arXiv subjects

Pooya Ronagh

Publications and source records attributed to Pooya Ronagh.

At least 19 recordsLinked to original sources

arXiv2026

Provable Quantum--Classical Separation for Continuous Gibbs Sampling

We prove the first quantum--classical separation for a sampling problem over a continuous domain. For a class of Gibbs states $p\propto e^{-\beta E}$ on the torus $\mathbb{T}^d$ with smooth ($s$-Gevrey) potential and barrier amplitude $\alpha=e^{\beta\Delta}$, where $\Delta = \max E-\min E$, every classical algorithm---querying the value, gradient, or any higher-order derivatives of the log-density---requires $\Omega(\alpha)$ queries to sample at constant accuracy in total variation distance, while a quantum algorithm based on quantum singular value thresholding and temperature annealing samples with $\tilde{O}\left(\sqrt{\alpha}\right)$ queries to an oracle for the gradient. The advantage is quadratic in the barrier amplitude, which becomes exponential in the dimension, $e^{\Omega(d)}$, at low temperature. The classical bound is information-theoretic, holding for every classical algorithm with query access to the Gibbs potential and its derivatives at any order.

quant-ph↗

arXiv2026

Breaking the Curse of Dimensionality in Quantum PDE Solvers via Gevrey Regularity

We connect different degrees of smoothness of real-valued periodic functions to the number of qubits required for their high-precision Fourier-basis amplitude encodings as quantum states. Our resulting central observation is that the Gevrey hierarchy, which stratifies the space between smooth and analytic functions, provides a natural class for high-precision quantum algorithms. We then specialize to solving general linear partial differential equations (PDEs) with periodic boundary conditions, showing how our Fourier methods do so efficiently at varying target precisions on a quantum computer. This also demonstrates how our framework enables passage from query-complexity results to explicit elementary gate counts. As an application, we introduce a hierarchy of many-body quantum simulation pipelines that harness these high-precision algorithms to probe the linear response of atomistic systems in first quantization. Each level of the hierarchy unlocks a further polynomial-degree quantum speedup, yielding a gradual improvement in simulation efficiency as quantum computers scale.

quant-ph↗

arXiv2026

Diffusion-warm sampling of the XY model enables fast thermalization at scale

We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach to the XY model, a fundamental continuous-spin model in condensed matter physics, we show that our technique addresses the shortfalls of the Markov chain Monte Carlo (MCMC) in generalization to varying system sizes. More specifically, we show that training a temperature-conditioned diffusion model on smaller-size XY model lattices enables the generation of accurate samples in larger lattice sizes. By tracking physically important observables of the model, such as spin correlations, our experiments demonstrate that diffusion sampling followed by a few MCMC steps reduces the thermalization time by an order of magnitude relative to the standard MCMC with random initialization. Our study provides valuable insight as to how generative models can be used to study continuous-state condensed matter systems at scale.

quant-ph↗

arXiv2026

Partially Fault-Tolerant Quantum Computation for Megaquop Applications

Partially fault-tolerant quantum computing (FTQC) has recently emerged as a promising approach for the execution of megaquop-scale circuits with millions of logical operations. In this work, we demonstrate the strengths and the limitations of this approach by conducting quantum resource estimation (QRE) of the space--time-efficient analog rotation (STAR) architecture using realistic hardware specifications for superconducting processors, and compare it against the QRE of the full FTQC architecture. We show how the performance of the STAR architecture's protocols is affected by hardware improvements. We also reduce the space requirements for partial FTQC by developing a procedure leveraging code growth to decrease the size of a factory producing analog rotation states. Our results reveal a non-trivial dependence of the optimal pre-growth code distance on the rotation angle with respect to post-growth infidelity. Further, we analyze space--time trade-offs between the factory size and the error-mitigation overhead, and observe that in an application-agnostic setting, there is a Goldilocks zone for circuits in the regime of roughly $10^5$--$10^6$ small-angle rotation gates. We show that quantum simulation of 2D Fermi--Hubbard model systems is a particularly well-suited application for the STAR architecture, requiring only hundreds of thousands of physical qubits and runtimes on the order of minutes for modest system sizes. Due to its favourable algorithmic scaling to larger system sizes, utility-scale simulation of the 2D Fermi--Hubbard model could potentially be attained using partial FTQC.

quant-ph↗

arXiv2025

Optimization of High-Fidelity Single-Qubit Gates for Fluxoniums Using Single-Flux Quantum Control

