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Kshitij Kapoor

Publications and source records attributed to Kshitij Kapoor.

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Plaquette: A hardware-aware design platform for fault-tolerant quantum computers

Hardware teams building fault-tolerant quantum computers (FTQCs) must decide which imperfections to suppress, and that decision requires the logical performance of the architecture under the device's actual noise. Hardware noise often departs from the stochastic Pauli models used by scalable stabilizer simulators: superconducting transmons leak out of the computational subspace, neutral atoms scatter through intermediate states, trapped ions heat as their motional modes absorb phonons, and miscalibrated controls over-rotate coherently. We present Plaquette, a theoretical framework and software suite that computes the logical performance of fault-tolerant architectures directly from the physics of such imperfections. In Plaquette, a hardware error model is specified once, as Kraus operators, Hamiltonian-Lindblad dynamics, or an experimentally reconstructed quantum channel, and is compiled automatically into the exact or approximate representation required by each of four sampler classes: stabilizer sampling for Pauli noise, the new XPauli sampler for leakage and environment sectors, near-Clifford samplers for coherent errors, and full-state simulation for exact reference calculations. We validate the XPauli and near-Clifford samplers against full-state simulation, which they can match within statistical uncertainty while Pauli twirling can fall short depending on the error model. We demonstrate the framework on three error models: leakage in superconducting qubits, intermediate-state scattering in neutral atoms, and heating in trapped ions. The size of the discrepancy between Plaquette and Clifford-only simulations varies with platform and noise process, so reliable thresholds, error budgets, and overhead estimates require the most accurate simulation available. Plaquette provides a direct path from the open-system physics of a device to the logical performance of the FTQC built on it.

quant-ph

Generating Synthetic Population

In this paper, we provide a method to generate synthetic population at various administrative levels for a country like India. This synthetic population is created using machine learning and statistical methods applied to survey data such as Census of India 2011, IHDS-II, NSS-68th round, GPW etc. The synthetic population defines individuals in the population with characteristics such as age, gender, height, weight, home and work location, household structure, preexisting health conditions, socio-economical status, and employment. We used the proposed method to generate the synthetic population for various districts of India. We also compare this synthetic population with source data using various metrics. The experiment results show that the synthetic data can realistically simulate the population for various districts of India.

cs.CY

Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population

Population censuses are vital to public policy decision-making. They provide insight into human resources, demography, culture, and economic structure at local, regional, and national levels. However, such surveys are very expensive (especially for low and middle-income countries with high populations, such as India), time-consuming, and may also raise privacy concerns, depending upon the kinds of data collected. In light of these issues, we introduce SynthPop++, a novel hybrid framework, which can combine data from multiple real-world surveys (with different, partially overlapping sets of attributes) to produce a real-scale synthetic population of humans. Critically, our population maintains family structures comprising individuals with demographic, socioeconomic, health, and geolocation attributes: this means that our ``fake'' people live in realistic locations, have realistic families, etc. Such data can be used for a variety of purposes: we explore one such use case, Agent-based modelling of infectious disease in India. To gauge the quality of our synthetic population, we use both machine learning and statistical metrics. Our experimental results show that synthetic population can realistically simulate the population for various administrative units of India, producing real-scale, detailed data at the desired level of zoom -- from cities, to districts, to states, eventually combining to form a country-scale synthetic population.

cs.LG