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Malay Singh

Publications and source records attributed to Malay Singh.

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Scalable Single-Step Generation of W States in 2D Superconducting Qubit Lattices

The reliable generation of multi-qubit entanglement is a prerequisite for large-scale quantum information technologies. In particular, W states are a valuable resource owing to their resilience under local loss or measurement. Nevertheless, preparing these states with sequential two-qubit gates often requires substantial time overhead. By contrast, engineered simultaneous interactions enable fast entanglement generation, even in qubit systems with limited nearest-neighbour connectivity. Here, we demonstrate a set of fast and robust operations for coherently distributing a single excitation across a lattice of arbitrary size, thereby directly generating W states from initial product states. In 2D lattices, the excitation propagates along both directions simultaneously, such that the total entanglement time scales only with the largest dimension. We exploit this property to prepare a six-qubit W state in a 3$\times$2 superconducting lattice within 99 ns, achieving a tomographic fidelity of 83.9$\pm$1.0%. We then extend the protocol to create entanglement across chains of up to seven qubits, with the largest W state generated in 264 ns with a fidelity of 79.6$\pm$1.3%.

quant-ph

Enhancing Variational Quantum Eigensolvers for SU(2) Lattice Gauge Theory via Systematic State Preparation

Computing the vacuum and energy spectrum in non-Abelian, interacting lattice gauge theories remains an open challenge, in part because approximating the continuum limit requires large lattices and huge Hilbert spaces. To address this difficulty with near-term quantum computing devices, we adapt the variational quantum eigensolver to non-Abelian gauge theories. We outline scaling advantages when using a spin-network basis to simulate the gauge-invariant Hilbert space and develop a systematic state preparation ansatz that creates gauge-invariant excitations while alleviating the barren plateau problem. We illustrate our method in the context of SU(2) Yang-Mills theory by testing it on a minimal toy model consisting of a single vertex in 3+1 dimensions. In this toy model, simulations allow us to investigate the impact of noise expected in current quantum devices.

quant-ph

Sub-Harmonic Control of a Fluxonium Qubit via a Purcell-Protected Flux Line

Protecting qubits from environmental noise while maintaining strong coupling for fast high-fidelity control is a central challenge for quantum information processing. Here, we demonstrate a control scheme for superconducting fluxonium qubits that eliminates qubit decay through the control channel by suppressing the environmental density of states at the transition frequency. Adding a low-pass filter on the flux line allows for flux-biasing and, at the same time, coherently controlling the fluxonium qubit by parametrically driving it at integer fractions of its transition frequency. We compare the filtered to the unfiltered configuration and find a five times longer $T_1$, and ten times improved $T_2$-echo time in the filtered case. We demonstrate coherent control with up to 11-photon sub-harmonic drives, highlighting the strong non-linearity of the fluxonium potential. Measured Rabi frequencies and drive-induced frequency shifts show excellent agreement with numerical and analytical models. Furthermore, we show the equivalence of a 3-photon sub-harmonic drive to an on-resonance drive by benchmarking sub-harmonic gate fidelities above 99.94$\,$%. These results open up a scalable path for full qubit control through a single Purcell-protected channel, providing strong suppression of control-induced decoherence and enabling wiring-efficient superconducting quantum processors.

quant-ph

Parity-dependent state transfer for direct entanglement generation

As quantum information technologies advance, challenges in scaling and connectivity persist, particularly the need for long-range qubit connectivity and efficient entanglement generation. Perfect State Transfer enables time-optimal state transfer between distant qubits using only nearest-neighbor couplings, enhancing device connectivity. Moreover, the transfer protocol results in effective parity-dependent non-local interactions, extending its utility to entanglement generation. Here, we experimentally demonstrate Perfect State Transfer and multi-qubit entanglement generation on a chain of six superconducting transmon qubits with tunable couplers, controlled via parametric drives. By simultaneously activating and engineering all couplings, we implement the transfer for up to six qubits, verifying single-excitation dynamics for different initial states. Extending the protocol to multiple excitations, we confirm its parity-dependent nature, where excitation number controls the phase of the transferred state. Finally, leveraging this property, we prepare a Greenberger-Horne-Zeilinger state using a single transfer operation, showcasing the potential of Perfect State Transfer for efficient entanglement generation.

quant-ph

Sensitivity-Adapted Closed-Loop Optimization for High-Fidelity Controlled-Z Gates in Superconducting Qubits

Achieving fast and high-fidelity qubit operations is crucial for unlocking the potential of quantum computers. In particular, reaching low gate errors in two-qubit gates has been a long-standing challenge in the field of superconducting qubits due to their typically long duration relative to coherence times. To realize fast gates, we utilize the hybridization between fixed-frequency superconducting qubits with a strongly interacting coupler mode that is tunable in frequency. To reduce population leakage during required adiabatic passages through avoided level crossings, we employ a sensitivity-adaptive closed-loop optimization method to design complex pulse shapes. We compare the performance of Gaussian-square, Fourier-series, and piecewise-constant-slope (PiCoS) pulse parametrizations and are able to reach 99.9 % controlled-Z gate fidelity using a 64 ns long Fourier-series pulse defined by only seven parameters. These high-fidelity values are achieved by analyzing the optimized pulse shapes to identify and systematically mitigate signal-line distortions in the experiment. To improve the convergence speed of the optimization we implement an adaptive cost function, which continuously maximizes the sensitivity. The demonstrated method can be used for tune-up and recalibration of superconducting quantum processors.

quant-ph

Efficient decoupling of a non-linear qubit mode from its environment

To control and measure the state of a quantum system it must necessarily be coupled to external degrees of freedom. This inevitably leads to spontaneous emission via the Purcell effect, photon-induced dephasing from measurement back-action, and errors caused by unwanted interactions with nearby quantum systems. To tackle this fundamental challenge, we make use of the design flexibility of superconducting quantum circuits to form a multi-mode element -- an artificial molecule -- with symmetry-protected modes. The proposed circuit consists of three superconducting islands coupled to a central island via Josephson junctions. It exhibits two essential non-linear modes, one of which is flux-insensitive and used as the protected qubit mode. The second mode is flux-tunable and serves via a cross-Kerr type coupling as a mediator to control the dispersive coupling of the qubit mode to the readout resonator. We demonstrate the Purcell protection of the qubit mode by measuring relaxation times that are independent of the mediated dispersive coupling. We show that the coherence of the qubit is not limited by photon-induced dephasing when detuning the mediator mode from the readout resonator and thereby reducing the dispersive coupling. The resulting highly protected qubit with tunable interactions may serve as a basic building block of a scalable quantum processor architecture, in which qubit decoherence is strongly suppressed.

quant-ph

Cribriform pattern detection in prostate histopathological images using deep learning models

Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate histopathological images. $163708$ Hematoxylin and Eosin (H\&E) stained images were extracted from histopathologic tissue slides of $19$ patients with prostate cancer and annotated for cribriform patterns. Our automated image classification system analyses the H\&E images to classify them as either `Cribriform' or `Non-cribriform'. Our system uses various deep learning approaches and hand-crafted image pixel intensity-based features. We present our results for cribriform pattern detection across various parameters and configuration allowed by our system. The combination of fine-tuned deep learning models outperformed the state-of-art nuclei feature based methods. Our image classification system achieved the testing accuracy of $85.93~\pm~7.54$ (cross-validated) and $88.04~\pm~5.63$ ( additional unseen test set) across three folds. In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.

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