discovery. Google DeepMind's tool, AlphaFold2, predicts protein backbone reliably but sidechain positioning less accurately. Recovering the lowest-energy rotamer assignment over a fixed backbone is NP-hard. We present a hybrid quantum-classical pipeline that repacks sidechains on the AlphaFold backbone using the Quantum Approximate Optimisation Algorithm (QAOA), encoding the one- and two-body energies as a quadratic unconstrained binary optimisation (QUBO) problem. We introduce a constraint-preserving ansatz, pairing a W-state initialisation with a cyclic XY ring mixer, that enforces one-hot rotamer validity without penalty terms while keeping two-qubit gate scaling linear in the rotamer count. We also define an asymptotic shot-scaling metric, measured against the experimentally resolved conformation, that fixes optimiser quality independently of the baseline; its fitted growth stays below the classical exhaustive-search rate at moderate rotamer flexibility. Evaluated on bovine pancreatic trypsin inhibitor (5PTI) across high- and moderate AlphaFold-confidence regions, the pipeline lowers conformational energy against the AlphaFold baseline.

quant-ph↗

arXiv2026

Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks

As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no longer hold on utility-scale devices, leading to data loss that can be difficult to detect from high-level model outputs alone. We present a case study of \texttt{SamplerQNN}, the sampling-based quantum neural network class in the Qiskit Machine Learning library. Here, the postprocessing method applies a filter that assumes measurement bit-strings are in virtual qubit space. On our quantum hardware runs, where bit-strings span over 100 physical qubits, this filter led to the loss of 85 to 99.6\% of valid measurement shots, depending on the transpiler's qubit placement. The resulting probability vector is unnormalised, allowing distorted prediction and loss values to propagate through the model without an API-level warning. We demonstrate the impact across five experiments on two IBM backends: for inference, accuracy drops from 0.94 to 0.39 on the same raw measurements; for training, the loss signal is compressed by 22 to 27$\times$, substantially reducing the sensitivity of the optimiser to the objective landscape. The behaviour arises in all released versions of the library (0.8.4 to 0.9.0). We implemented a layout-based marginalisation fix, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.

quant-ph↗

arXiv2024

Grover Speedup from Many Forms of the Zeno Effect

It has previously been established that adiabatic quantum computation, operating based on a continuous Zeno effect due to dynamical phases between eigenstates, is able to realise an optimal Grover-like quantum speedup. In other words, is able to solve an unstructured search problem with the same $\sqrt{N}$ scaling as Grover's original algorithm. A natural question is whether other manifestations of the Zeno effect can also support an optimal speedup in a physically realistic model (through direct analogue application rather than indirectly by supporting a universal gateset). In this paper we show that they can support such a speedup, whether due to measurement, decoherence, or even decay of the excited state into a computationally useless state. Our results also suggest a wide variety of methods to realise speedup which do not rely on Zeno behaviour. We group these algorithms into three families to facilitate a structured understanding of how speedups can be obtained: one based on phase kicks, containing adiabatic computation and continuous-time quantum walks; one based on dephasing and measurement; and finally one based on destruction of the amplitude within the excited state, for which we are not aware of any previous results. These results suggest that there may be exciting opportunities for new paradigms of analog quantum computing based on these effects.

quant-ph↗

arXiv2019

Embedding quadratization gadgets on Chimera and Pegasus graphs

We group all known quadratizations of cubic and quartic terms in binary optimization problems into six and seven unique graphs respectively. We then perform a minor embedding of these graphs onto the well-known Chimera graph, and the brand new Pegasus graph. We conclude with recommendations for which gadgets are best to use when aiming to reduce the total number of qubits required to embed a problem.

quant-ph↗

arXiv2019

Pegasus: The second connectivity graph for large-scale quantum annealing hardware

Pegasus is a graph which offers substantially increased connectivity between the qubits of quantum annealing hardware compared to the graph Chimera. It is the first fundamental change in the connectivity graph of quantum annealers built by D-Wave since Chimera was introduced in 2009 and then used in 2011 for D-Wave's first commercial quantum annealer. In this article we describe an algorithm which defines the connectivity of Pegasus and we provide what we believe to be the best way to graphically visualize Pegasus in order to see which qubits couple to each other. As supplemental material, we provide a wide variety of different visualizations of Pegasus which expose different properties of the graph in different ways. We provide an open source code for generating the many depictions of Pegasus that we show.

quant-ph↗