We present a gradient-based method to construct memory-efficient, high-fidelity, single-qubit gates for fluxonium qubits. These gates are constructed using a sequence of single-flux quantum (SFQ) pulses that are sent to the qubit through either capacitive or inductive coupling. The schedule of SFQ pulses is constructed with an on-ramp and an off-ramp applied prior to and after a pulse train, where the pulses are spaced at intervals equal to the qubit period. We reduce the optimization problem to the scheduling of a fixed number of SFQ pulses in the on-ramp and solve it by relaxing the discretization constraint of the SFQ clock as an intermediate step, allowing the use of the Broyden-Fletcher-Goldfarb-Shanno optimizer. Using this approach, gate fidelities of 99.99 % can be achieved for inductive coupling and 99.9 % for capacitive coupling, with leakage being the main source of coherent errors for both approaches.

quant-ph↗

arXiv2025

Impacts of Decoder Latency on a Utility-Scale Quantum Computer Architecture

The speed of a fault-tolerant quantum computer depends in large part on the reaction time of its classical electronics, that is, the total time required by decoders and controllers to determine the outcome of a logical measurement and execute subsequent conditional logical operations. Despite its importance, the reaction time and its impact on the design of a logical microarchitecture of a quantum computer are not well understood. In this work, we perform a detailed system-level analysis of the classical and quantum resource requirements of a surface code based architecture. To this end, we construct a model of the reaction time in which the decoder latency is based on parallel space- and time-window decoding methods. In addition, we draw communication latencies from our envisioned quantum execution environment, which comprises a high-speed network of quantum processing units, controllers, decoders, and high-performance computing nodes. We use this model to estimate the increase in the logical error rate of magic state injections as a function of the reaction time. We then show how the microarchitecture can be optimized with respect to the reaction time, and present full-system quantum resource estimates based on realistic hardware noise parameters for executing utility-scale quantum circuits of the Fermi-Hubbard model (2562 logical qubits and $4$$\times$$10^6$ $T$ gates) and NMR spectral prediction (241 logical qubits and $5.11$$\times$$10^{11}$ $T$ gates). We numerically determine the impact of improving reaction time on the code distance required for the core processor and on the size of the resource state factory. Moreover, we analyze how decoder speeds constrain the size of practically executable circuits, and show that state-of-the-art decoders will require a performance improvement of at least an order of magnitude to execute utility-scale quantum algorithms within days.

quant-ph↗

arXiv2025

Coherent Ising Machines: The Good, The Bad, The Ugly

Analog computing using bosonic computational states is a leading approach to surpassing the computational speed and energy limitations of von Neumann architectures. But the challenges of manufacturing large-scale photonic integrated circuits (PIC) has led to hybrid solutions that integrate optical analog and electronic digital components. A notable example is the coherent Ising machine (CIM), that was primarily invented for solving quadratic binary optimization problems. In this paper, we focus on a mean-field interpretation of the dynamics of optical pulses in the CIM as solutions to Langevin dynamics, a stochastic differential equation (SDE) that plays a key role in non-convex optimization and generative AI. This interpretation establishes a computational framework for understanding the system's operation, the computational role of each component, and its performance, strengths, and limitations. We then infer that the CIM is inherently a continuous state machine, capable of integrating a broad range of SDEs, in particular for solving a continuous global (or mildly constrained) optimization problems. Nevertheless, we observe that the iterative digital-to-analog and analog-to-digital conversions within the protocol create a bottleneck for the low power and high speed of optics to shine. This observation underscores the need for major advances in PIC technologies as we envision that fully analog opto-electronic realizations of such experiments can open doors for broader applications, and orders of magnitude improvements in speed and energy consumption.

physics.optics↗

arXiv2025

Towards a Cryogenic CMOS-Memristor Neural Decoder for Quantum Error Correction

This paper presents a novel approach utilizing a scalable neural decoder application-specific integrated circuit (ASIC) based on metal oxide memristors in a 180nm CMOS technology. The ASIC architecture employs in-memory computing with memristor crossbars for efficient vector-matrix multiplications (VMM). The ASIC decoder architecture includes an input layer implemented with a VMM and an analog sigmoid activation function, a recurrent layer with analog memory, and an output layer with a VMM and a threshold activation function. Cryogenic characterization of the ASIC is conducted, demonstrating its performance at both room temperature and cryogenic temperatures down to 1.2K. Results indicate stable activation function shapes and pulse responses at cryogenic temperatures. Moreover, power consumption measurements reveal consistent behavior at room and cryogenic temperatures. Overall, this study lays the foundation for developing efficient and scalable neural decoders for quantum error correction in cryogenic environments.

quant-ph↗

arXiv2024

Optimization of Two-Qubit Gates in Tunable-Coupler Architectures Using Single Flux Quantum Control

We present a gradient-based method to construct high-fidelity, two-qubit quantum gates in a system consisting of two transmon qubits coupled via a tunable coupler. In particular, we focus on single flux quantum (SFQ) pulses as a promising and scalable alternative to traditional control schemes that use microwave electronics. We develop a continuous embedding scheme to optimize these discrete pulses, taking advantage of auto-differentiation of our model. This approach allows us to achieve fSim-type gates with average gate fidelities on the order of 99.99% and CZ and CNOT gates with fidelities above 99.9%. Furthermore, we provide an alternative semi-analytical construction of these gates via an exact decomposition using a pair of fSim gates which leads to the reduction in memory required to store the associated pulse sequences.

quant-ph↗

arXiv2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.

quant-ph↗

arXiv2024

Optimizing Multi-level Magic State Factories for Fault-Tolerant Quantum Architectures

We propose a novel technique for optimizing a modular fault-tolerant quantum computing architecture, taking into account any desired space-time trade-offs between the number of physical qubits and the fault-tolerant execution time of a quantum algorithm. We consider a concept architecture comprising a dedicated zone as a multi-level magic state factory and a core processor for efficient logical operations, forming a supply chain network for production and consumption of magic states. Using a heuristic algorithm, we solve the multi-objective optimization problem of minimizing space and time subject to a user-defined error budget for the success of the computation, taking the performance of various fault-tolerant protocols into account. As an application, we show that physical quantum resource estimation reduces to a simple model involving a small number of key parameters, namely, the circuit volume, the error prefactors ($\mu$) and error suppression rates ($\Lambda$) of the fault-tolerant protocols, the reaction time ($\gamma$), and an allowed slowdown factor ($\beta$).

quant-ph↗

arXiv2024

Optimizing Pulse Shapes of an Echoed Conditional Displacement Gate in a Superconducting Bosonic System

Echoed conditional displacement (ECD) gates for bosonic systems have become the key element for real-time quantum error correction beyond the break-even point. These gates are characterized by a single complex parameter $\beta$, and can be constructed using Gaussian pulses and free evolutions with the help of an ancillary transmon qubit. We show that there is a lower bound for the gate time in the standard construction of an ECD gate. We present a method for optimizing the pulse shape of an ECD gate using a pulse-shaping technique subject to a set of experimental constraints. Our optimized pulse shapes remain symmetric, and can be applied to a range of target values of $\beta$ by tuning only the amplitude. We demonstrate that the total gate time of an ECD gate for a small value of $\beta$ can be reduced either by relaxing the no-overlap constraint on the primitives used in the standard construction or via our optimal-control method. We show a slight advantage of the optimal-control method by demonstrating a reduction in the preparation time of a $|+Z_\mathrm{GKP}>$ logical state by $\thicksim$$10\%$.

quant-ph↗

arXiv2024

Multi-qubit Lattice Surgery Scheduling

Fault-tolerant quantum computation using two-dimensional topological quantum error correcting codes can benefit from multi-qubit long-range operations. By using simple commutation rules, a quantum circuit can be transpiled into a sequence of solely non-Clifford multi-qubit gates. Prior work on fault-tolerant compilation avoids optimal scheduling of such gates since they reduce the parallelizability of the circuit. We observe that the reduced parallelization potential is outweighed by the significant reduction in the number of gates. We therefore devise a method for scheduling multi-qubit lattice surgery using an earliest-available-first policy, solving the associated forest packing problem using a representation of the multi-qubit gates as Steiner trees. Our extensive testing on random and application-inspired circuits demonstrates the method's scalability and performance. We show that the transpilation significantly reduces the circuit length on the set of circuits tested, and that the resulting circuit of multi-qubit gates has a further reduction in the expected circuit execution time compared to serial execution.

quant-ph↗

arXiv2024

Bootstrapping Classical Shadows for Neural Quantum State Tomography

We investigate the advantages of using autoregressive neural quantum states as ansatze for classical shadow tomography to improve its predictive power. We introduce a novel estimator for optimizing the cross-entropy loss function using classical shadows, and a new importance sampling strategy for estimating the loss gradient during training using stabilizer samples collected from classical shadows. We show that this loss function can be used to achieve stable reconstruction of GHZ states using a transformer-based neural network trained on classical shadow measurements. This loss function also enables the training of neural quantum states representing purifications of mixed states. Our results show that the intrinsic capability of autoregressive models in representing physically well-defined density matrices allows us to overcome the weakness of Pauli-based classical shadow tomography in predicting both high-weight observables and nonlinear observables such as the purity of pure and mixed states.

quant-ph↗

arXiv2023

Compact Pulse Schedules for High-Fidelity Single-Flux Quantum Qubit Control

In the traditional approach to controlling superconducting qubits using microwave pulses, the field of pulse shaping has emerged in order to assist in the removal of leakage and increase gate fidelity. However, the challenge of scaling microwave control electronics has created an opportunity to explore alternative methods such as single-flux quantum (SFQ) pulses. For qubits controlled by SFQ pulses, high fidelity gates can be achieved by optimizing the binary control sequence. We extend the notion of the derivative removal by adiabatic gate (DRAG) framework a transmon qubit controlled by SFQ drivers. The proposed implementation of SFQ pulse sequences can be stored in 22 bits or fewer, with gate fidelities exceeding 99.99%. This modest memory requirement could help reduce the footprint of the SFQ coprocessors and power dissipation while preserving their inherent advantages of scalability and cost-effectiveness.

quant-ph↗

arXiv2023

A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction

Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Its ability to adapt to hardware noise and long-term drifts make neural decoders a promising candidate for inclusion in a fault-tolerant quantum architecture. However, given their limited scalability, it is prudent that small-scale (local) neural decoders are treated as first stages of multi-stage decoding schemes for fault-tolerant quantum computers with millions of qubits. In this case, minimizing the decoding time to match the stabilization measurements frequency and a tight co-integration with the QPUs is highly desired. Cryogenic realizations of neural decoders can not only improve the performance of higher stage decoders, but they can minimize communication delays, and alleviate wiring bottlenecks. In this work, we design and analyze a neural decoder based on an in-memory computation (IMC) architecture, where crossbar arrays of resistive memory devices are employed to both store the synaptic weights of the neural decoder and perform analog matrix-vector multiplications. In simulations supported by experimental measurements, we investigate the impact of TiOx-based memristive devices' non-idealities on decoding fidelity. We develop hardware-aware re-training methods to mitigate the fidelity loss, restoring the ideal decoder's pseudo-threshold for the distance-3 surface code. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant QEC.

quant-ph↗

arXiv2023

Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI

Motivated by applications of quantum computers in Gibbs sampling from continuous real-valued functions, we ask whether such algorithms can provide practical advantages for machine learning models trained on classical data and seek measures for quantifying such impacts. In this study, we focus on deep energy-based models (EBM), as they require continuous-domain Gibbs sampling both during training and inference. In lieu of fault-tolerant quantum computers that can execute quantum Gibbs sampling algorithms, we use the Monte Carlo simulation of diffusion processes as a classical alternative. More specifically, we investigate whether long-run persistent chain Monte Carlo simulation of Langevin dynamics improves the quality of the representations achieved by EBMs. We consider a scheme in which the Monte Carlo simulation of a diffusion, whose drift is given by the gradient of the energy function, is used to improve the adversarial robustness and calibration score of an independent classifier network. Our results show that increasing the computational budget of Gibbs sampling in persistent contrastive divergence improves both the calibration and adversarial robustness of the model, suggesting a prospective avenue of quantum advantage for generative AI using future large-scale quantum computers.

cs.LG↗

arXiv2023

Neural-Shadow Quantum State Tomography

Quantum state tomography (QST) is the art of reconstructing an unknown quantum state through measurements. It is a key primitive for developing quantum technologies. Neural network quantum state tomography (NNQST), which aims to reconstruct the quantum state via a neural network ansatz, is often implemented via a basis-dependent cross-entropy loss function. State-of-the-art implementations of NNQST are often restricted to characterizing a particular subclass of states, to avoid an exponential growth in the number of required measurement settings. To provide a more broadly applicable method for efficient state reconstruction, we present "neural-shadow quantum state tomography" (NSQST)-an alternative neural network-based QST protocol that uses infidelity as the loss function. The infidelity is estimated using the classical shadows of the target state. Infidelity is a natural choice for training loss, benefiting from the proven measurement sample efficiency of the classical shadow formalism. Furthermore, NSQST is robust against various types of noise without any error mitigation. We numerically demonstrate the advantage of NSQST over NNQST at learning the relative phases of three target quantum states of practical interest, as well as the advantage over direct shadow estimation. NSQST greatly extends the practical reach of NNQST and provides a novel route to effective quantum state tomography.

quant-ph↗